“AI APIs” is one of the most-searched filters on apis.io. It’s also one of the least useful searches if you take it literally — half of the catalog touches AI in some way now. The more useful question is: what does the AI infrastructure layer look like underneath the models themselves? That’s the cohort I want to walk through here.
The four layers of the AI stack on apis.io
The catalog organizes the AI-infrastructure surface into roughly four functional bands:
| Layer | What it does | Examples on apis.io |
|---|---|---|
| Model inference | Run inference against hosted models | OpenAI, Anthropic, Google AI, Together, Fireworks, Replicate, Cohere, Mistral |
| Vector and retrieval | Embed, store, query, rerank | Pinecone, Weaviate, Qdrant, Turbopuffer, MongoDB Atlas Vector, Elastic, pgvector hosts |
| Model serving / fine-tuning | Host your own models, fine-tune base models | Replicate, Modal, Hugging Face, Together, Anyscale, Baseten |
| Agent runtime infrastructure | Tool routing, memory, MCP, identity | Frostbyte, Kong AI Gateway, Drata MCP, AI Gateway, the MCP-server ecosystem |
The fourth row is the newest. A year ago there were no providers in the catalog publishing “agent runtime” APIs as a distinct category. Today there are several, and most of them publish MCP servers alongside or instead of traditional REST.
What’s actually moving
Three patterns in the AI-infra cohort that have shifted noticeably over the last six months:
- MCP-first publishing. Providers like Drata, BuyWhere, BrewPage, and Memesio are publishing MCP servers as a primary surface, not just a wrapper around an existing REST API. The catalog now has a dedicated path for this — the
MCP ServerAPI type and the/.well-known/mcp/server-card.jsondiscovery endpoint at the network level. - Vector databases keep partitioning. The early vector-DB providers shipped a single API. Pinecone now ships six APIs — Database Control, Database Data, Inference, Assistant Control, Assistant Data, and Admin. That’s the same enterprise-fragmentation pattern we’ve seen in fintech, and it’s a good sign of maturity: the surfaces are stable enough to be partitioned by operational role.
- Inference hardware is becoming an API. Replicate exposes a
Hardwarecapability. Modal exposes container/GPU selection programmatically. The hardware tier is moving from “account setting” to “request parameter” — which has real implications for FinOps tracking and capacity planning.
Where to start on apis.io
The category and capability entry points for AI infrastructure work:
- Machine Learning category — the canonical filtered list.
- capabilities.apis.io — search by what you want to do (embed, rerank, run inference, fine-tune) rather than by provider.
/.well-known/agent-skills/index.json— agent skills the network ships, includingdiscover-integrations.- Provider profiles worth walking: Replicate, Pinecone, Hugging Face, Cohere.
The takeaway
AI infrastructure is the vertical where the capability layer is most valuable, because the providers themselves are moving so fast that the API list a vendor advertised six months ago is already stale. Walking the catalog by capability (embed, retrieve, rerank, infer, fine-tune, host) surfaces the right cross-vendor comparison without you having to keep up with each provider’s release cadence individually.
The provider list is the inventory. The capability list is the menu. For AI infrastructure specifically, the menu is the one to read first.