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Context Engineering: The Practice Behind Dependable AI Agents

Or Hillel — Mon, 26 Oct 2026 13:09:00 +0000

Key Takeaways

Context engineering is the practice of determining what information an agent has in its context window at every step of a task: instructions, retrieved code, tool definitions, memory, and the results of earlier steps. The window is just the visible part. It depends on a knowledge layer, the code graph, tickets, docs, and team conventions, that the agent can query through tools while it runs.
Prompt engineering vs context engineering boils down to scope. A prompt is a single input written once. Context is assembled continuously while the agent runs, and most production failures trace back to that assembly rather than to the prompt.
Larger context windows do not fix the problem. Research across 18 models shows accuracy drops as inputs grow, even on simple tasks, so context window management is about choosing and compressing what goes in.
AI agent memory needs to be scoped and skeptical. Memory that persists across runs makes agents better over time, and a confident wrong memory makes them worse more quickly than anything else.
Overcut treats context as part of the orchestration layer. It automatically injects the related repositories and tickets from the organization's structure and systems, splits workflows into deterministic and agentic steps that each start with their own context and the previous step's output, and runs workflows that keep organizational knowledge current.

Most teams discover the limit of prompting the same way. An agent does well in a demo, gets wired into a real repository, and then begins failing in ways that are difficult to reproduce. It edits the wrong module. It ignores a convention that was spelled out in the instructions. It retries a failing test with the same fix it already tried. Someone opens the prompt, adds a sentence in capital letters, and the failure moves somewhere else.

When we trace failures like these back, the prompt is seldom the cause. The agent made a reasonable decision based on what was in front of it, and what was in front of it was wrong, stale, missing, or buried under forty thousand tokens of test output. The fix lives in the system that decides what the model sees. That system has a name now, context engineering, and for any team running agents on actual engineering work it matters more than the wording of any single instruction.

The Gap Between Prompting and Production-Level Agent Reliability

Prompting was designed for a single exchange. You write instructions, the model answers, you read the answer. An agent working a ticket runs dozens or hundreds of turns. Every tool call, file read, and test run gets appended to the window, so by the middle of a real task the instructions a team wrote are a small fraction of what the model is reading.

That is where reliability slips away. Chroma's context rot study tested 18 models, including GPT-4.1, Claude 4, and Gemini 2.5, and found performance changes significantly with input length, even on simple retrieval tasks. A single distractor, a passage that looks relevant but is not, measurably lowered accuracy. Anthropic's engineering team describes the same thing as an attention budget: every token added competes for the model's attention with every other token, so more context is not free even when it fits.

Drew Breunig gave the failure modes helpful names. Context poisoning is when a hallucination lands in the window and gets referenced again and again. Context distraction is when the accumulated history crowds out what the model knows from training. Context confusion comes from superfluous material shaping the answer, and context clash from new information contradicting old. None of these can be fixed by rewording a prompt, because the prompt is not where they happen. They happen in the running history, which nobody wrote and nobody reviewed.

This is the gap between an agent that works and an agent a team can depend on. Prompting controls the first few thousand tokens. Reliability depends on the rest.

The Practice Behind Dependable AI Agents

The term context engineering caught on in mid-2025. Shopify's Tobi Lütke described it as providing all the context needed for a task to be plausibly solvable by the model, and Andrej Karpathy called it the art and science of filling the context window with the right information for each step. Anthropic's working definition is the most practical: strategies for curating and maintaining the optimal set of tokens during inference.

The word context gets stretched in two directions, so here is how we apply it. In this post, context means everything the model reads when it makes a decision at a given step: the instructions, the files and tickets it pulled in, the tool definitions, its notes, and the history of the task so far. That is narrower than what people usually mean by organizational context, which is the codebase and its dependency graph, the ticket and incident history, the runbooks, and the conventions a team has built up over years. We treat the two as layers of one system. The knowledge layer is everything an agent could know. The window is what it knows right now. Context engineering is the work of moving the right slice from one to the other at the right moment.

That is also why we do not see context as a window problem alone. An agent with a perfectly curated window and no way to look anything up is stuck with whatever someone chose for it before the task started, and nobody can predict every file, caller, or past incident a real ticket will need. Agents that hold up on actual work have runtime access to knowledge through tools: code search, a graph of how modules and symbols depend on each other, the ticket and incident history, the team's written procedures. Those tools let the agent decide what to fetch based on what it learned a minute ago, and the quality of that access sets a ceiling on everything else. A code graph that answers "who calls this function" in one query keeps hundreds of files out of the window that the agent would otherwise have to open to find out.

The phrase "for each step" carries most of the load. Prompt engineering asks how to phrase an instruction. Context engineering asks what the model should be looking at right now, what should have been removed, what should be fetched only if needed, and what should survive into the next step or the next run. The first is a writing problem. The second is a systems problem, with state, budgets, retrieval, and failure handling, and it gets solved in code and infrastructure rather than in a text box.

We believe the distinction matters because it shifts who owns reliability. When a team treats agent failures as prompt problems, the fix is a person editing instructions until the symptom goes away. When a team treats them as context problems, the fix is a change to how context gets assembled, and that change applies to every future run.

The Components of the Context Engineering System

A context engineering system has a few core parts. Each one answers a different question about what the model sees.

Instructions and specifications. The stable layer: the role, the task, the constraints, and the definition of done. This is where prompt engineering still lives, and it should be kept short. Anything that only applies to some tasks belongs in a later layer, loaded when relevant.

Retrieval. The code, tickets, docs, and logs the task needs, and the tools the agent uses to reach them. The pattern that works best for agents is just-in-time retrieval: the agent holds lightweight references, file paths and ticket IDs, and uses search and graph queries to load content when it needs it rather than having everything stuffed in up front. The window only ever holds a slice of what the team knows, so how much of that knowledge retrieval can reach, and how precisely, matters as much as what the agent is handed at the start.

Tools. Tool definitions are context too, and they add up fast. Factory's deferred context engine keeps only compact tool metadata in the prompt and loads full schemas when a task calls for them. They measured an average 50.8% input token reduction in sessions with more than 100 hidden tools, and found that only 5.4% of sessions actually executed an MCP tool at all.

Memory. AI agent memory comes in two kinds. Working memory is the agent's notes within a task: the plan, what it tried, what it learned, written outside the window so it survives compaction. Persistent memory carries lessons across runs, like the fact that a particular test suite is flaky and should be rerun once before anyone investigates. Persistent memory is the most powerful component on this list and also the most dangerous one, because a wrong lesson gets applied with confidence on every run after it.

Window management. Context window management is what takes place as a task grows. Compaction summarizes the history and restarts with the summary. Tool outputs get trimmed to the parts that matter, so a failing test run contributes the failure rather than ten thousand lines of passing tests. Subtasks get handed to sub-agents that work in a clean window and return a condensed result.

Observability. A record of exactly what the model saw at each step. Without it, debugging an agent comes down to guessing, and every failure turns back into a prompt edit.

How Context Engineering Works Inside Software Development Agent Workflows

Consider a ticket moving from intake to a merged pull request, the kind of chained flow we wrote about in the agentic software development lifecycle.

At intake, the context is the ticket itself plus what surrounds it: linked issues, the reporter's earlier reports, the owning team, recent incidents on the same service. A triage agent that sees only the ticket text guesses. One that sees the surrounding record classifies.

At planning, the agent needs a map of the repository, not the repository. Module boundaries, ownership, the conventions the team has written down, and the skills that describe how this team does a migration or adds an endpoint. The plan it produces becomes a compact artifact that the next stage can load in place of the whole reasoning trail that produced it.

At implementation, window management takes over. The agent loads files as it needs them, runs tests, and reads failures. Windows fill up fastest here, and trimming tool output and keeping working notes outside the window decide whether the agent finishes or loses the thread halfway through.

Review is where we make an intentional choice. The review agent does not inherit the implementer's history. It gets the diff, the ticket, the plan, and the team's review standards, in a clean window. An implementer's context is full of its own reasoning, including the parts that were wrong, and a reviewer that reads that reasoning tends to agree with it. Separating the windows gives the review an independent view of the change, which is the whole point of having one.

At handoff, each stage passes forward a distilled result and writes back what it learned. In Overcut, that memory is scoped to the workflow step rather than shared globally, and it is biased toward skepticism: a memory that led an agent astray loses more weight per bad run than a helpful one gains per good run. A lesson has to keep proving itself to stay in the window. We believe that tradeoff is right, because a missing memory costs a little time and a wrong one costs a lot.

How Overcut Assembles and Optimizes Context

Everything above holds for any team building agents. Here is how we apply it in Overcut, and why.

Agents start with the organization already mapped. When a workflow runs on a ticket, Overcut automatically injects what the organization's structure and systems say is related to it: the repositories the work touches, the linked and related tickets, and the knowledge attached to them. The agent does not spend its first twenty turns working out which repository owns a service or whether the bug was reported before. We made this automatic because discovery is where agents silently go wrong. An agent that picks the wrong repository produces work that looks fine and is aimed at the wrong place. Injecting the relationships up front, while leaving the contents to be fetched on demand, gives the agent a correct starting map without filling its window.

Workflows are separate steps, and each one starts clean. A workflow in Overcut is a chain of steps, and not every step needs a model. Fetching a ticket, checking out a branch, running the test suite, or applying a label are deterministic steps that run as code, cost no tokens, and behave the same way every time. Steps that need judgment, like planning, implementing, and reviewing, are agentic. Every step has access to the output of the steps before it, and every step starts with its own context instead of inheriting the running history of the one before. The clean review window described earlier is one instance of this rule. We consider it one of the most effective optimizations in the system, because it caps window growth at the size of a single step, and it keeps one step's mistakes from becoming the next step's assumptions. Moving mechanical work into deterministic steps also removes a whole category of context: no tool definitions, no retries, and no verbose output for work that never needed a model in the first place.

The knowledge layer is maintained by workflows too. Runtime access is only as good as what it reaches, and organizational knowledge goes stale all the time. Repositories get split, ownership moves, conventions evolve, and a runbook written last year describes a system that no longer exists. In Overcut, keeping that knowledge current is itself a workflow. The same mix of deterministic and agentic steps that works tickets can run on a schedule or on events to refresh what the organization knows about its own code and systems, so the context agents draw on improves as the organization changes instead of drifting away from it. We will dig deeper into how that works in an upcoming post.

What Teams Need to Do Differently to Engineer Context

Moving from prompting to context engineering changes what a team treats as the product of its agent work.

Handle context sources as maintained assets. The docs, ownership maps, and written procedures an agent reads are inputs to a production system now, and stale ones produce confident wrong work. The teams that get consistent results are the ones that version their skills and conventions alongside their code and fix the source when an agent goes wrong, instead of patching the prompt.

Debug by checking the context first. When an agent fails, the first question should be what it saw at the step where it went wrong. Most failures that look like model mistakes turn out to be retrieval that missed something or history that should have been trimmed.

Design the handoffs. Every boundary between steps or agents is a decision about what carries forward. Pass artifacts, like plans, diffs, and summaries, rather than raw transcripts. The ticket is a good carrier for this, which is one reason we argued that the real interface for AI development is the ticketing system rather than a new IDE.

Budget the window per step. Decide how much of the window instructions, tools, retrieval, and history each get, and enforce it. A token budget that nobody sets gets set by whichever tool returns the longest output.

Some of this is still difficult. There is no reliable automatic way to tell which past lesson is still true, and compaction still occasionally drops the one detail that turns out to matter three steps later. We expect both to get better. What we do not expect to change is where reliability comes from. Models will keep getting better, and the teams that get the most out of them will be the ones that control what those models see.

