DEV Community: Pratik Pathak

The Real Difference Between Azure OpenAI and the Standard API

Pratik Pathak — Fri, 24 Apr 2026 03:30:00 +0000

Azure OpenAI Service is increasingly becoming a critical decision point for enterprise teams. Artificial Intelligence has come a long way, and today, tools like ChatGPT, GPT-4, and DALL-E are helping developers, students, and businesses every day. But here’s a common question I hear people ask: “What’s the difference between OpenAI and Azure OpenAI?” If you’ve ever wondered which one to use, or if the Azure wrapper is worth the cloud overhead, let’s break it down.

I decided to dig deep into the architectural differences to see how much of a technical edge Azure OpenAI actually gives over just hitting the standard OpenAI API. Spoiler alert: OpenAI gives you the model, but Azure OpenAI gives you the model plus an entire enterprise cloud ecosystem.

Core Architectural Differences

At first glance, hitting the direct OpenAI API feels identical to the Azure endpoint. You pass your payload, and you get your tokens back. However, the infrastructure layer is entirely different.

OpenAI (via OpenAI.com or their direct API) hosts its models on its own proprietary compute instances. It’s built for rapid iteration and developer access. Azure OpenAI, on the other hand, runs the exact same foundational models (GPT-4o, DALL-E 3, Whisper) but hosts them entirely within your Microsoft Azure tenant boundary.

The models themselves are mathematically identical. The difference lies entirely in the infrastructure, data residency, and compliance wrapper.

Network Isolation & Security

This is usually the dealbreaker for enterprise deployments. With the direct OpenAI API, your data travels over the public internet to OpenAI’s servers. While they have strict privacy policies (API data isn’t used for training by default), the network path is public.

Azure OpenAI allows you to use Azure Virtual Networks (VNet) and Azure Private Link. This means your application can communicate with the AI models entirely within the Microsoft backbone network. Your traffic never hits the public internet. If you want to dive deeper into the official setup, you can read more in the official Microsoft documentation. Let’s look at how a basic Python integration looks when hitting an Azure endpoint.

import os
from openai import AzureOpenAI

client = AzureOpenAI(
    api_key=os.getenv("AZURE_OPENAI_API_KEY"),  
    api_version="2026-04-01-preview",
    azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT")
)

response = client.chat.completions.create(
    model="gpt-4o-deployment", # Notice this is a custom deployment name, not just the model name
    messages=[
        {"role": "system", "content": "You are a technical assistant."},
        {"role": "user", "content": "Explain VNet integration."}
    ]
)

print(response.choices[0].message.content)

Data Residency and Compliance

Why did I decide to prioritize Azure for production workloads? Simply put: data residency. When you deploy an instance of Azure OpenAI, you select a specific geographic region (e.g., East US, West Europe). All prompts, completions, and fine-tuning data are stored within that specific region.

Direct OpenAI doesn’t give you this granular geographical control. Furthermore, Azure OpenAI inherits all of Microsoft’s compliance certifications, including HIPAA, SOC 2, and ISO 27001. If you’re building in healthcare or finance, this isn’t just a nice-to-have; it’s a hard requirement.

Identity and Access Management (IAM)

OpenAI uses standard API keys. If a key leaks, anyone can use it until it’s revoked. Azure OpenAI natively integrates with Microsoft Entra ID (formerly Azure AD). This allows for Role-Based Access Control (RBAC).

Instead of hardcoding API keys, your application can authenticate to Azure OpenAI using Managed Identities, eliminating the risk of leaked credentials entirely.

Here is what authenticating via Azure DefaultAzureCredential looks like:

from azure.identity import DefaultAzureCredential
from openai import AzureOpenAI

credential = DefaultAzureCredential()
token = credential.get_token("https://cognitiveservices.azure.com/.default")

client = AzureOpenAI(
    azure_endpoint="https://my-custom-endpoint.openai.azure.com/",
    azure_ad_token=token.token,
    api_version="2026-04-01-preview"
)

Content Filtering and Responsible AI

Another massive difference is the Azure AI Content Safety layer. While OpenAI has baseline moderation, Azure OpenAI lets you create custom content filters. You can configure the exact severity thresholds (Low, Medium, High) for categories like hate speech, sexual content, violence, and self-harm. You can even create custom blocklists for specific industry terms.

