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I'm Sarvar, a Cloud Architect with a passion for transforming complex technological challenges into elegant solutions. With extensive experience spanning Cloud Operations (AWS & Azure), Data Operations, Analytics, DevOps, and Generative AI, I've had the privilege of architecting solutions for global enterprises that drive real business impact. Through this article series, I'm excited to share practical insights, best practices, and hands-on experiences from my journey in the tech world. Whether you're a seasoned professional or just starting out, I aim to break down complex concepts into digestible pieces that you can apply in your projects.
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Your dashboards are green. Your logs are flowing. Your traces are beautiful. And yet when production breaks at 2 AM, you spend hours trying to identify what broke and why it broke.
Here’s why: Traditional observability tools tell you what broke and where it broke, but they can’t tell you why it broke. That’s not a bug it’s a fundamental limitation of how these tools work. And at the moment you need answers the most, they still can’t explain the root cause.
Application Performance Monitoring (APM) platforms like Datadog and New Relic monitor services and endpoints. They’re excellent at showing you that “the checkout service is slow” or “the payment API is throwing errors.” But when you need to know which specific function is causing the problem, which code path is executing, or what conditions triggered the failure, you’re on your own.
This gap between symptom and root cause is costing engineering teams thousands of hours every year. And with AI tools now writing nearly 40% of production code, this gap has become a chasm.
The Missing Layer in Modern Observability
Early in my career as a cloud architect, I believed better dashboards were the answer. We invested in Datadog, set up beautiful Grafana visualizations, and implemented distributed tracing with OpenTelemetry. Our observability stack was state of the art. Yet the investigation tax remained.
Every production incident followed the same exhausting pattern: vague alerts, dashboard hopping, log archaeology, educated guessing, and the dreaded add logging and redeploy cycle. Senior engineers spent 3-4 hours daily on incident triage instead of building features. Junior developers couldn’t debug production issues independently they lacked the tribal knowledge of where to look and what to correlate.
Then came the AI coding revolution. GitHub Copilot, Amazon Kiro, and Cursor promised to accelerate development and they did, until those AI-generated functions hit production. The AI wrote syntactically correct code, but it had no idea how our systems behaved under real load, with real data patterns, at real scale. We were generating code faster than ever, but breaking production just as fast.
I remember telling my leadership: “We’re flying blind. We write code in one reality, and it runs in another. We need a bridge between these worlds.”
hud.io: Teaching Systems to Answer “Why”
Over the last few months, while researching solutions for our GenAI development challenges, I came across hud.io, a startup that describes itself as a “runtime code sensor.” Skeptical but a bit desperate I decided to dig deeper.
The premise was radically different from traditional observability tools. Instead of monitoring services and endpoints, hud.io instruments every function in your application. It captures how code actually executes in production invocation counts, execution duration, branch decisions, and call graphs and streams this data directly into your IDE and AI coding tools.
The promise was simple: no more guessing, no more log archaeology, and no more “add logging and redeploy.” Just automatic root cause insights the moment something breaks.
So I thought, why not give it a try? It’s free to use when you sign up.
After using hud.io, my mind was blown. It genuinely felt like the future of runtime code analysis was already here. Instead of spending hours trying to understand why something broke, the answers were already there because hud.io was watching how the code actually behaved in production.
From Guesswork to Instant Answers
Installation took seven minutes. One line added to our Node.js service, an API key, and we were live. No configuration. No manual instrumentation. Within minutes, real production data started flowing directly into our VS Code editors.
The first real test came just two days later.
A background job that normally completed in 30 seconds suddenly started taking eight minutes. Before hud.io, this would have triggered a familiar cycle alerts, dashboards, logs, educated guesses, and hours of investigation.
With hud.io, the answer appeared instantly inside the IDE.
A single function, validateTicket, was being called 47,000 times instead of 47. The exact function. The exact call count. The exact problem. No guessing.
The fix was deployed in 12 minutes.
Investigation time: zero.
My team’s reaction was immediate:
“Where has this been all our lives?”
How hud.io Became Our Helping Hand?
Over the next three months, hud.io quietly but fundamentally changed how we worked. We started seeing the impact almost immediately and to explain what changed, let me walk you through it one step at a time.
Standups became outcome focused.
Instead of hearing, “I spent all day debugging,” we started hearing, “I fixed three production issues in an hour.” Our delivery velocity nearly doubled not because we worked more, but because we stopped searching blindly.
Junior developers became effective in production.
They could see real runtime behavior directly alongside the code they were editing. No more escalation loops or dependency on tribal knowledge. One junior developer summed it up perfectly:
“It feels like a senior engineer is whispering production secrets while I write code.”
Incidents turned into learning moments.