FAQs

Is context engineering the same as retrieval augmented generation?

No. Retrieval augmented generation is one technique inside context engineering: fetching documents and inserting them into the prompt. Context engineering also covers what to leave out, how tool definitions are loaded, how memory persists across runs, how history is compacted, and what gets passed between agents. A team can run a strong RAG pipeline and still have unreliable agents if the running history grows unchecked or stale memories keep resurfacing.

How does context engineering scale as agent workflows grow?

It scales by keeping every window small rather than making one window bigger. As workflows add steps and agents, each step should receive a purpose-built context and pass forward a compact artifact. That keeps per-step token cost roughly flat as the workflow grows. The tougher scaling problem is governance: knowing which sources, memories, and tools each step can reach, and keeping those scopes current as the codebase and team change.

What happens when an agent exhausts its context window?

Without management, the request fails or the oldest content gets truncated, frequently including the original instructions. Well-built agents compact before that point, summarizing the history and continuing from the summary, with the plan and key decisions saved to working notes outside the window. The risk is that compaction loses a detail that matters later, so what gets preserved should be decided deliberately rather than left to a generic summarizer.

Can context engineering compensate for a weaker underlying model?

Partly. Good context narrows down the problem so a smaller model can handle tasks it would otherwise fail, and many teams route simpler steps to cheaper models once each step's context is well scoped. It cannot add reasoning ability the model lacks. For long, ambiguous tasks the stronger model still comes out ahead. Poor context hurts every model, though, so better context usually improves results more than a model upgrade alone.

How do you measure whether context engineering is working?

Track outcomes per workflow step rather than per session: task success rate, human rejection rate, retries, and rework after merge. Add context-level signals, including tokens per step, how often compaction triggers, and how often retrieved content is never referenced. When a step fails, check whether the needed information was in the window. A falling share of failures caused by missing or wrong context is the clearest signal that the system is improving.

Build or Buy SDLC Orchestration? The Maintenance Bill Starts at Commit One

Or Hillel — Sun, 25 Oct 2026 12:09:00 +0000

No one sits down and decides to build an internal AI platform. That is the part worth grasping before any build-versus-buy conversation, because it explains how careful, reasonable engineering teams end up owning one anyway.

It begins small. Someone wires up an agent that turns a Jira ticket into a draft PR. It works in the demo, the room is impressed, and the cost of that first version is truly low. A few prompts, an API call, a webhook. So you build a second agent for code review. Then a third for test generation. Then a new model is released that is better at one of those tasks, so you add a way to swap models. Then two teams want to use the tool, so you add a way to control who can edit which agent. Then someone in security asks what happens when an agent touches production, and you add a permission layer. Then an agent does something you did not anticipate, and you add logging so you can understand what happened.

At no point did anyone suggest building a platform. You built one agent at a time, and at some point along the way you crossed a line. The script became a system, and the system became something you now maintain. The catch with that line is that you only notice it after you have already crossed it. You were building a feature, and you woke up operating infrastructure.

This is why we think the framing of a six-month clock understates the problem. The legacy does not start at month six. It starts at commit one. The day you write the first piece of orchestration glue, you have committed to something you will have to keep alive, and the cost of keeping it alive only rises from there.

We Have Seen This Issue Before

There was a period when serious engineering organizations built their own continuous integration. You wrote the scripts that fetched the latest code, ran the build, kicked off the tests, and posted the outcome somewhere a human would see it. It was not exciting, but it was tractable, and for a while building it yourself was a sensible call because the off-the-shelf options were immature.

Almost no one does that today. Not because engineers got worse at writing build scripts, but because CI/CD turned into a solved category with mature products that handle the long tail of problems you do not want to think about. Caching, parallelism, secrets management, flaky-test handling, artifact storage, audit logs. If you decided to build your own CI/CD in 2026, your colleagues would ask you why. The value no longer lies in the orchestration of the pipeline. The value is in the product the pipeline ships.

Agentic SDLC orchestration is at precisely that inflection point. The first version is easy to build, which is exactly the trap, because easy-to-start and hard-to-run-right are not the same property. With the right platform, orchestrating AI agents across your development lifecycle is now as much a settled category as CI/CD. The question is no longer whether you can build it. You can. The question is whether building it is a smart use of the people who could be building your actual product.

Why the Urge to Build SDLC Orchestration Is So Strong

Engineering leaders are not naive when it comes to build-versus-buy. They make this call constantly, and they are typically good at it. So it is worth being exact about why orchestration is unusually tempting to build, because the normal instincts misfire here.

The first reason is that the cost is truly hidden at the outset. With most build-versus-buy decisions, the difficulty shows up early. You can see that writing your own database is hard. Orchestration is the opposite. The prototype that turns a ticket into a PR really does take just a weekend, and that early success sends the message that the whole thing is a weekend-shaped problem. It is not. The weekend buys you the demo. It does not buy you audit trails, policy enforcement, human-in-the-loop controls, rollback, or boundary enforcement at enterprise scale. Those are where internal builds silently stall, and you cannot see them from the demo.

The second reason is that orchestration looks like glue, not like a product. A database looks like a product, so people respect how hard it is. Orchestration looks like a few queues and some prompt templates, the kind of plumbing a strong engineer should be able to put together. That framing is wrong, but it persists, and it is how a full product ends up mistaken for a feature.

The Part of the Iceberg You Discover Last

If you ask which of the hard requirements teams find latest and regret most, the answer is governance. Not because the other hard parts are simple, but because governance is almost impossible to retrofit. You can bolt another integration onto a system after the fact. You cannot cleanly add the property that every action an agent ever took was authorized, logged, attributable, and reversible. That has to be in the architecture from day one, and teams rarely put it there at the start, because at the start they were building one agent that drafts a PR.

So the governance bill arrives late, typically when the system is already load-bearing and someone with the word "risk" or "compliance" in their title begins asking questions. Who approved this change? What can this agent access? What prevents it from touching production? Show me every action it took last quarter. Roll that one back. A homegrown system that was not built for those questions cannot answer them without a rewrite, and a rewrite of the thing your teams now rely on is the worst possible moment to do it.

This is also where the conventional wisdom gets it backwards. The usual advice says regulated industries, fintech and healthcare and legal, should build in-house for control and auditability. We believe that is exactly wrong, and our own experience confirms it. Governance and audit are the hardest things to get right, not the easiest, which means regulated industries are the teams that can least afford to uncover the edge cases themselves in production. The control you really want in a regulated environment is control that has already been proven in a regulated environment, not control you are debugging live with auditors watching.

What You Are Really Signing Up For

Strip away the demo and look at what owning an orchestration layer commits you to over time, in the terms a CTO actually budgets in.

You are signing up to keep up with the models, forever. Models, APIs, and capabilities change week to week. An internal build is current the day it ships and outdated a month later, and staying current is not a project with an end date. It is an ongoing tax on your team's attention. Either someone chases that churn constantly or your platform gradually falls behind the frontier while still costing you to run.

You are signing up for cross-system depth that keeps expanding. The real value of orchestration appears when it spans Jira, GitHub, GitLab, Azure DevOps, Bitbucket, and whatever else your organization uses, with shared context and long-term memory carried across executions. None of those integrations is a one-time cost. Each one is a maintenance surface. Every vendor's API changes, every one adds edge cases, and the surface area you need to keep working only grows as you add more.

You are signing up for the recurring half of total cost of ownership, which is the expensive half. The build is the cheap part. The costly part is the ongoing maintenance, the on-call rotation when an agent misbehaves at 2am, the prompt tuning, the model migrations, the security reviews, and the dedicated team you need to staff indefinitely to keep the whole thing running. That team does not get disbanded once the platform works. Keeping it working is the job.

And you are signing up for all of the risk, learned the hard way. An internal build means you discover the failure modes, the edge cases, and the security gaps yourself, in production, because there is nobody ahead of you who already found them. Gartner predicts that more than forty percent of agentic AI projects will be canceled by the end of 2027, pointing to escalating costs, unclear value, and inadequate risk controls. Read that as a map of where homegrown builds run aground. Cost, value, and governance are exactly the three places an internal orchestration effort is most exposed.

The Number That Really Matters

For an engineering leader, the cost of building your own orchestration layer is not truly measured in the engineering hours it takes to build. It is measured in what those engineers would otherwise be working on.

Every senior engineer assigned to internal AI tooling is a senior engineer not shipping the product your company actually sells. That is the real number, and it is bigger than any line item in the build estimate. An internal orchestration layer, however well built, is undifferentiated infrastructure. It does not make your product faster, better, or more defensible. Your customers will never know it exists. You carry the full cost of building and maintaining it and you get no competitive return, because orchestration plumbing is not where your moat is. Your moat is in the product, and the product is what those engineers were hired to build.

There is also a timing cost stacked on top of the opportunity cost. A platform reaches real production maturity, the kind that survives security review and enterprise scale, in roughly six to eighteen months. During each of those months, a competitor who bought instead of built is already getting value and already moving ahead. You are paying twice, once in the engineers you spent and once in the time you lost.

Buy the Commodity, Invest Your People in the Moat

The reason we built Overcut to be vendor-agnostic and to run alongside your existing stack, rather than replace it, comes straight from everything above. The failure mode of building your own orchestration is not just the cost. It is that internal builds harden around whatever your stack looks like today and gradually become the one thing you cannot migrate away from later. The platform that was meant to give you flexibility becomes the constraint. A layer that works with your current CI/CD and tooling, instead of forcing you to rebuild around it, is the opposite of that trap.

This is why the build-versus-buy question is really a question about where your engineering attention compounds. Orchestration is now a category with mature options, just as CI/CD became one. The hard parts, governance, audit, cross-system depth, model churn, production readiness, have already been solved by teams whose whole job is solving them. You can solve them again yourself, more slowly, at the expense of the roadmap, and end up with infrastructure that gives you no advantage. Or you can buy that layer and point your engineers at the product only they can build.

The clock everyone worries about is not a six-month clock. It starts the moment you write the first line of orchestration you did not need to. The question is not whether your homegrown platform will become legacy. It is legacy from day one, by definition, because it is something you now have to maintain. The only real question is whether you wanted to be in the platform-maintenance business at all. For nearly everyone, the honest answer is no.

The 6 top AI code review tools for automated pull request reviews

Or Hillel — Thu, 22 Oct 2026 10:51:13 +0000

Code review is where a lot of shipping velocity silently goes to wait. The work is done and the PR is open, but it sits in a queue behind everyone else's changes while its author shifts to the next thing. When a reviewer eventually gets to it, they're often reading under time pressure, so the issues that slip through tend not to be the clear ones. They're the subtle logic and cross-file problems that surface later in production, long after the diff felt fine.

That gap between how much code teams ship and how carefully they can really review it is where AI code review tools now live. The question is no longer whether to use one. It's which one, and how much of the review you're prepared to trust it with.

Key Takeaways

The best AI code review tools have gone beyond style linting into logic, cross-file context, and code security review, catching bugs that rely on callers and shared modules outside the diff.
Bug catch rate and noise are in tension. Greptile catches the most bugs but raises more false positives, while CodeRabbit stays quieter at a lower catch rate. There's no one winner, only the right fit for your team.
The tools worth adopting are those that reduce reviewer load without eroding trust. A bot that cries wolf gets silenced within a week.
AI pull request review is one node in a larger agentic workflow. Teams that see the most value have already automated triage, requirements, and test generation around it.
Overcut is the best fit for teams thinking beyond a single PR. It goes beyond a single review bot to orchestrate multiple specialized agents across the review under organization-level approvals and policies, so AI review connects to triage, tests, and fixes rather than living in its own dashboard.

