AI Infra
AI Infrastructure & LLMOps
Evaluation, tracing, vector storage and serving — the unglamorous layer that decides whether an AI feature survives contact with real traffic.
- Tools 01
8
in this category
- Average score 02
82/100
- Free tier 03
8of 8
- Self-hostable 04
5of 8
- Comparisons 05
1
[ 01 ] Our read on this market
Every team that shipped an LLM feature in the last two years arrived at the same problem in the same order: it worked, then it silently regressed, and nobody could say when or why. This category exists to answer that. Evaluation and tracing have consolidated into the default first purchase; vector databases have largely commoditised into features of systems teams already run.
What to weigh
- 01
Eval ergonomics
The cost of writing an eval determines how many get written. Favour tools where turning a production trace into a regression test takes one click, not one sprint.
- 02
Data residency
Traces contain prompts, and prompts contain customer data. Confirm retention windows, redaction and region pinning before piping production traffic anywhere.
- 03
Model neutrality
Tools tied to a single provider age badly. Check that you can compare across model families in the same experiment.
- 04
Sampling cost at volume
Full-fidelity tracing on every request gets expensive quickly. Look for head-based and tail-based sampling controls.
[ 02 ] AI Infra tools
Ranked by composite score within this category.
[ 03 ] Comparisons in this category