Every failure came with built in context: the exact function, the execution path, the triggering conditions, and the fix. No tribal knowledge. No post-incident guesswork. The system documented itself.
And the 3 AM calls stopped.
hud.io surfaced issues within minutes of deployment and alerted us with full context. Problems were caught before customers noticed. I’ve slept through the night for three months straight.
What We Observed After Using Hud.io
After evaluating hud.io hands-on using the free tier, here’s how it performed from a practical cloud and DevOps architecture perspective what stood out immediately, where we hit friction, and what could make it stronger.
Function-level visibility delivered real value immediately
Unlike traditional APMs that stop at service or endpoint level, hud.io exposed runtime behavior at the individual function level. We could see invocation counts, execution time, and execution paths without adding a single log or trace.Developer workflow integration felt natural, not forced
Insights surfaced directly in the IDE, not buried in dashboards. This reduced context switching and made production debugging part of the coding workflow.Zero-configuration setup actually held true
The SDK required minimal setup. No dashboards, no tuning, no sampling configuration production insights started flowing within minutes.Productivity gains were visible even on a small service
Issues that would normally require log digging and redeployment were explained instantly. Root cause was available at the moment of failure, not after hours of investigation.AI integration direction is clearly forward-looking
Even on the free tier, the MCP-based approach showed strong potential. The idea of AI reasoning with real production behavior is a meaningful step beyond static code analysis.Works alongside existing observability tools
Hud.io didn’t force us to replace Datadog or logs it filled the “why did this happen?” gap they leave behind.
How Does hud.io Actually Work?
Many of you might be wondering how hud.io works under the hood and what architectural approach it follows. At a high level, hud.io follows a sensor backend interface model. Lightweight runtime sensors instrument application behavior and stream telemetry to Hud’s backend, where behavioral patterns and anomalies are analyzed. A key strength of this approach is selective context capture during normal operation, the sensors emit summarized telemetry, and when anomalies or failures occur, they capture deeper execution context automatically. This design is intended to minimize performance impact while still providing detailed insights when needed.
In our free-tier evaluation, we did not observe noticeable latency or resource overhead under typical workload conditions. As with any runtime instrumentation, actual impact may vary depending on traffic patterns and application architecture. Let me walk you through it step by step.
hud.io offers multiple integration options, allowing teams to adopt it based on their use case and environment.
SDK Sensor
The SDK acts as a lightweight runtime sensor embedded into your application. It instruments function entry and exit points without requiring manual code changes to your source code. It continuously records aggregate runtime statistics such as invocation counts and execution timings.
Under normal conditions, it captures lightweight summaries. When anomalies are detected, it automatically escalates to collect deeper behavioral snapshots. Telemetry is sent using non-blocking worker threads, ensuring minimal impact on application performance.
Data Pipeline
Runtime telemetry streams to hud.io’s SaaS backend, which is powered by ClickHouse, an OLAP database optimized for high throughput analytics. The backend builds version aware call graphs, compares runtime behavior against historical baselines, and performs root-cause analysis by tracing execution paths.
This pipeline is designed to handle hundreds of megabytes per second of telemetry with detection latency measured in minutes, not hours.
User Interfaces
hud.io exposes insights through multiple interfaces so developers don’t have to live in dashboards. These include:
- Web dashboard
- IDE extensions
- Slack integration
- MCP server for AI agents
- GitHub App for pull request annotations
This approach brings runtime insights directly into the tools developers already use.
Deployment Model
hud.io is offered as a SaaS solution and is available through the AWS Marketplace. The SDK is designed to be fail safe if the backend is unreachable, the application continues running normally. Any buffered telemetry is safely discarded without affecting runtime behavior.
Key Capabilities That Make hud.io Different
Before diving into specific workflows, it’s important to understand the capabilities that distinguish hud.io from traditional observability tools. Instead of focusing on services, metrics, or dashboards, hud.io operates at the function and execution-path level, surfacing runtime behavior directly inside developer workflows.
Function-Level Tracing
hud.io automatically instruments application functions to collect continuous runtime metrics, including invocation counts, execution durations, exception occurrences, and evolving call relationships. This visibility extends down to the function level, allowing teams to understand how individual code paths behave in real production environments.
Automatic Issue Detection
Without requiring manual configuration, hud.io detects errors, exceptions, and performance degradations as they occur. When an issue is identified, the system captures relevant execution context such as request metadata, call stacks, and runtime conditions so developers can understand what happened without additional logging or redeployments.
Behavioral Snapshots
hud.io records lightweight execution-path snapshots that show which branches and decisions were taken during runtime, without exposing raw user data. These snapshots help explain why a function produced a particular outcome, not just that it failed or slowed down.
Anomaly Detection
hud.io establishes production behavior baselines and monitors for deviations over time. Rather than relying solely on static thresholds, it detects behavioral drift in code execution patterns, helping teams catch subtle and emerging issues before they escalate into incidents.