Why Manual Code Review Breaks Down as Engineering Teams Scale

Manual review works great at five engineers and starts breaking at fifty. The math is brutal. Every new engineer adds their own output to the review queue and also turns into another reviewer others wait on. Review capacity grows linearly while the number of PR interdependencies grows far faster.

Three things break as the team expands:

Latency. PRs sit. A change that took two hours to write waits a day and a half for review, and by the time feedback arrives the author has already moved on and needs to page the whole change back into memory. This is a real tax on developer productivity, and it seldom shows up in any dashboard because the time is spread across dozens of small waits.
Depth. A reviewer staring down a 900-line diff at 4:45pm isn't tracing how a changed function behaves three files away. They're checking that the tests pass, that the naming looks reasonable, and that nothing is clearly on fire. The bugs that survive review are rarely style problems. They're the ones that live in the seams between files, in an assumption a caller makes that the diff silently breaks.
Consistency. Two reviewers apply two different bars. One blocks on missing tests, the other lets it through. New engineers learn the codebase's real standards by osmosis and guesswork rather than anything written down. Code review best practices exist on a wiki somewhere, but the wiki isn't in the PR when it counts.

None of this means human review is obsolete. It means human attention is the scarcest resource on the team, and spending it on things a machine can catch is a poor trade.

What AI Code Review Tools Actually Do vs. What They Claim to Do

Every tool in this category will tell you it finds bugs, improves security, and speeds up your team. The marketing sounds the same. The actual behavior does not, and the difference boils down to how much context the tool can see when it reviews a change.

The shallow end of the pool reads the diff and nothing else. These tools are basically linters with better phrasing. They'll flag an unused variable, suggest a more idiomatic loop, and tell you a function is getting long. Useful, sometimes, but a competent formatter and a static analyzer already handle most of it, and they do it deterministically.

The more interesting tools read the entire codebase. When they review a PR, they know what calls the function you changed, what shared module you just altered the contract of, and which internal API now returns something its consumers don't expect. This is the distinction that matters for AI pull request review, because the bugs that cost real money are the cross-file ones. Greptile reports catching bugs at an 82% rate exactly because it indexes the entire repository and reviews each change against that context rather than in isolation.

Code security review divides the same way. A diff-only tool catches the common patterns: a hardcoded secret, an unsanitized string heading toward a query, a missing input validation. A context-aware tool catches the ones that don't look like anything in the diff, like an endpoint that silently dropped an authorization check or an API path that now exposes more of an object than it should. Neither replaces a dedicated security scanner with formal taint tracking, and honest vendors admit it. AI review findings don't map cleanly to CWE or OWASP categories yet, which limits how far they reach for compliance. But they regularly catch the authorization logic that deterministic scanners miss entirely.

The claim to be wary of is autonomy. Most of these tools are assistive. They comment, they suggest, and a human still decides. That's the correct design for now. Just don't mistake a very good commenter for a system that owns the review.

The Best AI Code Review Tools for Automated Pull Request Reviews

1. Overcut

Most tools here treat code review as the product. Overcut treats it as one stage in an orchestrated pipeline that starts at the ticket and ends at a merged, validated PR. Instead of running a single bot over a diff, it coordinates several specialized agents across the review: one generating tests, one running static analysis, one focused on code security review, one applying fixes across files after a reviewer weighs in. Humans remain in control through explicit approvals, org-level policies about what agents may and may not do, and full visibility into every step recorded inside the ticket and PR where the rest of the team can see it. That framing is important for organizations that have realized code writing was never the bottleneck. Coordination and governance were. Overcut fits teams that want AI pull request review connected to triage, requirements, and release, with the kind of control an engineering org really needs, rather than a standalone bot with its own dashboard. If you're standing up an agentic SDLC and want multi-agent coordination behind consistent human oversight, it's the obvious anchor.

2. CodeRabbit

The most widely deployed standalone reviewer in the category, and the one developers grumble about least. Its strong suit is signal-to-noise. It generates really useful PR walkthroughs, including sequence diagrams that show how a change ripples through the system, and it keeps its comment volume low enough that people really read them. Catch rate is lower than the aggressive tools, but a review nobody mutes is worth more than one nobody trusts. Best for teams that want broad platform support across GitHub, GitLab, and Bitbucket, and prefer a quiet reviewer over a loud one.

3. Greptile

The bug hound. It indexes your whole codebase and reviews each PR against that full context, which is why its catch rate tops the pack. The tradeoff is real and worth stating: more bugs caught also means more false positives per run. Greptile fits teams where catch rate is the metric that matters most, who are on GitHub, and who have the reviewer bandwidth to triage a higher volume of findings. It's an especially strong pick for teams whose PRs increasingly come from coding agents, where the large volume of machine-authored changes needs machine-scale scrutiny.

4. Qodo

Built for enterprise engineering at real scale, across hundreds or thousands of repositories. Its standout move is bundling test generation into review. When Qodo finds an untested code path, it doesn't just tell you to write a test, it writes one. It also focuses on structured review with ticket compliance checks and custom labels. Best for large organizations with low test coverage that want review and coverage to improve together instead of as separate initiatives.

5. Graphite

Code review reimagined around stacked PRs, breaking large changes into small dependent pieces that merge in sequence. Its AI reviewer, launched as Diamond and now folded into Graphite Agent, keeps an unhelpful comment rate below 3% and reports that developers change their code when it comments more often than they do for human reviewers. Best for teams that already work in a stacked-diff style or want to, and value tight, high-signal feedback on small units of change.

6. GitHub Copilot code review

The practical default. If you already pay for Copilot, its built-in review adds AI pull request review at no extra cost, native to GitHub, summarizing changes and suggesting improvements inline. It's an assistive layer rather than an autonomous agent, and it won't match the depth of full-codebase tools, but the friction to turn it on is almost zero. Best for teams that want to try AI review without adding a vendor, or that work entirely inside GitHub.

The Evaluation Criteria That Separate Good AI Code Review From Impressive Demos

Demos are designed to catch a planted bug in a clean example. Your codebase is neither clean nor planted, so test against reality before you decide.

Run the tool on real PRs from last month, ones where you already know what broke and what didn't. The question isn't whether it finds the bug in the demo. It's whether it finds the bug that actually shipped, and whether it stays silent on the 40 lines that were fine. Precision matters more than recall past a certain point, because a reviewer who gets five false alarms stops reading the sixth comment, and then the tool is worse than useless.

Measure noise directly. Count the unhelpful comments per PR over a week. Anything a developer would roll their eyes at is a cost, not a catch. The tools that survive real adoption are the ones that respect reviewer focus, which is the whole reason you're buying one.

Check how it handles your context. Point it at a PR whose correctness depends on a caller two files away and see whether it notices. Diff-only tools fail this test cleanly, and it's the quickest way to sort the linters from the reviewers.

Check whether it can learn your standards. A good tool lets you encode team-specific rules and enforces them consistently, which is where AI review starts doing something your wiki never could. Then look at where it fits: does it live only in the PR, or does it connect to the ticket, the tests, and the fixes? A reviewer that comments but can't act leaves the most boring part, applying the feedback, entirely on you.

How AI Code Review Fits Into a Broader SDLC Automation Strategy

Here's what most comparison posts miss. AI pull request review is not a destination. It's one node in a graph, and its value depends largely on what surrounds it.

Consider where a PR comes from and where it goes. It comes from a ticket that someone triaged, scoped, and turned into a change. It goes toward tests, a merge, a deploy, and eventually an incident or the absence of one. A code review agent operating in isolation improves just one edge of that graph. A code review agent operating inside the workflow can pull the ticket's acceptance criteria into the review, generate the missing tests, apply the reviewer's fixes across files, and re-run itself, all without a human stitching the steps together by hand.

Organizations that get the most out of AI code review have typically automated around it first. They've let agents handle triage so tickets arrive scoped. They've let agents draft requirements and design so the PR reflects a real plan. They've wired review into CI so findings gate merges automatically. In that world, review stops being a bottleneck a human babysits and becomes a checkpoint that mostly runs itself, with humans stepping in for the decisions that need judgment. We've written previously about why the real future of AI development isn't a new IDE but a new interface for coordinating this work, and how Overcut compares to Copilot, Cursor, and ChatGPT as tools built for the individual rather than the organization.

The practical takeaway is to choose a review tool that assumes it's part of something larger. The standalone bots are solid, and for a small team a standalone bot might be exactly right. But the teams scaling agents past a handful of workflows will feel the seams of a review tool that can't talk to the ticket, the tests, or the fix. Buy for where you're going, not just where the queue hurts right now.

FAQs

Can AI code review tools enforce team-specific standards?

Yes, the stronger ones do. Most context-aware tools let you encode custom rules, coding patterns, and conventions that the reviewer applies consistently on every PR. This is where AI review outperforms a wiki nobody reads, because the standard shows up in the pull request itself. The catch is that rules need upkeep, so treat them like code: version them, review them, and remove the ones that create noise.

How do AI tools handle security vs. style issues?

Differently, and the split is worth understanding. Style issues are shallow and diff-local, so almost every tool handles them. Code security review is harder. AI reviewers catch pattern-based risks like injection, hardcoded secrets, and missing validation, plus logic flaws such as broken authorization that deterministic scanners miss. They don't replace formal SAST tools with taint tracking, and their findings rarely map to CWE or OWASP categories, so combine them for compliance work.

Will AI code review replace senior engineers?

No, and the tools that claim otherwise should concern you. AI review shifts where senior attention goes rather than removing the need for it. Machines are great at catching the mechanical bugs, the missing tests, and the cross-file inconsistencies that burn a reviewer's afternoon. They're weak at judgment calls about architecture, tradeoffs, and whether a change is even the right idea. That's the work seniors should have been doing all along.

How does AI code review handle large pull requests?

Better than humans, usually, since a model doesn't fatigue at line 800 the way a reviewer does at 4:45pm. Full-codebase tools handle large PRs best because they evaluate changes against the whole repository rather than the diff alone. That said, giant PRs are hard for everyone, which is why stacked-diff approaches that break changes into small dependent pieces produce sharper reviews. The best fix is smaller PRs, and good tooling pushes you there.

What separates AI code review from SonarQube?

Determinism versus context. SonarQube runs static analysis, so the same code yields the same findings every time, mapped to specific rules, which is just what compliance needs. AI review is probabilistic and context-aware, catching things rules can't express, like an endpoint that exposes too much data or authorization logic that's subtly wrong. They're complements, not replacements. Deterministic analysis for the standards you must prove, AI review for the bugs no rule anticipated.

10 Best AI Video Platforms for Explaining Complex Topics in 2026

Or Hillel — Sun, 21 Jun 2026 21:00:00 +0000

Every team faces a common challenge: there is too much information to share and not enough attention to cover it all. Documentation often goes unread, onboarding presentations get quickly skimmed, and the one engineer who understands the legacy system becomes a critical point of failure for knowledge. This overload of information has turned into a communication issue rather than a content problem. It affects everything from compliance to product education.

AI video platforms are tools that use AI to convert a script, document, or recording into a video. This way, a team can explain something once and share it widely. Instead of writing another wiki page that no one reads, you can turn a complex topic into a short video that makes it easier to understand. Tools like simpleshow and D-ID are now used in onboarding, training, product education, compliance, and internal communication. They help customers understand a product without needing to make a support call.

These tools do not replace good writing or clear ideas. What they do is make it cheaper to communicate those ideas to many people without losing their meaning. The following is an overview of ten platforms designed to explain complex topics. They are ranked by how well they maintain clarity at a large scale, starting with the best options.

