Pragmatic write-ups on AI architecture, production patterns, and the trade-offs we hit on real client work.
We run production LLM workloads on all three. Here is how they actually compare on the dimensions that matter once you stop building demos.
A frank look at what it actually takes to keep an agent alive in production — circuit breakers, retries, state, and the failure modes nobody warns you about.
A pragmatic decision guide for choosing between prompting, RAG, and fine-tuning — based on the trade-offs we hit on real client engagements.
What guardrails actually do (and do not do) on Bedrock, Azure OpenAI, and OpenAI's hosted API — and where you still need to build your own.
How to decompose a real workflow into agents, route between them, and keep the whole system observable — without building a science project.
A pragmatic comparison of pgvector, Pinecone, Qdrant, Weaviate, and OpenSearch — with the questions we ask before recommending any of them.
Concrete techniques we use to keep Bedrock spend predictable on production workloads — model selection, caching, batching, and the cost surprises to watch for.
