#AI Architecture

Articles about AI Architecture — exploring patterns, best practices, and real-world implementations in production systems.

10 posts tagged with ai architecture. ← All posts

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Pratik Dhanave · ·5 min read

smolagents in Practice

smolagents is the right choice when you value a small library you can fully understand, the code-agent approach fits your task, and you can execute code safely. It's the wrong choice when you need a big ecosystem, can't sandbox, or your tasks are simple isolated calls. This closing post gives the honest verdict and places smolagents in the landscape.

smolagents is the right choice when you value a small library you can fully understand, the code-agent approach fits your task, and you can execute code safely — and the wrong choice when you need a big ecosystem, can't sandbox, or your tasks are simple isolated calls.

#smolagents#AI Agents#Hugging Face#AI Architecture
Pratik Dhanave · ·5 min read

Strands in Practice

Strands is the right framework when you want to trust a capable model to drive and get out of its way — and the wrong one when you need to guarantee a process. This closing post gives the honest verdict on when to reach for Strands, how it compares to its peers, and how the model-driven approach fits the wider agent landscape.

Strands is the right framework when you want to trust a capable model to drive and get out of its way — and the wrong one when you need to guarantee a process. The honest verdict on when to reach for Strands and how it compares.

#Strands#AI Agents#AWS#AI Architecture
Pratik Dhanave · ·5 min read

Postgres/pgvector vs a Dedicated Vector Database

The vector-storage decision has a boringly practical answer that cuts against the hype: for most systems, the database you already run with a vector extension beats adding a new specialized system — until scale or specific features force the upgrade.

A boringly practical answer that cuts against the hype: for most systems the database you already run with a vector extension beats adding a specialized system — until scale or specific features force the upgrade.

#AI Architecture#RAG#Databases#AI Engineering#Software Architecture#LLMs#Vector Databases
Pratik Dhanave · ·5 min read

Managed API vs Self-Hosting Open Models

This is the classic fixed-versus-marginal decision, and it has a clean answer: managed APIs win until your volume is high and steady enough to keep expensive GPUs busy — which is a much higher bar than most teams assume.

The classic fixed-vs-marginal decision with a clean answer: managed APIs win until your volume is high and steady enough to keep expensive GPUs busy — a much higher bar than most teams assume.

#AI Architecture#AI Engineering#FinOps#LLMs#Software Architecture#APIs
Pratik Dhanave · ·5 min read

MCP vs A2A: Tools vs Agents

The most common question about the two big agent protocols is which one to use — and the answer is almost always "both," because they solve different problems: MCP connects an agent to its tools, A2A connects an agent to other agents.

The most common question about the two big agent protocols is which to use — and the answer is almost always both, because MCP connects an agent to its tools and A2A connects an agent to other agents.

#AI Architecture#MCP#A2A#AI Agents#AI Engineering#Software Architecture#LLMs
Pratik Dhanave · ·7 min read

Model Selection and Prompt Audits

Model selection is the most commonly botched cost decision, because the intuitive answer — pick the cheaper model — is measured by the wrong number. The right number is cost per completed task, and by that measure the more capable model often wins. Paired with it is the least-known lever of all: auditing prompts written for an older model against your current one.

Model selection is the most commonly botched cost decision, because the intuitive answer — pick the cheaper model — is measured by the wrong number. The right number is cost per completed task, priced on the tail not the median.

#AI Cost#AI Engineering#LLMs#AI Architecture#FinOps#Prompt Engineering
Pratik Dhanave · ·5 min read

RAG vs Fine-Tuning vs Long-Context

The most common architecture mistake in applied AI is reaching for fine-tuning to fix a knowledge problem — so the single most useful rule here is that RAG is for knowledge and fine-tuning is for behavior, and long-context is a convenience, not a strategy.

The most common architecture mistake is reaching for fine-tuning to fix a knowledge problem — so the key rule: RAG is for knowledge, fine-tuning is for behavior, and long-context is a convenience, not a strategy.

#AI Architecture#RAG#Fine-Tuning#AI Engineering#Software Architecture#LLMs
Pratik Dhanave · ·5 min read

Choosing a Model Platform: Bedrock vs watsonx vs NVIDIA NIM vs Vertex

The model platform decision is usually decided before you compare models at all — by which cloud you're already on, what governance you need, and whether you're renting inference or running it — and getting that framing right matters more than any benchmark.

The model-platform decision is usually settled before you compare models — by which cloud you're on, what governance you need, and whether you're renting inference or running it.

#AI Architecture#AI Engineering#LLMs#Software Architecture
Pratik Dhanave · ·4 min read

Choosing an Agent Framework: MAF vs LangGraph vs ADK vs CrewAI

Four popular agent frameworks, four genuinely different philosophies — and the right choice is decided less by features than by how much control you want, how your team thinks, and what you're actually building.

Four popular agent frameworks, four genuinely different philosophies — the right choice is decided less by features than by how much control you want, how your team thinks, and what you're building.

#AI Architecture#AI Agents#AI Engineering#Software Architecture#LLMs
Pratik Dhanave · ·5 min read

How to Make AI Architecture Decisions

Most AI architecture debates are settled by hype, familiarity, or whoever spoke last — this series settles them by requirements and trade-offs, starting with the meta-framework that every specific decision reduces to.

Most AI architecture debates are settled by hype or familiarity; this series settles them by requirements and trade-offs, starting with the meta-framework every specific decision reduces to.

#AI Architecture#AI Engineering#Software Architecture#LLMs

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.