1. simpleshow - AI explainer videos for enterprise communication

simpleshow is built around one discipline: taking a complicated subject and reducing it to a short explainer anyone can follow. Its AI does most of the work of explainer video creation, turning a script or even a rough document into a structured storyboard with visuals and voiceover, so a non-technical team can produce consistent explainer videos without a designer. The result reads less like a polished marketing clip and more like a clear walkthrough, which is what simplifying complex business topics usually calls for.

Key Features

AI-assisted explainer creation from a script or document
Enterprise scalability for global, multi-department rollouts
Voiceover and translation for multilingual audiences
Brand controls and a corporate-friendly visual style

Best For: Onboarding and corporate training, compliance, change management, SOP communication, internal communication, and knowledge transfer, where the same message has to reach a lot of people and be understood the same way.

2. D-ID - Interactive AI video experiences

D-ID pushes past one-way video into interactive territory, pairing avatars that present a script with conversational agents that answer questions in real time. That turns a static explainer into something closer to a guided conversation, and a solid API makes it practical to embed an avatar straight into a portal or product.

Key Features

Real-time conversational AI avatars
Text-to-video in many languages
API and embeds for portals, intranets, and apps

Best For: Interactive onboarding, in-app guidance, and knowledge bases where people want answers rather than passive playback.

3. Vyond - Enterprise animated explainers

Vyond is a mature animation platform for business storytelling, with custom characters and scenes and AI that can draft a script and assemble scenes from a prompt. Its creative control over the animation itself goes deeper than most AI-first tools.

Key Features

Character-driven animated storytelling
AI scene and script generation
A large library of assets and templates

Best For: Scenario-based training and narrative explainers where characters and situations carry the message.

4. Powtoon - Business communication videos

Powtoon keeps things quick and presentation-led. Templates, a drag-and-drop editor, and AI drafting turn a slide deck or memo into something more watchable than a PDF, with almost no learning curve.

Key Features

Drag-and-drop editor with a deep template library
AI-assisted drafting
Fast turnaround for everyday business video

Best For: Internal announcements, lightweight training, and giving routine business updates some polish.

5. Loom - Async knowledge sharing

Loom records your screen and voice to explain something without scheduling a meeting, and its AI layer now adds titles, summaries, chapters, and transcripts. It captures real, in-the-moment explanation rather than scripted, produced video, which is often exactly what a developer needs to pass on context.

Key Features

One-click screen and camera recording
AI summaries, chapters, and transcripts
Searchable, easily shared video

Best For: Async knowledge sharing, code walkthroughs, bug reproductions, and quick how-tos between teammates.

6. Colossyan - AI learning and training videos

Colossyan generates presenter-led videos from a script with a clear tilt toward workplace learning. Its standout is built-in interactivity, with branching scenarios and in-video quizzes that check understanding instead of assuming it.

Key Features

AI avatars and text-to-video
Branching scenarios and in-video quizzes
Quick translation into many languages

Best For: Structured training and onboarding modules that need more than passive viewing.

7. Pictory - AI-powered video summaries

Pictory turns long-form content into short video, surfacing the key points from an article, webinar recording, or script and assembling a concise clip with captions and stock footage. Its strength is summarization, distilling what already exists rather than starting from a blank page.

Key Features

Long-to-short video summarization
Automatic captioning
A large stock footage library

Best For: Repurposing existing content, summarizing webinars, and producing highlight reels at volume.

8. Veed - Simplified AI video editing

Veed is a browser-based editor that wraps AI helpers around ordinary editing: auto-subtitles, background removal, voice cleanup, and text-to-video. It assumes you already have raw video and want to make it presentable quickly.

Key Features

Browser-based editing, nothing to install
Auto-subtitles and background removal
Voice cleanup and text-to-video helpers

Best For: Quick edits, subtitling, and cleaning up recorded footage before sharing it widely.

9. HeyGen - AI marketing avatar videos

HeyGen produces realistic avatar videos and is strong at localization, with translation and lip-sync that adapt one video into many languages. It's at its best on polished, presenter-style content, and narrower than the explainer-first tools for breaking down a complex internal process.

Key Features

Realistic and custom avatars
Translation with matched lip-sync
Business and marketing templates

Best For: Marketing videos, product announcements, and localized avatar presentations.

10. Synthesia - Corporate avatar explainers

Synthesia converts scripts into presenter-led videos across a wide range of languages with easy updates, a dependable way to standardize corporate video around a consistent AI presenter. The caveat is that a talking presenter isn't always the clearest route to a difficult idea.

Key Features

A deep library of realistic avatars
Text-to-video in 140+ languages
One-click updates when content changes

Best For: Corporate announcements and routine training built around an AI presenter.

How to choose an AI video platform

The best tool tracks the kind of complexity in front of you. To make a difficult subject clear to a broad audience, an explainer-first platform like simpleshow fits the brief. For interactive, answer-on-demand learning, D-ID. For async sharing inside an engineering team, Loom. Avatar tools like Synthesia and HeyGen earn their place when a polished presenter or fast localization outweighs the need to simplify. A useful test before committing: take your hardest-to-explain topic, draft it once, and see which platform makes it land for someone outside the team. Match the platform to the actual problem, and the video stops being one more asset nobody opens.

What's next for AI video

Video content is changing. Instead of just watching videos, we are now seeing videos that interact and respond to viewers. Conversational avatars are moving from being simple demos to becoming useful tools for onboarding and support. Meanwhile, tools that generate video content are starting to add features that make them more interactive and easier to track, similar to learning platforms.

As a result, the difference between creating a video and managing information is becoming less clear. The same content can now be automatically adjusted based on a viewer's role, region, or knowledge level. For teams that explain complex topics, this means they will spend less time creating different versions of content and more time focusing on whether the explanation is effective.

Common questions

What is an AI video platform? Software that uses AI to turn a script, document, or recording into video, so a team can explain something once and share it at scale. Some focus on creating content; others add interactivity or tracking.

Which AI video platform is best for explaining complex topics? For making a difficult subject clear to a broad audience, explainer-first tools like simpleshow are the strongest fit, since they're built to simplify rather than just render a presenter reading a script. Interactive needs point toward D-ID, and quick async sharing toward Loom.

Can these platforms handle multilingual, enterprise-scale communication? Most support translation into many languages and scale across departments. simpleshow, Synthesia, HeyGen, and D-ID all generate or translate the same content across languages, which makes consistent global communication far less manual.

10 Best Real-Time Conversational AI Video Platforms in 2026

Or Hillel — Sun, 14 Jun 2026 09:27:34 +0000

Here is how most AI video platforms work: You write a script, choose an avatar, render the video, and publish it. That is useful. Training teams use it for onboarding. Marketing teams use it for product explainers. Support teams use it for short help videos. But a rendered video cannot respond when the viewer has a question.

That is the gap real-time conversational AI video can close. Instead of watching an avatar deliver a fixed script, users can speak or type and get a visual response back. Sometimes that looks like a product guide. Sometimes it becomes a training coach, a support assistant, or an AI agent inside an app.

Some of the platforms are built for live conversational avatars. Some are closer to classic AI video makers. Others sit near the category: workplace training, business presenters, generated characters, or interactive learning.

For this article, the top spots go to platforms closest to live interaction. The lower-ranked tools may still be useful, but they mostly solve a different job.

How the platforms were judged

The main question was: can this platform support an actual conversation, or does it mainly produce a finished video?

A pre-rendered avatar clip can be useful. No argument there. Training teams use them. Marketing teams use them. Internal comms teams use them when nobody wants to film another talking-head update.

But a live conversational avatar is closer to product infrastructure. It needs lower latency. It needs APIs. It needs to connect to an LLM, a knowledge base, speech tools, product data, maybe a CRM. It also needs to recover when the user says something messy, which they will. That is the lens for this list.

1. D-ID

D-ID is the cleanest fit for this category.

The platform is built around expressive visual AI agents that can speak with users in real time. That separates it from tools where the avatar is mostly a presenter reading a script. D-ID becomes more interesting when the avatar is part of the interaction itself.

A customer asks a product question. A new employee talks to an onboarding guide. A learner practices a difficult conversation with an AI character. In those cases, the video layer is not decoration. It changes the interface.

For developers, the useful part is connection. A D-ID avatar can sit on top of an LLM, internal documentation, support content, product data, or a knowledge base. That puts it closer to agent infrastructure than standard video software.

There is also a clear fit with agentic video and broader AI agents use cases, where users can interact with information rather than simply consume it. A viewer watches a video, stops, asks a question, and gets an answer in the same experience. Not every company needs that today. But if you have a library of training videos that people skip through, the use case is obvious.

We choose D-ID as the best real-time conversational AI video platform for 2026 because it is focused on the actual shift: from video as content to video as an interface.

2. Tavus

Tavus is another serious platform here, especially for product teams and developers. Its focus is conversational video infrastructure. Slightly dry phrase. Useful idea.

Tavus is mainly about building AI video agents that can appear inside a product and respond in a more personal way than a standard chatbot. The product language feels closer to APIs and programmable agents than to "choose a template and export a video." That's relevant for onboarding, tutoring, customer conversations, and product-led flows where the AI human is not a separate asset but part of the app.

Its work around replicas and perception gives it a distinct angle. The promise is that the avatar can respond with some sense of timing and awareness.

That is hard to get right. Users notice when it is off.

3. Hour One

Hour One is not the most real-time conversational tool in this list. Its strength is enterprise presenter video.

That still earns it a place, because many companies do not start with live AI agents. They start with a pile of slides, onboarding decks, policy updates, and internal explainers that nobody has time to turn into good video.

Hour One helps turn that material into presenter-led AI videos. Training, sales enablement, product education, internal communication. Familiar use cases. Very real ones.

For a team that wants a live AI agent, Hour One may not be the first stop. For a team that wants professional presenter videos without booking a studio, it makes sense.

Different job.

4. Colossyan

Colossyan is mostly a workplace learning tool and useful when training content changes often: Compliance updates. New policies. Manager training. Onboarding modules. A regional team needs a localized version.

This is where AI video can be genuinely helpful. Because editing training content is usually boring, slow, and weirdly expensive.

Colossyan is not the first choice for live conversational agents. Its value is more direct: make workplace learning videos faster, keep them consistent, update them without starting over.

For L&D teams, that matters more than another shiny demo.

5. Elai

Elai sits close to Colossyan, but it has a broader education and training angle. You can create AI avatar videos from text and supports translations, voice features, and interactive learning elements. That makes it useful for teams that have content but not much production capacity.

The stronger use cases are practical: customer education, internal training, onboarding, and repeatable learning modules.

Elai is not trying to be the deepest real-time agent platform. At least not in the way D-ID, Tavus, or Simli are. It is better understood as an AI video tool for teams that need to produce learning content without making every project a production project.

The interactive pieces keep it relevant here. They move the experience beyond a plain presenter video, even if it is not a full live agent.

6. Simli

Simli deserves attention because it focuses on latency. That sounds technical but is also the whole experience: If a visual AI agent responds too slowly, people feel it immediately. The face hangs there. The voice comes late. The magic disappears. You do not need a UX study to notice it.

Simli is built for low-latency conversational avatars. That makes it a good fit for developers building live AI tutors, coaching bots, language practice tools, role-play simulations, or sales assistants.

It is not so much a finished content studio but a component for builders. Good. Not every team needs another dashboard for making videos. Some teams need an avatar layer they can plug into something else.

7. DeepBrain AI

DeepBrain AI is built around AI humans and business video creation.

Its AI Studios product supports avatar-led videos for education, media, support, and enterprise communication. The platform feels more like polished business video than experimental agent infrastructure.

That is not a bad thing. A lot of companies still need realistic presenter videos. A support team may want short help videos. A training team may want updates that do not require filming a human presenter every time.

