Inference Provider
Inference Provider is a company or platform that runs AI model inference at scale and offers developer access through a standardized API; Providers deploy model weights on fleets of accelerators, implement optimizations like continuous batching and KV caching, and expose endpoints that accept prompt payloads and return completions; Provider selection affects latency, cost, model availability, data residency, and rate limits
Inference Provider is a company or platform that runs AI model inference at scale and offers developer access through a standardized API. Providers abstract away GPU cluster management, model serving optimization, and availability engineering, letting builders integrate model capabilities into applications by making HTTP requests.
How it works
Providers deploy model weights on fleets of accelerators, implement optimizations like continuous batching and KV caching, and expose endpoints that accept prompt payloads and return completions. They handle rate limiting, authentication, load balancing, and global availability. Pricing is typically metered per token consumed.
Key facts
- First-party providers: Model developers like Anthropic and OpenAI operate their own inference APIs.
- Third-party hosts: Companies like Together AI, Fireworks, Groq, and Replicate serve open-weight models at competitive prices.
- AI Gateways: Middleware tools like LiteLLM, Portkey, and Braintrust route traffic across multiple providers behind a unified interface.
- Self-hosting: Teams hosting models themselves with frameworks like vLLM or TGI effectively operate their own inference provider.
For builders
Provider selection affects latency, cost, model availability, data residency, and rate limits. A multi-provider strategy using an AI gateway enables fallback routing when a provider experiences degradation, and allows cost optimization by routing cheaper models for straightforward tasks. Builders should benchmark providers on their specific prompt and load profiles rather than relying on synthetic benchmarks.
Sources
- Zhao, W. X., et al. (2023). A Survey of Large Language Models. arXiv:2303.18223. arxiv.org
- Yu, G., et al. (2022). Orca: A Distributed Serving System for Transformer-Based Generative Models. OSDI. usenix.org
- Kwon, W., et al. (2023). Efficient Memory Management for LLM Serving with PagedAttention. vllm-project. github.com
- Anthropic. Claude API documentation. docs.anthropic.com
- OpenAI. API reference. platform.openai.com