Pros, Cons, and Trade-offs

Azure OpenAI Service
OpenAI Direct API
Pros: Enterprise security (VNet, Private Link), strict data residency, Managed Identities via Entra ID, customizable content filtering, backed by Azure SLA.
Cons: Can be slightly slower to receive the absolute newest model versions from OpenAI. Requires navigating the complex Azure portal.
Pros: Immediate access to the latest models on day one. Extremely simple to set up and start coding. Lower barrier to entry for solo developers.
Cons: Lacks enterprise VNet isolation. Less granular control over geographic data residency. API keys are harder to secure securely at scale.

Final Thoughts

For side projects, hackathons, or general scripting, I’ll still reach for the direct OpenAI API. It’s frictionless. But if I’m building an AI agent that touches PII, requires strict compliance, or lives inside a corporate network, Azure OpenAI Service is the only logical choice. You get the brilliance of GPT-4o with the fortress of Microsoft Azure.

I run Code AI Locally, fully offline and Pay 0$ on subscription

Pratik Pathak — Thu, 23 Apr 2026 06:25:08 +0000

I was working on a sensitive client architecture last week, sitting in a coffee shop with spotty Wi-Fi, when my IDE suddenly crawled to a halt. My cloud-based AI coding assistant could not connect to its API. It was in that frustrating moment that I realized relying entirely on cloud-hosted LLMs for daily engineering tasks is a single point of failure. Why are we sending every keystroke, every proprietary function, and every sensitive database schema over the internet when modern laptops have enough compute to run these models natively?

That is when I decided to fully explore the world of offline code AI. The ecosystem has matured incredibly fast in 2026. You no longer need a massive GPU server rack to run a competent coding assistant locally. If you have an Apple Silicon Mac (M1/M2/M3/M4) or a Windows machine with a decent dedicated GPU, you can run powerful code generation models directly on your hardware, completely offline, with zero latency and zero subscription fees.

Let’s figure out how to set this up together, exploring the best tools, models, and configurations to replace cloud-dependent assistants.

Why You Need Offline Code AI in 2026

Beyond the obvious benefit of working on an airplane or during an internet outage, there are three massive reasons why engineering teams are shifting toward local LLMs:

Data Privacy and Security: When you work with healthcare data, financial systems, or highly confidential proprietary code, sending context to a third-party API is a massive compliance risk. Offline AI guarantees your code never leaves your machine.
Zero API Costs: Cloud models charge per token. If your IDE assistant is constantly indexing your workspace and sending context windows to the cloud, the bill adds up quickly. Local models are free forever.
Customization: You can fine-tune or swap out models instantly based on the specific language you are writing. You can run a specialized Rust model one minute, and a Python-optimized model the next.

If you are working in an enterprise environment, many CISOs are now actively blocking cloud-based code assistants. Getting comfortable with offline code AI is becoming a mandatory engineering skill.

The Stack: Ollama and Continue.dev

There are many ways to run local models, but the absolute best developer experience right now is the combination of Ollama (for model hosting) and Continue.dev (for IDE integration).

Downloads & Tools Needed

To get your offline code AI stack running, you’ll need to download these free, open-source tools:

Ollama: The local model runner and API backend. Download it at ollama.com.
Continue.dev: The IDE extension (VS Code or JetBrains) that connects your editor to Ollama. Download the extension at continue.dev or directly from your IDE’s marketplace.

1. Setting up the Local API with Ollama

Ollama is a lightweight tool that allows you to run open-source LLMs locally. It acts as the backend server. Download and install it, then open your terminal to pull a coding-specific model. For general coding tasks, I highly recommend downloading the DeepSeek Coder model or CodeLlama.

# Pull and run the DeepSeek Coder model locally
ollama run deepseek-coder

# Alternatively, if you have more RAM (16GB+), run the larger 7b version
ollama run deepseek-coder:7b

Once the model is downloaded, Ollama exposes a local API (usually on port 11434) that your IDE can talk to. Your machine is now officially an AI server.