DeepBrain AI becomes more interesting when companies think of AI humans as a recurring communication layer rather than one-off video assets. It is not as close to real-time conversational agents as the top tools here, but it belongs in the broader category.

8. Hedra

Hedra comes from another direction. Most platforms in this list start with the business presenter. Hedra starts closer to character creation.

That makes it useful for teams that want generated characters, expressive scenes, or creative video formats that do not look like another corporate training module. It is more brand lab than enterprise support desk.

Would you pick Hedra first for a live customer service agent? Probably not. But conversational AI video will not always look like a person in a blazer reading from a script. Some products will use fictional guides. Some brands will want characters. Some learning experiences may work better with stylized people than realistic ones.

Hedra fits that edge of the category.

9. HeyGen

HeyGen is one of the best-known AI avatar video tools, especially in marketing and sales.

It is good at polished avatar videos, video translation, custom avatars, and quick business content. A team might use it for product explainers, landing page videos, campaign content, sales enablement, or localized assets.

For real-time conversational AI video, though, HeyGen is not the strongest fit. Its center of gravity is still video creation, not live interaction. That is why it sits lower here.

If the job is "make a good-looking avatar video," HeyGen can be useful. If the job is "build a live AI agent users can talk to," other tools are closer.

10. Synthesia

Synthesia is a familiar name in corporate AI video. Training videos. Onboarding. Internal updates. Business presentations. The workflow is easy to understand: write the script, choose an avatar, create the video.

That simplicity is part of the appeal. Corporate teams often need reliable video content without filming employees or hiring production crews.

In this list, Synthesia ranks lower because its core strength is scripted video, not real-time conversation. It is a strong corporate avatar video platform. It is just not the most direct answer to conversational AI video. Again, different job.

Where this category is going

The big shift is responsiveness: A user can pause a product video and asks a question. A sales rep practices an objection with an AI buyer. A new employee asks an onboarding agent what to do next.

That creates a different technical stack. Avatar rendering. Speech. LLM orchestration. Retrieval. Product state. User context. Logging. Guardrails. All the unglamorous parts.

And those parts decide whether the experience holds up.

A good demo can last thirty seconds. A useful agent has to survive confused users, bad audio, unclear questions, missing data, and awkward silence. That is where the category gets serious.

Common questions

What is real-time conversational AI video?

It is an AI video experience where a user can speak or type to an avatar and receive a visual response in real time. The video is part of the interaction, not just a finished asset.

How is it different from AI avatar video?

AI avatar video is usually scripted and rendered before anyone watches it. Conversational AI video responds to the user. The path can change depending on the question, task, or context.

What should developers check first?

Latency. Then API quality, speech support, LLM compatibility, documentation, and how the platform connects to the rest of the product. A beautiful avatar is not enough if the response feels slow or detached.

Best 5 Tools to Help Eliminate CVEs from Container Images

Or Hillel — Thu, 18 Sep 2025 11:34:17 +0000

Key Takeaways

Automated CVE scanning from build to runtime is no longer optional.
The right tools eliminate friction, integrate with modern workflows, and turn detection into rapid remediation.
Continuous monitoring, context-aware prioritization, and real developer empowerment are the hallmarks of best-in-class container security.

Containers have revolutionized DevOps, making it easier to build, deploy, and manage applications. However, this agility comes at a cost: containers can be a vector for security vulnerabilities, specifically Common Vulnerabilities and Exposures (CVEs), which can compromise the integrity, confidentiality, and availability of your applications.

CVEs are publicly disclosed cybersecurity vulnerabilities and exposures found in software. Since containers bundle applications with all their dependencies into a single package, even a single vulnerable library or outdated base image can be the weak link that exposes your organization to security risks.

The solution? Automated, high-fidelity vulnerability scanning and remediation embedded into your container lifecycle.

Why CVEs Pose a Unique Risk in Containers

Before exploring the solutions, let’s understand the problem at hand. Containers are built on images that may include operating system packages, language libraries, and third-party dependencies. Any of these components may contain vulnerable code, and traditional security controls often don’t provide visibility inside containers.

Key Challenges

Containers are ephemeral and multiply rapidly, complicating traditional security scans.
Images may still contain outdated packages from upstream sources.
Developers may unknowingly include third-party dependencies with known CVEs.
Manual remediation does not scale with the velocity of DevOps pipelines.

The Best Tools to Help Eliminate CVEs from Container Images

1. Echo

Echo is an emerging innovative platform focused on cloud-native application security, particularly for container images. Its primary strength lies in making it easier for development and operations teams to address CVEs before deployment.

Key Features:

Zero-CVE Images: echo images are built from source with minimal components, so vulnerabilities are minimized by design and CVEs effectively drop to zero.
Automated Patching SLA: vulnerabilities are handled within 24 hours and fully patched in up to 7 days (critical/high), with defined SLAs for medium/low as well.
Registry Mirroring & Auto-Cleanup: private registries automatically pull fixes so you always use the latest clean version.
Backport Protection: stay on known working image versions without sacrificing security—backports ensure fixes don’t break your app.

2. Wiz

Wiz is best known as a comprehensive cloud security platform covering everything from infrastructure posture management to workload and data security. In the context of containers, Wiz’s vulnerability management module delivers robust scanning and remediation.

Key Features:

Agentless Scanning: Scans container images directly from registries and cloud environments.
Broad CVE Coverage: Detects zero-days and known CVEs from multiple feeds.
Risk Prioritization: Ranks CVEs by exploitability, reachability, and business context.
Cloud Integration: Deep integration with AWS, Azure, GCP, and Kubernetes clusters.
Remediation Collaboration: Groups findings and provides prescriptive fixes.

3. SentinelOne

SentinelOne is recognized for its autonomous threat detection powered by AI and behavioral analytics. Their container security offering extends protection to cloud-native applications, focusing on vulnerability scanning and runtime defense.

Key Features:

Automated Image Scanning: Integrated into CI/CD pipelines.
Runtime Protection: Monitors and remediates live container environments.
Attack Surface Reduction: Quarantines containers with critical vulnerabilities.
Unified Visibility: Single dashboard for hosts, VMs, and containers.
Threat Intelligence Integration: Enriches vulnerability data and flags zero-days.

4. Snyk Container

Snyk is a developer-centric security platform focused on open source and container security. Its container offering is trusted for usability, detailed fix recommendations, and integration throughout the software development lifecycle.

Key Features:

Comprehensive Image Scanning: Covers base OS, dependencies, Dockerfiles, and configs.
Developer-Friendly Fixes: Suggests direct edits or PRs with secure versions.
Real-Time CVE Database: Updated frequently with new disclosures.
Integration Everywhere: Works with GitHub, GitLab, Bitbucket, Docker Hub, CI/CD.
Policy Controls: Define thresholds, enforce rules, and block vulnerable images.

5. Grype

Grype is a popular open-source vulnerability scanner purpose-built for container images and filesystems, developed by Anchore. It stands out for simplicity, efficiency, and flexibility.

Key Features:

Open Source: No vendor lock-in.
Rich Ecosystem Support: Scans Docker, OCI, Alpine, Deb, RPM, etc.
Extensible Integration: CLI or embedded in pipelines.
Up-to-Date Feeds: Syncs with NVD and distribution-maintained databases.
Flexible Reporting: JSON, table, or custom formats for CI tools and dashboards.

CVE Management Best Practices

Eliminating CVEs from container images is more than tooling—it’s a cultural and procedural shift. Here are industry best practices:

Shift Left Security

Embed image scanning early in the lifecycle to reduce risk.
Continuous Automation and Policy Enforcement

Automate scans on every build or image push. Fail builds if critical CVEs appear.
Track and Scan Third-Party Dependencies

Use tools that analyze external libraries and frameworks.
Integrate with Ticketing and Workflow Systems

Convert findings into tickets (e.g., Jira, GitHub Issues, ServiceNow).
Monitor in Production

Pair static image scanning with runtime monitoring for new vulnerabilities.
Remediate with Prioritization

Fix first what’s most exploitable, internet-facing, or business-critical.
Foster DevSecOps Collaboration

Choose tools that fit developer, DevOps, and security workflows.

Final Thoughts

Eliminating CVEs from your container images is non-negotiable for modern application security. By automating this process with tools like Echo, security becomes part of your software DNA, not an afterthought. Select the best mix of tools based on your stack, threat model, and team preferences, and make vulnerability management a continuous, collaborative, and automated part of your DevOps culture.

Remember: The weakest link in your container ecosystem could be a simple, fixable CVE. Act proactively, scan, remediate, and monitor constantly.

The Top 8 ML Model Monitoring Tools

Or Hillel — Tue, 19 Nov 2024 11:52:19 +0000

In the rapidly evolving landscape of artificial intelligence and machine learning, the importance of robust model monitoring cannot be overstated. As organizations increasingly rely on machine learning models to drive critical business decisions, the need for sophisticated monitoring tools has become paramount. This comprehensive guide explores the top 8 ML model monitoring tools that are transforming how organizations maintain, optimize, and ensure the reliability of their machine learning systems in 2024.

Understanding ML Model Monitoring

The complexity of modern machine learning systems demands a sophisticated approach to monitoring. Unlike traditional software systems, ML models present unique challenges that require specialized monitoring solutions. These challenges stem from the dynamic nature of data, the complexity of model behavior, and the critical importance of maintaining performance over time.

The Critical Nature of Model Monitoring

Model monitoring serves as the cornerstone of reliable machine learning operations. Performance degradation, a common challenge in ML systems, occurs gradually and often imperceptibly. Models trained on historical data naturally begin to drift as real-world patterns evolve, leading to decreased prediction accuracy and potentially significant business impact. This degradation can manifest in various ways, from subtle shifts in prediction patterns to dramatic changes in model outputs.

Data quality issues represent another critical concern that necessitates robust monitoring. In production environments, input data streams can be affected by numerous factors: upstream system changes, modifications in data collection processes, or evolution in user behavior patterns. These changes can introduce corrupted or invalid data, leading to degraded model performance. Moreover, feature engineering processes might break or become less effective over time, further compromising model accuracy.

Resource utilization presents its own set of challenges that demand careful monitoring. Machine learning systems often require substantial computational resources, and inefficient resource allocation can lead to increased costs and decreased performance. Organizations must continuously optimize their computing resources, monitor memory usage patterns, and track GPU utilization to maintain cost-effective operations while ensuring model performance meets business requirements.

The Best 8 ML Monitoring Tools

1. Deepchecks ML Monitoring

Deepchecks stands out as a robust open-source testing and validation framework specifically designed for ML models and data. The platform excels in detecting data and model drift, validating model performance, and ensuring data integrity throughout the ML lifecycle. Its distinctive feature is the comprehensive suite of tests that can be run at various stages of model development and deployment.

What sets Deepchecks apart is its ability to perform sophisticated validation checks without requiring extensive configuration. The platform automatically identifies potential issues in both data and model behavior, providing detailed insights through its intuitive interface. For data scientists and ML engineers, Deepchecks offers customizable validation suites that can be integrated seamlessly into existing ML pipelines.

The tool's strength lies in its three-tiered testing approach: data integrity, model performance, and production monitoring. In production environments, Deepchecks continuously monitors for data drift, concept drift, and model performance degradation, enabling teams to proactively address issues before they impact business outcomes. Its Python SDK allows for easy integration with popular ML frameworks like scikit-learn, PyTorch, and TensorFlow.

2. Qwak

Qwak represents a new generation of ML platforms that combines model monitoring with end-to-end MLOps capabilities. This comprehensive platform offers robust monitoring features while streamlining the entire machine learning lifecycle. What distinguishes Qwak is its unified approach to model development, deployment, and monitoring.

