Modern AI Dev Stack 2026: LangChain, LlamaIndex, vLLM, Vector DBs—What to Use and Why

The AI application stack is noisy. New libraries ship weekly; most product teams need a boring, reliable subset. This map explains layers, popular tools, why they exist, and when to adopt—so you do not build a demo museum.

Companion reads: RAG, production agents.

Stack layers (top to bottom)

1. Product UI / API — your app, auth, billing
2. Orchestration — chains, tools, agents, RAG flows
3. Model access — APIs or self-hosted inference
4. Retrieval — embeddings + vector/keyword search
5. Evals & observability — quality and cost control
6. Data & platform — warehouses, feature stores, queues

Orchestration: LangChain, LlamaIndex, and thinner alternatives

Why use: connectors, retrievers, and agent patterns speed prototypes.

Why be careful: abstractions can hide control flow and make debugging painful.

Production tip: many teams prototype with a framework, then extract critical paths to plain TypeScript/Python services with explicit steps.

Serving: vLLM, TGI, TensorRT-LLM

Why use: high-throughput self-hosted inference, continuous batching, efficient GPU use.

Why wait: if managed APIs meet cost/latency SLOs, do not operate GPUs yet.

Vector databases: pgvector, Pinecone, Weaviate, Qdrant, etc.

Start simple: Postgres + pgvector covers many SaaS RAG cases.

Specialize when: scale, hybrid search features, or multi-tenant isolation needs exceed Postgres comfort.

Evals: the feature most teams skip

Without golden sets and regression checks, every model upgrade is a production gamble. Treat evals as part of the stack equal to the database.

Recommended default for product teams (2026)

• TypeScript or Python API
• Managed model API + optional self-host later
• Postgres + object storage + redis
• Thin orchestration, thick domain logic
• Tracing (OpenTelemetry) + prompt versions

Frequently asked questions

Do we need agents on day one?
No. Ship retrieval + tools with human approval first. Agents after reliability exists.

Do I need a vector database if I have under 10k documents?
Probably not. pgvector inside a Postgres you already run gets you most of the way there, and adding a dedicated vector database at that scale is usually solving a problem you don’t have yet. Revisit once you’re past low six figures of documents or need hybrid search features Postgres doesn’t do well.

When does self-hosted inference actually save money over a managed API?
Later than most teams think. You need sustained, predictable volume to make the GPU math work — sporadic or spiky traffic almost always loses to a managed API once you count idle GPU time. If you’re not sure you’ve hit that volume, you probably haven’t.

Should we build our own eval framework or use an off-the-shelf one?
Neither, yet. Get a golden set and a regression check running this week — a spreadsheet and a script is fine. Pick a framework once you know what you’re actually checking for, not before.