2. Bridging the Gap with Continue.dev

Continue.dev is an open-source extension for VS Code and JetBrains that brings the “Copilot” experience to your local models. Instead of hardcoding the assistant to a cloud provider, you can configure it to talk to your local Ollama instance.

After installing the extension, you simply open the config.json file for Continue and point it to your local environment:

{
  "models": [
    {
      "title": "DeepSeek Coder (Local)",
      "provider": "ollama",
      "model": "deepseek-coder",
      "apiBase": "http://localhost:11434"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Starcoder 2 (Autocomplete)",
    "provider": "ollama",
    "model": "starcoder2:3b",
    "apiBase": "http://localhost:11434"
  }
}

Notice how we configured two different models! We use a larger model (DeepSeek) for the chat interface where we ask complex questions, and a much smaller, faster model (Starcoder2 3B) for real-time tab autocomplete. This is the secret to a snappy offline experience.

Top Local Models for Offline Code AI

The beauty of this architecture is that you can swap out the “brain” of your assistant whenever a new model drops. Here is what I am running locally right now:

DeepSeek Coder V2: Unbelievably good at Python, JavaScript, and C++. It punches way above its weight class and handles complex logic refactoring beautifully.
Starcoder 2 (3B): The absolute king of low-latency autocomplete. If you want your code completions to feel instantaneous on a laptop, this is the model you run in the background.
Llama 3 (8B): While not strictly a coding model, the base Llama 3 model is fantastic for generating documentation, writing commit messages, and explaining abstract architectural concepts offline.

The Trade-offs: Hardware Constraints

I have to be honest here. Running offline code AI is not pure magic – it is bound by the laws of physics and RAM. If you are running a 5-year-old laptop with 8GB of memory, your experience is going to be painful.

To run a 7B or 8B parameter model comfortably while also running Docker, VS Code, and a browser, you really need 16GB of Unified Memory (like an M-series Mac) or a dedicated Nvidia GPU with at least 8GB of VRAM. If your hardware is constrained, you can still participate! Just download smaller, highly quantized models (like 1.5B parameter models) which can run on almost anything.

Final Thoughts

Why did I decide to fully transition my workflow? Because having a coding assistant that works at 35,000 feet, never exposes my client’s proprietary algorithms, and costs zero dollars a month is an absolute superpower. It forces you to understand how these models actually work under the hood, rather than just treating them as magic black boxes provided by massive tech monopolies.

If you haven’t tried running an offline code AI stack yet, take 15 minutes today, install Ollama and Continue, and pull a local model. You will be shocked at how capable your local hardware actually is.

LangGraph vs Azure AI Agents: Orchestration Frameworks Compared

Pratik Pathak — Wed, 22 Apr 2026 04:30:00 +0000

I was sitting in a design review last week, staring at a whiteboard covered in multi-agent workflows, and a terrifying thought crossed my mind: how on earth are we going to orchestrate all of this reliably in production? We developers get so obsessed with crafting the perfect prompts and tool use that we often forget about the underlying framework. Orchestrating multi-agent workflows is rapidly becoming the new frontier in AI development. As applications evolve from simple chat interfaces to complex, autonomous agents that can plan, execute, and collaborate, the framework you choose becomes your most critical architectural decision.

Two powerful contenders have emerged at the forefront of this space: LangGraph (by LangChain) and Azure AI Agents. Both offer robust solutions for building stateful, multi-agent applications, but they take fundamentally different approaches to architecture, deployment, and developer experience. Let’s figure out which one makes sense for your next enterprise build.

What is LangGraph?

LangGraph is an open-source library built on top of LangChain, designed specifically for creating stateful, multi-actor applications with LLMs. At its core, LangGraph models agent workflows as graphs. Nodes represent agents or functions, and edges represent the flow of data or control between them.

The Developer’s Playground

If you can write it in Python or TypeScript, you can model it in LangGraph. You have absolute control over the execution flow, state transitions, and tool integrations. Unlike standard Directed Acyclic Graphs (DAGs), LangGraph natively supports cyclic workflows. This is absolutely essential for agents that need to reflect, self-correct, or retry actions until a condition is met. Why did I decide to use LangGraph for a recent open-source project? Because it gave me granular control over the state checkpointing system, allowing me to pause, resume, or “time travel” through agent states.

