What is CheaperInference?

Cheaper Inference provides a marketplace that aggregates AI model capacity from multiple providers and exposes them through an OpenAI-compatible API without changing your request format. The platform offers a live catalog and rate-comparison tools to inspect available capacity, provider activity, and model rankings.

Each request is routed to a selected upstream provider and recorded in history; application logs retain only usage and billing metadata while routes can be configured for zero-data-retention.

The API supports model selection per request, A/B testing, and optional prompt caching with cache-control pass-through when supported. Integration is handled by swapping base URL and API key, preserving existing messaging, tools, streaming, and response handling.

The platform also accepts listings of unused inference capacity and manages provider review and settlement, with SDKs, docs, and usage visibility available.

CheaperInference on the web:

CheaperInference pricing

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CheaperInference's key features

OpenAI-compatible API aggregating AI model capacity from multiple providers
Live catalog and rate-comparison tools showing available capacity, provider activity, and model rankings
Per-request routing to selected upstream providers with history recording and configurable zero-data-retention (application logs retain only usage and billing metadata)
Per-request model selection, A/B testing, and optional prompt caching with cache-control pass-through
Drop-in integration by swapping base URL and API key while preserving messaging, tools, streaming, and response handling

CheaperInference use cases

Reduce your AI inference bill and latency by routing requests through Cheaper Inference's OpenAI-compatible API to select the lowest-cost or fastest model per request, run live rate comparisons and use prompt-caching passthrough to avoid repeated charges while leveraging unused inference capacity for bursty workloads
Build a privacy-first conversational product by configuring per-request routing to backends with optional zero-data-retention, sending sensitive prompts only to compliant providers while non-sensitive traffic uses cheaper models; use live cataloging and A/B testing to evaluate accuracy and cost without changing your integration
Run large-scale model experiments and quality benchmarks by splitting traffic across multiple providers via the OpenAI-compatible API, automatically compare inference costs and model results, cache prompts to stabilize evaluation spend, and take advantage of unused capacity marketplaces to scale experiments affordably