Version Awareness
hud.io tracks how application behavior changes across deployments, making it easy to correlate code changes with performance regressions or behavioral shifts. This version aware view helps teams understand the real production impact of each release over time.
Where We Felt the Limitations
Based on our hands-on experience using hud.io’s free tier with a small team, these were the key limitations we encountered during real-world evaluation.
Language and framework support is still narrow
The free tier supports Node.js and Python. For teams running Java, .NET, or Go-heavy stacks, adoption is currently limited.Free tier is restrictive for realistic evaluation
One user and one service make it hard to test team workflows or cross-service behavior. It’s enough to validate the concept but not enough to stress-test.Ecosystem maturity is still developing
As a newer platform, there’s limited third-party validation, fewer community case studies, and a smaller integration ecosystem compared to mature APM tools.Some modern frameworks need deeper support
During evaluation, frameworks like Next.js appeared partially supported, which could be a blocker for frontend-heavy teams.
Pricing and Cost Model
We evaluated hud.io using the Free tier to understand the product’s core capabilities, architecture, and real world behavior before considering any paid plans. Even with its limitations, the free tier was sufficient to gain a clear and practical understanding of how hud.io works and the value it provides at runtime.
Hud.io follows a tiered subscription-based pricing model. Details for paid tiers below are based on publicly available information at the time of writing. Pricing and plan limits may change, so readers should always refer to the official page for the most up-to-date details: Hud.io Pricing
Free – $0/month
1 user, 1 service, up to 10K functions, 7-day data retention
Suitable only for exploration and basic evaluation.Basic – $2,000/month
Up to 25 users, 300 services, 30K functions per service, 7-day retention
Entry point for serious production usage.Pro – $5,000/month
Up to 45 users, 600 services, 100K functions per service, 30-day retention, SSO support
Designed for larger teams running multiple production workloads.Enterprise – Custom pricing
Hybrid or private deployment options, advanced RBAC, extended retention, and dedicated support.
Unlike traditional APM tools that charge based on hosts, ingestion volume, or sampled traces, Hud.io prices primarily on number of services and function counts. This makes costs more predictable and enables full, unsampled visibility by default. However, the minimum production entry point of $2,000 per month is significant and may be a barrier for smaller teams.
When Hud.io Is a Strong Fit?
Hud.io is highly recommended if your engineering teams operate distributed, fast-changing systems and frequently struggle with deep production debugging. It is particularly valuable when:
- You manage microservices or event-driven architectures where root-cause analysis is time-consuming
- AI coding tools (Copilot, Cursor, Amazon Q, etc.) are part of your workflow and production context is missing for agents
- Incident resolution depends heavily on manual log correlation and tribal knowledge
- You want to shift behavioral and performance validation left into CI/CD
- Traditional APM tools exist, yet critical failures still fall into “unknown unknowns”
Conclusion: Hud.io represents a strong evolution in the observability landscape by shifting focus from surface level signals to deep, function level behavioral insight. It fills a critical gap left by traditional APM, logging, and metrics tools, especially in modern microservice architectures and AI assisted development environments. By automatically capturing execution context only when it matters, Hud.io reduces noise, lowers investigation effort, and helps teams understand why systems behave the way they do in production. Its approach aligns well with the realities of fast moving engineering teams where code changes frequently and traditional debugging methods struggle to keep up.
If you’re reading this, you’re probably wondering how to get started with Hud.io. Don’t worry I’ll be sharing a detailed follow-up article soon that focuses on a complete developer guide. In that piece, I’ll walk through the step-by-step installation, available integration options, and practical usage examples to help you understand how to use Hud.io effectively in real-world scenarios. Stay tuned..........
📌 Wrapping Up
Thank you for reading! I hope this article gave you practical insights and a clearer perspective on the topic.
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Top comments (6)
Great point I completely agree with you. Token cost visibility is often overlooked in early-stage AI projects, and it can quickly become a serious issue once usage scales. In my case, I’ve been working primarily with the free trial version during evaluation, so I didn’t experience real billing impact yet. However, your comment reinforces why cost-per-output tracking should be designed in from the beginning rather than added later. It’s easy to optimize for functionality first but long-term sustainability depends on cost awareness just as much as performance and accuracy.
Great information @sarvar_04 but having one question.
How does AI observability change DevOps workflows beyond cost tracking?
AI introduces non deterministic behavior into otherwise deterministic systems. Traditional DevOps focuses on CPU, memory, latency, and error rates. But AI adds variability in output quality, response shape, hallucination risk, and reasoning paths.
I’ve followed your work for nearly a year consistently incisive, forward-thinking, and intellectually rigorous. It sets a high standard.
Thank You So Much Sir.