Being part of the LangChain ecosystem means immediate access to thousands of community tools, document loaders, and vector store integrations out of the box.

What are Azure AI Agents?

Azure AI Agents (formerly part of the Azure OpenAI Assistant API features) represents Microsoft’s enterprise-grade, managed approach to building intelligent applications. It abstracts away much of the infrastructure complexity required to run multi-agent systems securely at scale.

The Managed Enterprise Engine

With Azure AI Agents, there is no need to provision custom state stores or handle checkpointing databases manually. Azure manages the underlying compute and state persistence, often backed securely by Cosmos DB or Azure Storage. The biggest selling point for me? Out-of-the-box compliance with enterprise standards, including Entra ID (Azure AD B2C) integration, private endpoints, and data residency guarantees.

It also features seamless Azure ecosystem integration. You get native connectivity to Azure OpenAI models, Azure AI Search for RAG pipelines, and Azure Monitor for telemetry without writing extensive glue code. The built-in threading simplifies conversational state management by providing managed threads, completely removing the headache of manual context window management.

Head-to-Head Architectural Comparison

Let’s look at how these two frameworks stack up across the most critical dimensions for engineering teams.

1. Developer Experience and Control

LangGraph is a developer’s playground. You define the exact state schema, write the reducer functions, and wire up the nodes manually. This gives you granular control but comes with a steeper learning curve and more boilerplate code.

from langgraph.graph import StateGraph, END
from typing import TypedDict

class AgentState(TypedDict):
    messages: list

workflow = StateGraph(AgentState)
workflow.add_node("agent", run_agent_model)
workflow.add_node("action", execute_tool)
workflow.set_entry_point("agent")
workflow.add_conditional_edges("agent", should_continue)
app = workflow.compile()

Azure AI Agents abstracts the graph away. You define instructions, equip the agent with tools (like Code Interpreter or Retrieval), and let the managed API handle the orchestration. It’s faster to market but less customizable if you need a highly specific, non-standard routing logic.

2. State Management and Memory

In LangGraph, state is a first-class citizen. You can use SQLite locally or PostgreSQL in production via LangGraph Cloud or custom deployments. You can easily inject human-in-the-loop steps to approve actions before they execute.

Azure AI Agents handles state opaquely via its managed Threads API. While incredibly convenient, you have less visibility into the raw state object at intermediate steps compared to LangGraph’s transparent checkpointing. However, for most conversational and task-oriented workflows, Azure’s managed memory is more than sufficient and entirely maintenance-free.

If you are dealing with strict compliance regulations that require you to audit every intermediate thought process of the LLM, LangGraph’s transparent state database might be legally required over Azure’s managed opaque threads.

3. Deployment and Scalability

Deploying a LangGraph application into production requires setting up your own API layer (e.g., FastAPI), managing a state database, and handling worker scaling. Though LangSmith and LangGraph Cloud are changing this, it’s still a separate platform-as-a-service to manage.

Azure AI Agents is essentially serverless. You call the API, and Microsoft scales the underlying infrastructure. If your organization is already embedded in the Azure cloud, deploying Azure AI Agents is a natural extension of your existing architecture.

The Verdict: Which Should You Choose?

Choose LangGraph
Choose Azure AI Agents
You are building highly custom, complex cognitive architectures (e.g., hierarchical agent teams with non-standard reflection loops).
You want zero vendor lock-in and prefer open-source Python or TypeScript solutions.
You need deep, programmatic control over every step of the agent’s thought process.
You are building enterprise applications where security, compliance, and data privacy are non-negotiable.
You want to ship to production quickly without managing state databases or underlying compute infrastructure.
Your tech stack is already heavily invested in Azure (Azure OpenAI, Cosmos DB, Entra ID).

Conclusion

Both LangGraph and Azure AI Agents are powerful tools, but they cater to different philosophies. LangGraph gives you the steering wheel, the engine, and the raw parts to build your own custom vehicle. Azure AI Agents gives you a managed, enterprise-ready fleet that gets you to your destination safely and securely. The best choice depends entirely on your team’s expertise, timeline, and security constraints. I’ve found myself using LangGraph for rapid prototyping and Azure AI Agents for production systems that handle PII. Let’s keep building and experimenting.