The platform's monitoring capabilities are deeply integrated with its deployment infrastructure, providing real-time insights into model performance, data quality, and operational metrics. Qwak excels in detecting and alerting on various types of drift, including feature drift, prediction drift, and concept drift. Its automated monitoring systems can track complex metrics across multiple models and deployments simultaneously.

For organizations looking to scale their ML operations, Qwak offers sophisticated A/B testing capabilities, automated retraining pipelines, and comprehensive version control for both models and data. The platform's unified dashboard provides stakeholders with clear visibility into model health, resource utilization, and business impact metrics.

3. Datadog

Datadog has evolved from a general-purpose monitoring solution to include powerful capabilities specifically designed for ML systems. Its strength lies in providing comprehensive observability across the entire ML infrastructure, combining traditional infrastructure monitoring with specialized ML metrics.

The platform's ML monitoring capabilities include sophisticated anomaly detection, automated alerting systems, and detailed performance tracking. Datadog's distributed tracing functionality allows teams to monitor entire ML pipelines, from data ingestion to prediction serving. The platform excels in correlating ML-specific metrics with broader system performance indicators, providing a holistic view of ML applications.

What sets Datadog apart is its extensive integration ecosystem and powerful visualization capabilities. Teams can create custom dashboards that combine ML metrics with infrastructure metrics, business KPIs, and log data, enabling comprehensive analysis of model performance in the context of overall system health.

4. Prometheus

Prometheus has become the de facto standard for metrics collection in cloud-native environments, and its capabilities extend effectively to ML model monitoring. The platform's strength lies in its highly scalable architecture and powerful query language (PromQL), which enables sophisticated analysis of time-series data.

For ML applications, Prometheus excels in collecting and storing crucial performance metrics, including prediction latency, throughput, and resource utilization. Its pull-based architecture and service discovery mechanisms make it particularly well-suited for monitoring distributed ML systems in containerized environments.

The platform's support for custom metrics enables teams to track ML-specific indicators while maintaining compatibility with standard monitoring practices. Prometheus's alerting capabilities, combined with its integration with alert management systems, enable proactive monitoring of ML model health.

5. Grafana

Grafana serves as a versatile visualization and analytics platform that excels in creating comprehensive dashboards for ML monitoring. While often used in conjunction with Prometheus, Grafana's support for multiple data sources makes it an invaluable tool for creating unified monitoring views across different ML platforms and tools.

The platform's strength lies in its ability to create sophisticated visualizations that combine metrics from various sources, enabling teams to correlate model performance with system metrics and business KPIs. Grafana's alerting system supports complex alert conditions and can integrate with various notification channels.

For ML monitoring, Grafana's template variables and dynamic dashboards allow teams to create reusable monitoring solutions that can scale across multiple models and deployments. Its plugin architecture enables extended functionality specific to ML monitoring needs.

6. Checkmk

Checkmk brings enterprise-grade monitoring capabilities to ML operations, offering a comprehensive solution for monitoring both ML models and the infrastructure they run on. The platform's strength lies in its ability to combine detailed system monitoring with application-specific metrics.

For ML applications, Checkmk provides specialized monitoring agents and plugins that can track model performance, data quality, and resource utilization. Its automated discovery capabilities help identify new monitoring targets as ML systems scale, while its distributed architecture ensures reliable monitoring of geographically dispersed deployments.

The platform's sophisticated alerting system includes predictive capabilities, enabling teams to address potential issues before they impact model performance. Checkmk's business intelligence features help translate technical metrics into meaningful business insights.

7. Amazon SageMaker Model Monitor

Amazon SageMaker Model Monitor represents AWS's comprehensive solution for ML model monitoring, deeply integrated with the broader AWS ecosystem. The platform automates the monitoring of models deployed on SageMaker, providing sophisticated capabilities for detecting data drift, bias, and feature attribution drift.

The tool's strength lies in its automated baseline creation and continuous monitoring capabilities. It automatically analyzes training data to establish baselines for model behavior and data distributions, then continuously monitors production data for deviations from these baselines. The platform's integration with AWS CloudWatch enables sophisticated alerting and visualization capabilities.

SageMaker Model Monitor's ability to handle large-scale deployments, combined with its pay-as-you-go pricing model, makes it particularly attractive for organizations already invested in the AWS ecosystem.

8. MLDemon

MLDemon emerges as a specialized monitoring solution focused on the unique challenges of maintaining ML models in production. The platform combines traditional monitoring capabilities with advanced ML-specific features, providing comprehensive visibility into model performance and health.

The tool's distinctive feature is its automated detection of complex patterns in model behavior, enabling early identification of potential issues. MLDemon's monitoring capabilities extend beyond basic metrics to include sophisticated analysis of feature importance stability, prediction drift, and data quality.

For teams managing multiple models, MLDemon offers powerful comparison tools that help identify performance differences across model versions and deployments. Its integration capabilities allow it to work seamlessly with existing ML infrastructure while providing valuable insights through its intuitive interface.

Essential Model Performance Metrics

The foundation of effective model monitoring lies in comprehensive performance metrics. These metrics provide quantitative insights into model behavior and effectiveness. Traditional statistical metrics form the backbone of model evaluation, but their interpretation requires context and understanding of business objectives.

Accuracy

Accuracy, while seemingly straightforward, must be considered within the context of class distribution and business impact. In real-world applications, overall accuracy can be misleading, particularly in cases of class imbalance. For instance, in fraud detection systems, a model might achieve 99% accuracy while failing to identify critical fraudulent transactions that represent a small percentage of total transactions.

Precision and Recall

Precision and recall metrics offer deeper insights into model performance, particularly in imbalanced datasets. Precision measures the accuracy of positive predictions, while recall indicates the model's ability to identify positive cases. The F1 score, as the harmonic mean of precision and recall, provides a balanced measure of model performance, especially useful when there's a need to find an optimal balance between precision and recall.

AUC-ROC

The Area Under the Receiver Operating Characteristic curve (AUC-ROC) provides a comprehensive view of classification performance across various threshold settings. This metric is particularly valuable in scenarios where the threshold for classification can be adjusted based on business requirements. For example, in clinical diagnosis systems, the threshold might be adjusted to favor higher sensitivity at the cost of more false positives.

Advanced Performance Analysis

Beyond basic statistical metrics, modern ML monitoring systems incorporate sophisticated analysis techniques. Time-series analysis of model predictions can reveal gradual drift patterns that might not be apparent in aggregate metrics. Feature importance stability monitoring helps identify when the relationship between input features and target variables changes significantly.

For regression models, metrics like Mean Square Error (MSE) and Root Mean Square Error (RMSE) provide insights into prediction accuracy, but their interpretation must consider the scale of the target variable and business context. Mean Absolute Error (MAE) offers a more interpretable metric in many business contexts, as it represents the average magnitude of errors in the same units as the target variable.

The Complete Guide to API Rate Limiting

Or Hillel — Tue, 07 May 2024 09:58:12 +0000

With the increasing demand for API usage, proper management becomes crucial to ensure smooth operations and prevent abuse or overload of resources. This is where API rate limiting comes into play. In this comprehensive guide, we will explore the concept of API rate limiting, its importance, popular rate limiting algorithms, and monitoring API usage and limits, as well as provide real-world examples to help you implement effective rate limiting strategies.

What is API rate limiting?

API rate limiting refers to controlling the number of API requests clients can make within a specified timeframe. API providers can restrict the data or operations a client can access by implementing rate limits over a given period. The rate limit defines the maximum number of requests allowed, often measured in requests per minute, hour, or day.

Organizations can prevent abuse, ensure fair usage, protect their resources from overloading, and maintain optimal client performance by enforcing rate limits. API rate limiting is a safeguard, providing the API infrastructure remains stable and available for all users, preventing any single client from monopolizing system resources.

One common strategy for implementing API rate limiting is to use a token bucket algorithm. Clients are assigned tokens that represent the number of requests they can make. As a client sends requests, tokens are consumed from their bucket. Once the bucket is empty, the client must wait until new tokens are added at a predefined rate. This method allows for bursts of requests while still maintaining an overall limit.

Some API providers offer different rate limits based on the type of client or the specific endpoint being accessed. For example, a public API may have lower rate limits for anonymous users than authenticated users with access to more features. This granular control helps tailor the API usage to different user needs and levels of access.

Why is API rate limiting important?

API rate limiting is crucial for several reasons. Here are a few of them:

It helps protect the API server from excessive traffic and potential denial of service attacks. By setting appropriate limits, organizations can prevent unauthorized or malicious clients from overloading resources and disrupting service for legitimate users.
Rate limiting promotes fair usage and prevents abuse. By defining specific limits, organizations can ensure that clients adhere to predefined usage thresholds, preventing them from extracting excessive data or placing an unnecessary burden on the API server. This promotes equitable access and prevents any single client from monopolizing system resources.
Rate limiting helps organizations manage API scalability and performance. By controlling the rate at which clients can make requests, API providers can ensure that their infrastructure remains stable, even during high-traffic periods. Rate limiting allows for efficient resource allocation, minimizing the impact on server performance and reducing the risk of system failures or performance degradation.
Organizations can mitigate the risk of brute force attacks and unauthorized access attempts by implementing rate limits. Limiting the number of requests a client can make within a specific timeframe adds an extra layer of protection against malicious activities, safeguarding sensitive data and preventing potential security breaches.
By regulating the volume of incoming requests, companies can better manage their resources and reduce unnecessary expenses associated with excessive bandwidth consumption or server overload. This cost-effective approach ensures that resources are utilized efficiently, improving financial sustainability and operational effectiveness in the long run.

Popular Rate Limiting Algorithms

Several rate limiting algorithms exist, each with its strengths and considerations. Commonly used algorithms include:

Fixed Window: In this approach, a fixed number of requests are allowed within a specific duration, such as 1000 requests per hour. Further requests are denied once the limit is reached until the window resets.
Sliding Window: This algorithm provides more flexibility by allowing a certain number of requests within a fixed window but with a smoothing factor. It will enable clients to make up for bursts by temporarily exceeding the limit as long as the average rate remains within the defined threshold.
Token Bucket: With this algorithm, clients are assigned tokens representing request allowances. Each request consumes a token, and once the tokens are depleted, further requests are denied until the system replenishes the token bucket.

The rate limiting algorithm's choice depends on the API's specific requirements, the expected usage patterns, and the desired level of control and flexibility.

Monitoring API Usage and Limits

Effective API rate limiting must be accompanied by robust monitoring and analytics to track client usage and ensure compliance with defined limits. By implementing comprehensive monitoring tools, organizations can gain insights into API usage patterns, identify potential abuse or anomalies, and make informed decisions for rate limit adjustments.

Monitoring API usage involves tracking the number of requests made by each client, analyzing the distribution of requests over time, and identifying any deviations from expected patterns. Notifications and alerts can be set up to notify administrators when clients approach their limits, enabling proactive measures to prevent service disruptions.

Organizations can leverage analytics to understand trends and usage patterns across clients or API endpoints. This data can help in capacity planning, identifying areas of potential optimization, and making data-driven decisions to improve overall API performance and user experience.

