When to use AnyInfer¶

AnyInfer is not the only way to call more than one model, and provider count is not a reason to add another dependency. Use it when the application needs to own a hybrid inference runtime, not merely switch API URLs.

The problem it is built for¶

AnyInfer keeps four decisions inside one boundary:

Prepare context against the chosen target's provenance-tagged window and pricing.
Select and route across hosted providers, hubs, existing local services, and a managed local model.
Enforce behavior through typed events, structured-output validation, bounded repair, normalized usage, and explicit degradation signals.
Own local lifecycle when asked by acquiring verified model artifacts, selecting a runtime for the machine, tuning and supervising llama-server, and binding it to loopback.

That combination is useful for a desktop application, developer tool, offline-capable service, or distributable Python product that wants a cloud route and a local route without making each frontend implement the seam between them.

flowchart LR
  A[application-approved context] --> B[budget and reduce]
  B --> C[typed request and route]
  C --> D[hosted provider]
  C --> E[router or hub]
  C --> F[existing local service]
  C --> G[managed llama.cpp]
  D --> H[one typed event stream]
  E --> H
  F --> H
  G --> H

Choose the smaller tool when it is enough¶

Your actual requirement	Usually the better boundary
Call one provider	That provider's client or HTTP API
Switch among cloud APIs with one Python function	A focused provider client such as any-llm or aisuite
Centralize credentials, virtual keys, quotas, organization spend, and admin policy	A gateway such as LiteLLM, Bifrost, or Portkey
Operate a dedicated local-model platform	Ollama, LM Studio, or LocalAI
Run high-throughput GPU serving infrastructure	vLLM or another serving platform
Build semantic retrieval over a changing corpus	A retrieval or vector-index system; pass its approved results into AnyInfer if you still need the hybrid runtime
Ship one application-owned route spanning cloud and a managed local fallback	AnyInfer

Those tools can be composed. AnyInfer earns its place only when removing the boundary between them makes the application simpler or its behavior more reliable.

What is actually distinctive¶

Application-owned local inference¶

The managed llama.cpp path needs no separately operated local-model daemon. Runtime installation is an explicit download, model artifacts are pinned and verified, and the client owns tuning, readiness, active streams, idle eviction, and process cleanup. Existing Ollama, LM Studio, and vLLM services remain valid targets when they are already the right boundary.

Portable behavior, not only portable syntax¶

An OpenAI-shaped request does not make providers behave alike. AnyInfer normalizes a typed event stream, validates structured output on the client, records attempt history and timing, tracks capability provenance, and announces dropped parameters or weaker enforcement mechanisms. The conformance matrix states what is actually supported.

Context engineering with receipts¶

client.budget() connects the selected model's window, output reserve, safety headroom, token estimate, and price into one preflight result. anyinfer.context can then select, pack, or structurally reduce application-approved documents to the remaining allowance. Every reduction reports its omissions and binding constraints; material that cannot fit at any fidelity can be distilled hierarchically with call count and usage exposed.

Token counting and text splitting exist elsewhere. The useful distinction here is that the same target capabilities drive budgeting, reduction, pre-dispatch refusal, cost planning, and context-overflow routing.

What is not a differentiator¶

A long provider list.
An OpenAI-compatible sidecar.
Basic retry and fallback.
The ability to call an already-running Ollama or vLLM endpoint.

AnyInfer supports all of those because integrators need them. Its reason to exist is the coherent runtime and correctness contract around them.