Related Reading: For more on architectural decisions in AI, check out my thoughts on Managing State in Multi-Agent Workflows and how to handle Silent Failures in Production AI Agents.

I saved up 80% Azure OpenAi cost optimization by making these 7 architectural decision

Pratik Pathak — Tue, 21 Apr 2026 04:30:00 +0000

Azure OpenAI cost optimization becomes a real concern not during experimentation, but after your system goes live.

A fintech team running ~50,000 daily queries saw their monthly bill jump from $3,000 to $28,000 in six weeks-with no new features shipped.

Nothing obvious broke.

Latency stayed stable. Outputs looked fine. But under the hood, retries increased, prompts grew longer, and multi-step workflows quietly multiplied token usage.

This is where azure-openai-cost-optimization shifts from a pricing problem to an architectural one.

Decision 1: Single-Call Simplicity vs Multi-Step Expansion

The fastest way to increase cost is to increase the number of model calls per request.

A simple system:

User Input → LLM → Response

A production system often becomes:

User Input → Planner → Tool → Re-ask → Summarize → Final Response

One request can easily turn into 5-10 model calls.

Each additional step introduces:

More tokens
More latency
More failure points

The key issue is not just cost-it’s unbounded execution.

Multi-step workflows make sense when the problem genuinely requires decomposition-autonomous agents, tool orchestration, or complex reasoning chains. But for most use cases, a well-structured prompt with clear instructions can achieve the same outcome in a single call, with far lower cost and complexity.

A customer support classifier, for instance, doesn’t need a planner-a single prompt with few-shot examples handles intent detection reliably. Reserve orchestration for tasks where intermediate tool results actually change the next step.

Decision 2: Model Selection – Capability vs Cost Efficiency

Model choice has a direct and often underestimated cost impact.

Many teams default to a high-capability model for all requests, even when unnecessary.

Real Pricing Difference (Illustrative)

GPT-4o → higher reasoning capability, higher cost
GPT-4o-mini → significantly cheaper, lower latency

In practice, you should also review Microsoft’s official Azure OpenAI pricing to understand model cost differences.

GPT-4o-mini can be 5-10× cheaper per token than GPT-4o
For classification, routing, or formatting tasks, the quality difference is often negligible

Practical Routing Pattern

Instead of sending everything to a large model:

Use a lightweight model to classify intent
Route only complex tasks to a higher-capability model

if task == "classification":
    use gpt-4o-mini
else:
    use gpt-4o

In high-traffic systems, even shifting 30-40% of requests to smaller models can significantly reduce total cost while improving latency.

Decision 3: Token Budgeting – Input Size Is the Hidden Multiplier

Most cost does not come from output tokens. It comes from input size.

Common production issues:

Sending full conversation history every time
Including irrelevant system prompts
Passing entire documents instead of filtered chunks

Practical Optimization Techniques

Trim conversation windows (last N turns only)
Use embeddings to retrieve relevant context
Summarize long histories before reuse

Instead of passing a full document, embed it into a vector store and retrieve only the top 2-3 relevant chunks at query time-often under 500 tokens total. This reduces input size without sacrificing answer quality.

Example Impact

Instead of:

5,000 tokens per request

Reduce to:

1,000 tokens

At scale, this can translate into a 60-80% reduction in token-related cost for that workflow.

Decision 4: Caching – Avoid Paying Twice for the Same Work

A surprising amount of LLM traffic is repetitive.

Without caching, you pay for the same computation repeatedly.

Two Types of Caching

1. Exact Match Caching

Same input → same output
Simple and fast

2. Semantic Caching

Similar inputs → reused responses
Uses embeddings to detect similarity

For example:

“What is my refund status?”
“Can you check my refund?”

These queries can map to the same cached response.

Azure Implementation

Azure Cache for Redis for low-latency storage
Embedding similarity search for semantic matching

cache_key = hash(user_input + context)

Caching reduces repeated model calls without affecting output quality. The main tradeoff is maintaining cache freshness, especially when underlying data changes.

