Building your AI dev stack in 2026: the practical guide

Quick answer. A 2026 AI dev stack has six layers: (1) model — OpenAI/Anthropic API or local open weights, (2) runtime — Ollama for local, vLLM for production, (3) orchestration — LangChain or LlamaIndex, (4) vector store — Qdrant or pgvector, (5) evaluation — Promptfoo or Ragas, (6) observability — Langfuse or Helicone. The right pick at each layer depends on your scale, your data residency requirements, and your team's familiarity.

Why a stack guide

The 2026 AI tooling landscape is fragmented. There are 6-10 credible options at every layer, and the marketing copy makes them all sound equivalent. They are not. The right pick at each layer depends on:

• Scale — are you serving 100 requests/day or 1M?
• Data residency — does the data need to stay on your infrastructure?
• Team familiarity — is your team Python-first or TypeScript-first?
• Latency requirements — are you doing real-time or batch?

This guide walks through the six layers of a 2026 AI stack and gives the default pick for each.

Layer 1: Model

The model is the foundation. In 2026, the credible options are:

• OpenAI API — GPT-5, GPT-5-mini, o3. The default for most teams that don't have a strong reason to use anything else.
• Anthropic API — Claude Opus 4.5, Sonnet 4.5. The right pick for code generation, long-context tasks, and safety-sensitive applications.
• Google API — Gemini 2.5 Pro, Flash. The right pick for Google Cloud shops and for the longest context windows.
• Local open weights — Llama 4, Mistral, Qwen, DeepSeek. The right pick for data-residency-sensitive applications or for cost optimization at scale.

Default pick: OpenAI GPT-5-mini for prototyping, GPT-5 for production. Add Anthropic Claude Sonnet 4.5 for code-generation paths.

For the broader data on the AI tooling ecosystem, see Best open source AI tools in 2026.

Layer 2: Runtime

If you go local, the runtime is the layer that hosts the model. In 2026:

• Ollama — the default for local development. Single-binary install, model management, OpenAI-compatible API. The right pick for prototypes and single-machine deployments. See Ollama vs llama.cpp for the head-to-head.
• vLLM — the default for production serving. High-throughput, continuous batching, PagedAttention. The right pick when you need to serve thousands of requests per second.
• llama.cpp — the underlying engine. The right pick when you need custom inference, custom quantization, or the absolute minimum overhead.
• LM Studio — desktop GUI on top of llama.cpp. The right pick for non-developer users who want a local model.

Default pick: Ollama for development, vLLM for production.

Layer 3: Orchestration

The orchestration layer is the framework that calls the model, manages prompts, and chains calls together. In 2026:

• LangChain — the default. The right pick for general LLM apps, agent frameworks, and chains. See LangChain vs LlamaIndex.
• LlamaIndex — the right pick for retrieval-heavy RAG pipelines. The differentiator is the data framework.
• LangGraph — the right pick for stateful agent workflows. The differentiator is the runtime (persistence, checkpointing, human-in-the-loop).
• CrewAI — the right pick for role-based multi-agent patterns.

Default pick: LangChain for general apps, LangGraph for stateful agents, LlamaIndex for retrieval-heavy RAG.

Layer 4: Vector store

The vector store is where you keep embeddings for RAG. See Vector databases in 2026 for the full breakdown. The short version:

• Qdrant — the default for new RAG stacks. Rust-native, single-binary, strong filtering.
• pgvector — the right pick when you already have Postgres.
• Chroma — the right pick for prototypes.
• Pinecone — the right pick when you want a managed service.

Default pick: Qdrant.

Layer 5: Evaluation

The evaluation layer is the test framework for your LLM app. In 2026:

• Promptfoo — the default for prompt evaluation. TypeScript-first, supports all major models, easy to set up.
• Ragas — the default for RAG-specific evaluation. The differentiator is the RAG-specific metrics (faithfulness, answer relevance, context precision).
• Braintrust — the right pick for production evaluation. The differentiator is the eval-in-production story and the experiment tracking.

Default pick: Promptfoo for prompt evaluation, Ragas for RAG-specific evaluation.

Layer 6: Observability

The observability layer is where you watch your LLM app in production. In 2026:

• Langfuse — the default for open-source-first teams. Self-hostable, OpenTelemetry-native, good UI.
• Helicone — the right pick for proxy-first observability. The differentiator is the one-line proxy setup.
• Arize Phoenix — the right pick for OpenTelemetry-first observability and eval.

Default pick: Langfuse.

The full default stack

For a typical 2026 AI dev team:

• Model: OpenAI GPT-5-mini (dev), GPT-5 (prod); Anthropic Claude Sonnet 4.5 (code-gen)
• Runtime: Ollama (dev), vLLM (prod)
• Orchestration: LangChain + LangGraph
• Vector store: Qdrant
• Evaluation: Promptfoo + Ragas
• Observability: Langfuse

This stack is not the only valid choice, but it is the safest. Every component has at least one viable alternative, and the migration paths are well-trodden. The GitPop radar tracks all of these projects, so you can see their current momentum and which way the trends are going.

For the broader data, see State of GitPop H2 2026. For the comparison between specific layers, see the compare matrix.