API Rate Limiting Examples

To understand API rate limiting in action, let's explore a few real-world examples:

Twitter API: Twitter implements rate limiting to prevent abuse and to ensure fair access for all developers using their API. Different levels of access are provided, each with specific rate limits. For example, the standard API allows 900 requests per 15-minute window, while the premium and enterprise tiers offer higher limits.
Google Maps API: Google imposes rate limits on the usage of their Maps API to prevent abuse and maintain optimal service for all users. The specific rate limits depend on the type of API calls and the authentication method used.
OpenAI API: OpenAI applies rate limiting to its APIs to manage demand and ensure a fair distribution of resources among users. The limits can vary based on the specific API model being used. For instance, GPT-4, one of OpenAI's most advanced models, has different rate limits based on the subscription plan and endpoint. Users might encounter limits such as 60 requests per minute for the standard plan, with possibilities for higher limits under custom arrangements for enterprise users.
Facebook API: Facebook's API, part of the Meta platform, enforces rate limiting to safeguard the user experience and ensure equitable access across its vast number of developers. Rate limits are based on the number of users interacting with the app rather than fixed time windows. For example, an app can make 200 API calls per hour per user, which scales as more users interact with the application. This dynamic rate limiting helps manage load and maintain performance as application usage grows.
HubSpot API: HubSpot utilizes rate limiting to maintain the stability and reliability of its platform as it serves numerous businesses and developers. The rate limits are designed to prevent any single user from overloading the system, ensuring consistent service for all. For standard API access, HubSpot typically allows up to 100 requests every 10 seconds per portal, with an additional daily cap of 250,000 requests. These limits help to manage the data flow smoothly and efficiently across their diverse customer base.
Claude API: Claude API, developed by Anthropic, employs rate limiting to manage system load and promote equitable resource distribution among its users. The rate limits vary depending on the API key's service plan and use case. Typically, users might encounter limits like 40 requests per minute for standard usage, with the potential for higher limits under enterprise agreements. These constraints are essential to ensure all users have access to the AI capabilities without degradation in service quality.

Multi-CDN Strategy: Benefits and Best Practices

Or Hillel — Tue, 14 Nov 2023 07:40:37 +0000

What is Multi-CDN?

A CDN (Content Delivery Network) is a network of geographically distributed servers that brings web content closer to where end users are located, to ensure high availability, optimized performance and low latency. Multi-CDN is the practice of employing a number of CDN providers simultaneously. This method augments the performance benefits of using a CDN while also ensuring redundancy and resilience and reducing costs.

To implement a M-CDN, organizations can use traffic management tools or Multi-CDN switching solutions that distribute and route content across the various CDN providers. Distribution is dynamic and will be determined and optimized in real-time, based on factors like performance, geographic reach, costs, use cases, and real-time conditions.

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Benefits of a M-CDN

There are multiple advantages for a Multi-CDN implementation. Some key advantages include:

1. Network Redundancy

The primary and most important advantage of a Multi-CDN strategy is redundancy, and, consequently, improved reliability. Outages are a common occurrence; a global outage could take the entire network down while local outages could force the CDN vendor to serve the traffic from a non-local PoP. The result would be a performance drop in the end users’ experience, often causing the application to become unusable. While these occurrences take place "only" a few times a year, local outages happen on a weekly, or even daily, basis.

A M-CDN enables enacting a failover strategy with additional CDN providers that have not been impacted. This ensures up to 5-nines of availability, minimizes the impact of disruptions, enables meeting SLAs and reduces the risk of a single point of failure.

2. Enhanced Performance

Another significant advantage of using multiple CDN providers is the improved content delivery performance for end users. Different CDN providers cover different regions to different extents. When using a single CDN, the organization is dependent on the CDN provider’s geographical coverage and server infrastructure. This coverage may not align with the organization's needs at all times.

Distributing content across multiple CDNs allows switching to the CDN with the best coverage and lowest latency for each user at any given time. This will result in faster load times that improve the user experience.

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3. Cost Optimization

M-CDN setups can help optimize content delivery costs. This is done by selectively choosing when to use different types of CDNs. While high premium CDNs provide a full set of features - including security services, edge computing and more - these come at a high cost. Standard CDNs, on the other hand, provide a more basic set of features, but at a lower cost. Organizations can use premium CDNs for critical tasks while cutting costs when performing simple or lower priority tasks by using standard CDNs.

In addition, different CDN providers offer competitive pricing models and packages. Organizations can select the most cost-effective option for each region or traffic type, reducing overall CDN expenses.

4. Flexibility

Multi-CDNs enable network professionals, IT, and DevOps teams to maximize the benefits offered by different CDN vendors. They provide flexibility in pricing by allowing the selection of the serving provider in specific areas or for specific portions of the traffic. The multi-vendor approach also allows leveraging the unique capabilities, such as unique Edge Computing functionality, offered by different providers. This flexibility provides customers with more control, increased bargaining power, and prevents them from being locked into a specific provider.

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Best Practices for Implementing a Multi-CDN

While the advantages of a M-CDN are clear, implementation is not as straightforward. A cautious approach is crucial when transitioning to a M-CDN architecture. The configuration of new CDNs is intricate and prone to errors. Even minor misconfigurations can lead to disruptive traffic interruptions.

To ensure a smooth transition, it is highly recommended to take the following best practices:

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1. Cherry Pick Your CDNs

Conduct a thorough evaluation of different CDN providers based on the requirements from best practice #1. Consider factors such as geographic coverage, server infrastructure, performance, CDN scalability, pricing models, features, and support services. Evaluate how well each provider can meet your specific requirements and objectives. Narrow the final choice down to two providers, run a POC and A/B test them over the course of a few weeks to determine which one can best answer your requirements.

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2. Configure Your New CDN

To effectively configure your new CDN, you need to replicate the settings of your primary CDN. Each role, policy and behavior needs to be manually and meticulously configured to align with the new CDN's engine, utilizing its API. Then, rigorous testing is necessary to ensure it functions properly. Otherwise, the result may be a deteriorated user experience, traffic loss and service unavailability.

3. Transit the Traffic Safely and Gradually

Once the CDN is configured, it’s time to proceed with a seamless and uninterrupted traffic transition that is based on Multi-CDN switching. It is recommended to employ an active/active strategy in a gradual manner. Begin by directing a small portion of the traffic to the new CDN. Progressively increase the volume of traffic being routed through it until the desired level is reached and you can rely on real-time switching.

4. Implement Traffic Management Tools

Utilize specialized traffic management tools or solutions to implement and manage your M-CDN effectively. Implementing such tools simplifies the management of a complex Multi-CDN environment. These tools can help automate traffic routing, monitor performance, collect data for analysis, and dynamically adjust traffic distribution based on real-time conditions. Make sure to never starve a CDN vendor - always keep all vendors with a portion of the traffic to keep the cache warm.

Many organizations use a DNS switching service, which routes traffic between the different CDNs. However, this method can become too rigid when conditions are dynamic, as it does not adjust levels of traffic according to changing needs. A DNS switching service also does not provide visibility into the traffic, making traffic management challenging and less accurate.

Alternatively, a Virtual Edge solution, a technology developed by IO River, provides visibility into traffic while enabling traffic routing management through a single interface. A Virtual Edge also simplifies the CDN configuration process by enabling IT, DevOps or network professionals to configure the CDN only once, and without having to manually configure each one. Traffic can then be safely and gradually rerouted. In case of an error, a Virtual Edge solution also enables reverting to the previous configuration easily and quickly.

5. Monitor and Optimize Performance and Availability

Continuously monitor the performance of each CDN provider and the overall M-CDN setup. Use real-time analytics and monitoring tools to detect any downtime and outages and to gather metrics like response times, latency and availability. Analyze this data to identify areas where you can improve and optimize traffic distribution for optimal performance.

6. Implement Failover and Redundancy Mechanisms

Failover and redundancy mechanisms can help ensure high availability and reliability. Define backup plans and strategies to automatically reroute traffic in case of CDN provider outages or performance issues. This helps minimize service disruptions and ensures consistent content delivery to end users. Avoid any manual failover plans - they fail to execute when you need them.

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Next Steps for IT Infrastructure, Network and DevOps Leaders

A M-CDN can be very beneficial for your organization, ensuring your traffic is always optimized and all global users get first-class citizen treatment. When determining how to transition from a single CDN vendor to a M-CDN, the first and most critical step is to define your KPIs. This will help you ensure you are en route to the desired outcome.

When you begin the transition, make sure to take it slowly and implement the new setup gradually. It is also recommended to have experienced professionals accompany you throughout the process. They will help you avoid any mistakes and ensure you and your users can benefit from the reliability and improved performance a Multi-CDN can provide.

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To learn more about IO River, the most advanced Virtual Edge solution for your Multi-CDN strategy, click here.

Optimizing CDN Architecture: Enhancing Performance and User Experience

Or Hillel — Thu, 03 Aug 2023 10:24:17 +0000

What is a CDN?

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A content delivery network (CDN) is a distributed network of servers strategically located across multiple geographical locations to deliver web content to end users more efficiently. CDNs cache content on edge servers distributed globally, reducing the distance between users and the content they want.

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CDNs use load-balancing techniques to distribute incoming traffic across multiple servers called Points of Presence (PoPs) which distribute content closer to end-users and improve overall performance.

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What is CDN Architecture?

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CDN architecture serves as a blueprint or plan that guides the distribution of CDN provider PoPs. The two fundamentals of a CDN architecture revolve around distribution and capacity.

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The distribution aspect determines how widely the PoPs are scattered and how effectively they cover different regions. Capacity determines how much content it can store in its cache and how efficiently it can serve that content to users simultaneously.

The data center's capacity is dependent on factors such as CPU, memory, bandwidth, and the number of machines.

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CDN architecture also focuses on caching, load balancing, routing, and optimizing content delivery, which can be measured by: cache offloading and round-trip time (RTT).

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RTT is the duration in milliseconds (ms) it takes for a data packet to go from a starting point to a destination and return back to the original starting point. A lower RTT indicates a faster network response time and happier end users.

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Cache offloading accurately determines the cache's ability to provide content without requiring the content from the origin.

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And if it’s one thing a customer literally doesn’t have time for, it’s downtime. Five Nines availability or 99.999%, also referred to as "the gold standard" significantly reduces downtime (5.26 minutes of annual downtime to be precise) and ensures that critical operations can continue to flow without disruption.

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All these elements combined serve as the blueprint of a CDN architecture.

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The Four Pillars of CDN Design

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CDN architecture can be broken down into several building blocks, known as the Four Pillars of CDN Design.

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Reliability

‍Reliability is the foundation of maintaining a consistent user experience. When an edge server goes down, end users in the affected region may experience an increase in latency for that specific location. This is because their requests need to be rerouted to an alternative server, which could be much farther away from the user's location.

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The CDN should be designed with propagation as it enhances content availability, mitigates the impact of server failures, minimizes latency due to traffic rerouting, and greatly assists in the recovery and resilience of the CDN.

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Performance

‍The number and distribution of PoPs play a crucial role in performance. Having more PoPs in diverse locations reduces latency by bringing content closer to end-users, and minimizing the distance data needs to travel.

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Ensure that your CDN provider has a broad and well-distributed network coverage with an extensive number of PoPs. A larger network footprint allows for content to be cached closer to end-users, reducing latency and improving performance.

Scalability‍

When planning for scalability, it’s essential to evaluate your business roadmap and identify target regions where you plan to expand. Your CDN should have edge servers strategically placed in these locations. These optimizations ensure that your CDN can handle growing user demands while maintaining fast and efficient content delivery.

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‍Responsiveness‍

Edge caching is another fundamental building block that helps enhance responsiveness. By caching frequently accessed content at edge servers within PoPs, CDNs reduce the need for content retrieval from the origin server. This minimizes response times, improves content delivery speed, and enhances overall responsiveness for your end-users.

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CDN Topology

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CDN topology describes how the network is organized and how its components are interconnected to efficiently deliver content to end-users. Here are a few examples.

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Centralized CDN

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In a centralized CDN, the emphasis is on larger PoPs strategically located in key countries/cities, while a distributed CDN aims to have a presence in several PoPs in every city to minimize the distance between content servers and end users.