Decision 5: Retry and Loop Control – The Silent Cost Multiplier

Retries are necessary in distributed systems-but dangerous in LLM workflows, especially when dealing with Azure OpenAI Rate Limits Guide.

Real Scenario

API returns error
System retries
Model re-plans
Same failure repeats

1 request → 3 retries → 4× cost

Common Causes

429 rate limit errors
Transient API failures
Unbounded agent loops

Example: Exponential Backoff

import time

for attempt in range(3):
    try:
        response = call_llm()
        break
    except Exception:
        time.sleep(2 ** attempt)

Control Mechanisms

Max retry limits
Exponential backoff
Failure classification (retry vs stop)

For agent-based systems, also add a hard step limit-if the agent hasn’t resolved the task within N iterations, surface a fallback response rather than continuing indefinitely.

Without explicit controls, retries silently multiply both cost and latency.

Decision 6: Observability – You Can’t Optimize What You Can’t See

Most teams track total cost.

That’s not enough.

You need visibility into:

Cost per request
Tokens per feature
Model usage distribution
Retry frequency

Minimal Trace Example

trace = {
    "feature": "support_agent",
    "model": "gpt-4o",
    "tokens_input": 1200,
    "tokens_output": 300,
    "cost": 0.02
}

Azure Implementation

Application Insights for logging
Custom dashboards for aggregation

Set cost alert thresholds in Azure Cost Management to notify your team when daily or hourly spend exceeds a defined limit. This helps catch runaway loops before they become expensive surprises.

Decision 7: System Design – Cost as a First-Class Constraint

Cost should not be optimized after deployment. It should shape architecture from the start.

Concrete Example

Assume:

Avg request = $0.02
Daily requests = 50,000

Daily cost = $1,000  
Monthly ≈ $30,000

Now apply:

30% token reduction
20% cache hit rate

New daily cost ≈ $560

Compounding Effect

Small improvements at each layer:

Model routing
Token trimming
Caching
Retry control

Together can reduce cost by 40-70%.

A system that costs $30,000/month at launch can realistically operate at $10,000-$18,000 with these controls in place-not through a single optimization, but through compounding small decisions across every layer.

When Azure OpenAI Cost Optimization Matters Most

Focus on optimization when:

Traffic is scaling – small inefficiencies multiply quickly at volume
Multi-step workflows are introduced – each layer increases call depth
Costs are unpredictable – a sign of uncontrolled execution paths
Multiple teams share infrastructure – shared systems amplify waste

Avoid over-optimizing when:

You are still experimenting – premature optimization slows iteration
Usage is low – cost signals are not yet meaningful
System behavior is unstable – fix correctness before efficiency

Final Thoughts

Azure OpenAI cost optimization is not about reducing tokens in isolation.

It is about controlling system behavior:

How often models are called
How much context is passed
How retries are handled
How work is reused

The tradeoff is clear:

You can build flexible systems that do everything…

or controlled systems that do only what is necessary.

The systems that scale sustainably are not the ones that generate the most intelligence.

They are the ones that generate it efficiently.

FAQ

What is the biggest cost driver in Azure OpenAI systems?

The number of model calls per request. Multi-step workflows and retries can multiply costs quickly.

How can I reduce token usage effectively?

Trim conversation history, retrieve only relevant data using embeddings, and summarize long inputs before sending them to the model.

Should I always use the most advanced model?

No. Use smaller models for simple tasks and reserve advanced models for complex reasoning.

How does semantic caching reduce cost?

Semantic caching reuses responses for similar queries using embeddings, reducing repeated model calls even when inputs are not identical.

Why do retries increase cost so much?

Each retry often triggers a full model call. Without limits, retries multiply both token usage and API costs.

When should I start optimizing costs?

Once your system reaches production scale or costs become unpredictable, optimization should be treated as a core architectural concern.

What is the difference between exact match and semantic caching?

Exact match requires identical inputs. Semantic caching uses embedding similarity to reuse responses for queries that are phrased differently but mean the same thing-making it far more effective in real user traffic.

Do you know Gemini Chrome Skills? A single line makes browser your AI Agent.

