the ai-native dev stack
for people building agents and llm products, not just bolting a chatbot onto a crud app.
plus llm api spend
most "ai stack" lists are just a model provider and a vague gesture at langchain. that's not a stack, that's a single api call with extra steps. this list is for the point where the llm calls itself, calls tools, and needs you to actually see what it did — because at that point a chat completion call isn't enough and you know it.
the load-bearing decision here is mastra over rolling your own orchestration. people resist agent frameworks because the early ones were overengineered, but skipping memory and tool-calling infrastructure just means you rebuild a worse version of it three weeks in, under deadline pressure.
observability is the piece teams skip until something goes wrong in production and nobody can explain why the agent did what it did. langfuse before launch, not after the first weird support ticket.
the stack — 7 tools
llm observability
·Langfuse
open source and self-hostable — no lock-in to one llm's tracing tool.
error tracking
·Sentry
catches your llm app's weird errors before users have to report them.
skip this stack if
- ×your product is a thin wrapper around one api call — you don't need an agent framework or dedicated observability for that, just call the api.
- ×you're cost-sensitive on inference — this stack doesn't pick a model provider, and that's usually where the real bill shows up.
- ×you need full control over model weights or self-hosted inference — that's a different, heavier stack entirely.
one of 5 opinionated stacks.
see all stacks →