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Advantages

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Since the PoPs are much larger, there is a significant increase in the cache capacity at the Edge.
Agile configuration deployment - since there are much fewer PoPs the configuration deployments are much simpler and faster
Reduced maintenance costs - the CDN is required to maintain the presence in much fewer data centers

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Disadvantages

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Higher RTT due to fewer PoPs. On average, the “edge” PoPs of a centralized CDN tend to be located farther away from the end user compared to a distributed CDN.
Inconsistencies in performance across different regions - a small amount of PoPs might create significant differences in performance for different geo-locations.

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The Distributed CDN

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In a Distributed CDN, PoPs are strategically positioned or scattered in as possibly more different regions or network locations to minimize latency and improve content delivery performance. The focus is on providing optimal physical proximity, so it’s not uncommon to see many PoPs grouped together within a small radius of each other.

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Distributed CDN is more affected by the local networks infrastructure they lease, there is a significant disparity between developing countries and developed countries.

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Advantages

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Closer physical proximity minimizes latency (RTT) - in distributed CDN the PoPs are as close as possible to end user
Faster speeds in low-connectivity areas - the impact of distributed CDN becomes even higher in low-connectivity areas, since in these areas the RTT to centralized CDN edge PoP is significantly higher compared to distributed CDN.

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Disadvantages

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Distributed PoPs create more complexity and increase maintenance costs - required the CDN to maintain present in more data centers
Deploying new configurations is more cumbersome - since the network is much more distributed, configuration updates, purges and more operations are taking more time (more locations and servers should be in sync).
Due to the aim of getting as close as possible to the end-user in a distributed architecture, Cache Management becomes an issue. Each PoP tries to keep content as 'hot' as possible, which leads to many small PoPs with relatively small coverage areas holding the same content.

In this scenario, a Cache Miss will cause the PoP to access a remote Data Center to fetch the content.

Higher cache miss on edge compared to a centralized solution - in distributed CDN the PoPs are much smaller compared to a centralized CDN and therefore the chance for cache misses on the Edge increases. Although, Cache Miss is even worse thanks to the Cache management issue mentioned above.

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In the early days of the CDN (Content Delivery Network) industry, successfully constructing a network that brought content as close as possible to the end-user was considered a significant commercial achievement. However, as the years passed, the quality of infrastructures improved significantly, reducing the advantages of Distributed CDNs compared to Centralized systems.

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CDN Architecture Optimization

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If you opt for a distributed CDN, you might want to consider utilizing cache tiers within the CDN. Cache tiers are a way to organize the caching infrastructure to improve cache hit rates and overall performance. This is where Origin Shield comes into place.

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Origin Shield is a crucial component of the cache tier architecture in a distributed CDN. It is a caching mechanism utilized in CDNs to prevent the origin server from being overwhelmed by a high volume of requests during cache misses.

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The Origin Shield acts as a buffer in the middle. It drives down the number of requests sent to the origin server to reduce cost and improves the overall efficiency of content delivery within the CDN topology.

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How to Reduce Your CDN Infrastructure Expenses

Or Hillel — Thu, 20 Jul 2023 10:24:17 +0000

Common Infrastructure Expenses

Your first step in optimizing CDN expenses isn’t to look for the best-priced solution but to remember that a cheaper price isn’t always the best deal. A low-cost CDN may not always be as reliable and high-performing as a high-cost one. Therefore, you need to make sure you have the right balance between costs and performance & reliability according to your business needs.

Here are some questions to ask yourself before choosing a CDN:

How important is the price to you? If price is your top priority, you'll need to decide how much you're willing to sacrifice in terms of reliability and performance.
What are your traffic patterns like? If your traffic is mostly static, you may be able to meet all your needs with a less expensive CDN that provides content distribution services. However, if your traffic is dynamic, you'll need a CDN that can adapt.
What features are important to you? Some CDNs offer features like security and load balancing. If these features are important to you, you'll need to factor them into your decision. For example, if you’re deploying the infrastructure for an e-commerce website, security becomes a fundamental requirement. You will probably look at CDNs that support security payment transfers, security authentications, customer PII protections, WAFs, and more.

Your CDN Bill

Typically, your CDN bill will be calculated according to two types of costs: the costs related to the delivery of traffic and the costs of special add-on services.

Delivery of Traffic - The costs related to the delivery of traffic are usually tied to your data volume and the geographical region traffic is being routed to.‍
Add-on Services - Special services can include capabilities like security features (WAF, Origin Shield), resource capping (rate limiting), image/video optimization, and more.

CDN Pricing Examples

1) Amazon Cloudfront’s data routing prices are determined according to amount of traffic and region. For example, the first 10TB to South America cost $0.11.

Source: Amazon Cloudfront Pricing

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Additional special features like Edge Compute or Origin Shield are priced separately. For example, Lambda@Edge request pricing is $0.6 per one million requests. Origin Shield costs $0.009 for 10,000 requests to Singapore.

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Source: Amazon Cloudfront Pricing

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2) Fastly’s offers a number of pricing plans. One of them is usage based pricing, which, similar to Amazon, is divided into bandwidth per geographic region and additional capabilities, like security and Edge.

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Source: Fastly Pricing Page

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Tips for Reducing CDN Infrastructure Expenses

Now that you know what your bill is made up of, let’s talk about how to reduce it. CDN infrastructure cost reduction can be achieved by taking the following steps:

1. Optimize CDN Settings to Reduce Costs

You can configure your CDN to ensure you’re not making extra unnecessary payments. For example:

Configure the Browser Cache TTL settings to ensure content is saved on the user’s browser, instead of having to deliver traffic from the origin or the CDN server in a more costly manner.
Optimize your cache key in CDN to ensure that it includes only mandatory information and avoid duplications (same object with multiple cache keys)
Segment traffic and prevent unnecessary traffic from being delivered to places it does not need to go, but you are paying for.

2. Transition to a Multi-CDN Setup

A multi-CDN strategy has multiple advantages, like ensuring network redundancy and enhanced performance. When it comes to your budget, an M-CDN strategy is the preferred option as well.

Negotiate from a Stronger Standpoint

Alternatively, working with multiple CDN vendors opens you up for negotiating with each vendor. When vendors know you have other options, they will reduce prices to ensure you choose to route your traffic through them.

Make the Most from Each CDN

Working with a few CDNs also lets you cherry-pick the routes and services you need from each vendor. This ensures you pay only for the delivery and services you need and you’re not constrained to their services bundles. Finally, when working with multiple vendors, you can choose to split the traffic across different geographies and routes, choosing the routes with the best rates, saving you money on CDN bandwidth costs.

Let’s say Vendor A charges $0.085 for the first 10TB to North America and $0.114 for Australia and New Zealand, while Vendor B charges $0.09 for the first 10TB to North America and $0.11 for Australia and New Zealand.

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If an organization uses a single vendor, they are forced to determine where the majority of their traffic is being routed to, North America or Oceania, choose the vendor that has the lower price for that destination and bite the bullet when they have to route traffic to the more expensive location.

But by working with both vendors A and B the organization can negotiate for a lower price or choose to route traffic to North America from Vendor A and to Oceania from Vendor B.

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3. Create an M-CDN Mix of Standard and Premium CDNs

Not all CDN providers are created equal. Some are standard, offering the basic services you need to ensure traffic routing and low latency, while others offer premium services like advanced security capabilities. As can be expected, premium CDN vendors also charge a premium price.

To reduce costs, you can create a multi-CDN strategy that combines both standard and premium CDNs. Then, build a strategy that determines when traffic is routed through each CDN. Any traffic that can be routed through standard CDNs, should be routed there, at a lower price.

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When should traffic be routed through a premium CDN?

When you use special services only premium CDNs provide (such as WAF)
When standard CDNs are down and you need failover.
When the standard CDN’s performance is lower than your predetermined threshold (we recommend allowing for a 5% delta)

We recommended implementing a management layer that will automatically detect any failover or performance degradation and can route your traffic to the premium CDNs.

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4. Capacity Commitment

Committing to a certain capacity for CDNs saves money by giving you a discounted rate on CDN bandwidth and storage. By committing to a certain amount for a year, the CDN vendor provides a discount. For example, Amazon CloudFront offers a Reserved CDN pricing plan that can save you up to 30% on bandwidth.

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5. Split and Separate Static and Dynamic Traffic

Static traffic is traffic that is cached close to the user and stored and served to them by the nearest server. Dynamic traffic, on the other hand, is personalized and served from the origin server. This means they need to be handled differently, in terms of security and performance.

If static and dynamic traffic are both routed the same way, organizations are incurring extra unnecessary costs. This is due to the implementation of unnecessary measures on both types of traffic. For example, static traffic does not require the same level of security that dynamic traffic does. Therefore, it’s recommended to ensure each traffic type is configured separately by using different DNS subdomains.

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Your Next Steps

High CDN costs are not set in stone. By implementing the right practices, you can significantly reduce your CDN bill and CDN infrastructure expenses. Maximize your investment by adopting a multi-CDN strategy, negotiating effectively, and optimizing traffic distribution. Following these tips will earn appreciation from your CFO.

To learn more about IO River, the most advanced Virtual Edge solution for your Multi-CDN strategy, click here.

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** Please note these examples are not exhaustive and do not represent the full pricing list by these companies

How to Monitor the Performance of Dynamic Content

Or Hillel — Thu, 13 Jul 2023 10:24:17 +0000

A Common Mistake Which Everyone does When it Comes to Measure Performance of Dynamic Content

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Monitoring the performance of your traffic is vital.

IT experts frequently rely on the performance measurements of static content, as a strong indication of Edge platform functionality.

They assume that this indication reflects also the performance of the Edge for their dynamic content.

If your online service heavily relies on dynamic traffic, you must take a closer look at it.

Trust me, you might be SURPRISED by the results.

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How to Measure your Dynamic Content's Performance

Dynamic and static traffics are SO DIFFERENT by the way they perform.

Dynamic traffic versus static traffic is making a long way and passing more hops and services on the way.

So how and what to measure (take a look at the illustration below).

Dynamic traffic starts its journey at the end-user, goes through the CDN to the origin, and travels back.

Let’s break it down into four parts:

(A) Time From End user to CDN
(B) Time inside the CDN platform
(C) Time From CDN to Data Center
(D) Time inside the Data Center itself
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How to measure each part:

(A) Measure by performance tests for cacheable content
(A+B+C+D) Measured by RUM (Real User Monitoring) or dedicated synthetic checks of dynamic traffic.
(D) By running performance testing on your infrastructure.
*(B+C) Not trivial to measure (In a second, I will show you how to calculate it)
*

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The way from the CDN to the Data Center (B + C), can be tremendously long and complex. It has a critical impact on the overall performance of dynamic traffic. This part needs to get more appropriate attention.

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With the simple formula I’ve shared below you can easily measure the performance from CDN to origin (marked as “B+C” in the illustration).

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So Why The "B+C" Part is So Critical

End-user request for dynamic content through CDN travels to the nearest CDN’s hop. Then the request travels through the CDN's global network until it reaches its origin.

Every CDN provider has a different algorithm that decided how to route the traffic to the data center. The decision-making process is further impacted by variables such as geo-location, time, network load, and the quality of the internet infrastructure.

It is a bit frustrating to not be able to pave the way for your request in your CDN provider’s network. But it is vital to acknowledge where your weakness in terms of performance.

Conclusion

I recommended running performance tests and collecting the measurements determined above*.

I would recommend analyzing B+C metrics to find opportunities to improve performance and take these metrics into consideration when you choose or evaluate the performance of your edge platform.

* Geo-location, time, network load, and the quality of the internet infrastructure and the content itself have a significant impact on the test result. Make sure you take it under your consideration