Pratik Pathak — Sun, 19 Apr 2026 04:30:00 +0000

If you want to know how to master Gemini Chrome skills , your life is about to get a lot easier. Google recently started rolling out ‘Skills’ for Gemini directly inside the Chrome browser. This update effectively turns Chrome into a lightweight, personalized AI agent that remembers your favorite workflows and can run them across multiple tabs simultaneously.

Why does this matter? Instead of treating AI as a basic chatbot, Skills allow you to build repeatable, customized processes for tasks like summarizing long documents, comparing products side-by-side, or analyzing recipes. Let’s break down exactly how to create, use, and master Gemini Chrome Skills.

What Are Gemini Chrome Skills?

At its core, a Skill is simply a saved prompt. Whether it is a highly specific set of instructions for analyzing the ingredients of a skincare product or a prompt to extract action items from a meeting transcript, you can save that exact command to your Chrome profile.

Instead of manually writing it out every time, you can trigger a saved Skill by typing a forward slash (/) or clicking the plus (+) button in your Gemini chat history. Your saved Skills sync across all desktop versions of Chrome (Mac, Windows, ChromeOS) where you are signed in with your Google account.

Note: The feature began rolling out in mid-April 2026. Initially, your Chrome browser language must be set to US English to access the Skills interface.

How to Create Your Own Custom Skill

Creating a custom workflow is incredibly intuitive. Here is the step-by-step process:

Open the Gemini side panel in Google Chrome.
Browse to a webpage you want to analyze (for example, a recipe blog).
Type your complex prompt. For instance: ‘Analyze this recipe, identify all ingredients, and suggest high-protein substitutions.’
Once Gemini answers, look for the option to save that exact prompt as a Skill from your chat history.
Give it a memorable name.

The next time you visit a completely different recipe site, you do not need to retype anything. You just trigger your newly created Skill, and Gemini runs the exact same analysis on the new page.

The Magic of Multi-Tab Analysis

The most powerful feature of Gemini Chrome skills is its ability to operate across multiple tabs at the same time. This fundamentally changes how you do research.

Imagine you are shopping for a new laptop or researching skincare products. You can open three different product pages in three separate tabs. By selecting those tabs and triggering a ‘Product Comparison’ Skill, Gemini will pull data from all three pages simultaneously. It will generate a clean, side-by-side comparison factoring in price points, specs, and user reviews without you ever having to copy and paste text between tabs.

Pro Tip: Multi-tab Skills work beautifully with Google Drive. You can open a recipe in one tab and your personal grocery list in Google Docs in another, then run a Meal Planner Skill to cross-reference and update your list automatically.

The Pre-Built Skills Library

If you don’t want to build prompts from scratch, Google included a built-in Skills Library. You can browse ready-made workflows for common tasks and add them to your profile with a single click. Every pre-built Skill is fully editable, so you can tweak the underlying prompt to match your exact preferences.

Some of the top pre-built Skills include:

Gift Concierge: A smart product comparison tool designed for multi-tab shopping.

Ingredient Decoder: Instantly breaks down complex ingredient lists on health or beauty pages, explaining what each component does and highlighting allergens.

Calendar Creator: Scans a webpage for event details and formats them for your schedule.

Meal Planner: Analyzes recipes and helps build weekly plans and shopping lists.

Privacy and Security Measures

Giving an AI agent the ability to run automated workflows across your browser raises valid security questions. Google built confirmation gates into the system to handle this. If a Skill attempts to perform a high-impact action like sending an email or creating an event on your calendar, the system halts and asks for your explicit manual approval before executing the task.

Final Thoughts

Why did I decide to start using these immediately? Because they eliminate the friction of modern AI. We spend too much time engineering the perfect prompt over and over again. By saving these as executable Skills, Gemini transforms Chrome from a simple web viewer into a personalized research assistant.

Give it a try today, and let’s figure out the most creative ways to automate our daily browsing habits together! For more technical updates on AI and Chrome, you can always check out the official Google Chrome Blog.

Related Reading: For a deep dive into extending AI agent capabilities natively inside your IDE instead of Chrome, read my guide on VS Code Extensions (VSIX) Offline Downloads. If you want to compress costs across your entire generative AI tech stack, check out How We Cut Azure OpenAI Costs by 40%.

