Tools & stack
AI Engineering Tools and Technologies
From model APIs to vector databases to evaluation frameworks — a practical map of the current stack.
The AI tooling landscape changes constantly, which makes it easy to feel behind. The categories of tools, however, are stable: every AI engineering stack needs a way to call models, ground them in data, give them actions, and measure the result.
Learn the categories first, and specific tool names become much easier to evaluate and swap as the ecosystem evolves.
Key takeaways
Model access and gateways
At the base of the stack is however you call the model itself — directly through a provider API or through a gateway that lets you route between multiple providers and models without rewriting your application code.
Using a gateway-style approach early saves significant rework later, since model choice tends to change as pricing, quality, and availability shift.
Retrieval and vector databases
Embeddings turn text into vectors that capture meaning, and vector databases store and search those vectors efficiently. Together they power retrieval-augmented generation — the technique behind most reliable, fact-grounded AI features.
The specific vector database matters less than understanding what it is for: fast, relevant retrieval at the moment a model needs grounding.
Orchestration, evaluation, and observability
Orchestration tools help manage multi-step workflows, tool calling, and agent loops so you are not hand-rolling that logic from scratch. Evaluation frameworks let you score outputs against a test set automatically, and observability tools log prompts, outputs, latency, and cost.
These three categories are what turn a working prototype into something a team can maintain and trust over time.
Put this into practice