Volume, latency or cost constrained
A llm api stack.
LLM API · 100,000 req/mo. At the default case — 100k requests a month, no further constraints — drawn from the 112 tools already in the index. Each pick is a tool whose own skip-when is stated, because a stack built on tools it does not know how to overcommit to is a guess.
This is the default case, stated as such. Describe your case to change it — the recommendation recomputes, and anything it assumes comes back in the answer rather than staying silent.
The picks
Routing — Cloudflare AI Gateway
Strong fitEdge caching and latency dominate, on Cloudflare already.
Watch out: Requests must not transit a third party's network.
Consider Martian instead when routing tuned on your own traffic rather than a public benchmark
Inference — TensorRT-LLM
Strong fitYou are committed to NVIDIA hardware and need the last of the throughput.
Watch out: Hardware portability matters, or you have no GPUs to tune against.
Consider llama.cpp instead when the constraint is hardware, not throughput — no GPU, or an edge box
Evals — Helicone
Strong fitGateway-level observability with per-request cost and latency.
Watch out: You need eval primitives more than traffic logs.
Consider Langfuse instead when self-hostable tracing, prompts and evals in one place
The costs and the risks
- Estimated cost
- $40–$220 per month — a heuristic band from query volume, not a vendor quote.
- Confidence
- 87% — lower when constraints narrow the field.
Biggest risk at the default case: Prompt changes without evals are guesses. Version prompts and measure before routing or self-hosting.
A starting point, not a prescription. The recommendation at the default case is what the engine would tell a stranger with your workload; the recommendation at *your* case is what it tells you once it knows the constraints. The Stack Builder holds both — the builder link preselects this workload and starts with your volumes from here.