Top 25+ Advanced DSA Projects in C++ with Source Code

Pratik Pathak — Sat, 18 Apr 2026 14:42:03 +0000

When you are serious about mastering Data Structures and Algorithms (DSA), building a high-complexity DSA project in C++ is the ultimate test. I wanted to put together a definitive list of advanced C++ projects that don’t just use basic arrays, but actually engineer optimal time and space complexities with professional patterns. Let’s figure this out together.

Why did I decide to compile this? Because most ‘beginner’ projects don’t teach you how to handle dynamic rehashing, memory coalescing, or thread-safe state. If we really want to get better at C++, we need to build systems that scale. Every project in this collection has been engineered to showcase high-fidelity logic and optimal complexities.

Top 25 Advanced DSA Projects in C++

1. Student Records System

This project implements a custom hash map with dynamic rehashing and O(1) chaining to handle student records efficiently. You will learn how to manage persistent storage directly via C++ File I/O streams while maintaining rapid lookup times. This is perfect for understanding how databases manage indexing under the hood.

DEV Community: Pratik Pathak

The Real Difference Between Azure OpenAI and the Standard API

Core Architectural Differences

Network Isolation & Security

Data Residency and Compliance

Identity and Access Management (IAM)

Content Filtering and Responsible AI

Pros, Cons, and Trade-offs

Final Thoughts

I run Code AI Locally, fully offline and Pay 0$ on subscription

Why You Need Offline Code AI in 2026

The Stack: Ollama and Continue.dev

Downloads & Tools Needed

1. Setting up the Local API with Ollama

2. Bridging the Gap with Continue.dev

Top Local Models for Offline Code AI

The Trade-offs: Hardware Constraints

Final Thoughts

LangGraph vs Azure AI Agents: Orchestration Frameworks Compared

What is LangGraph?

The Developer’s Playground

What are Azure AI Agents?

The Managed Enterprise Engine

Head-to-Head Architectural Comparison

1. Developer Experience and Control

2. State Management and Memory

3. Deployment and Scalability

The Verdict: Which Should You Choose?

Conclusion

I saved up 80% Azure OpenAi cost optimization by making these 7 architectural decision

Decision 1: Single-Call Simplicity vs Multi-Step Expansion

Decision 2: Model Selection – Capability vs Cost Efficiency

Real Pricing Difference (Illustrative)

Practical Routing Pattern

Decision 3: Token Budgeting – Input Size Is the Hidden Multiplier

Practical Optimization Techniques

Example Impact

Decision 4: Caching – Avoid Paying Twice for the Same Work

Two Types of Caching

Azure Implementation

Decision 5: Retry and Loop Control – The Silent Cost Multiplier

Real Scenario

Common Causes

Example: Exponential Backoff

Control Mechanisms

Decision 6: Observability – You Can’t Optimize What You Can’t See

Minimal Trace Example

Azure Implementation

Decision 7: System Design – Cost as a First-Class Constraint

Concrete Example

Compounding Effect

When Azure OpenAI Cost Optimization Matters Most

Final Thoughts

FAQ

What is the biggest cost driver in Azure OpenAI systems?

How can I reduce token usage effectively?

Should I always use the most advanced model?

How does semantic caching reduce cost?

Why do retries increase cost so much?

When should I start optimizing costs?

What is the difference between exact match and semantic caching?

Do you know Gemini Chrome Skills? A single line makes browser your AI Agent.

What Are Gemini Chrome Skills?

How to Create Your Own Custom Skill

The Magic of Multi-Tab Analysis

The Pre-Built Skills Library

Privacy and Security Measures

Final Thoughts

Top 25+ Advanced DSA Projects in C++ with Source Code

Top 25 Advanced DSA Projects in C++

1. Student Records System

2. Snake Game Logic Engine

3. Library Management with AVL Trees

4. Sudoku Solver Engine

5. GPS Navigator (Dijkstra)

6. Huffman Coding Compression

7. File System Simulator

8. Bank Core Management

9. Social Graph Analysis

10. Text Editor Engine