🤖 Inference APIs
APIs and runtimes for AI models, especially LLMs, enable powerful text generation and processing in apps. They serve as the foundation for many AI solutions and allow easy integration, making advanced AI accessible for developers.
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About Inference APIs
Inference API providers give developers access to large language models without managing GPU infrastructure. These services expose model endpoints via REST or gRPC APIs, handling scaling, load balancing, and hardware optimization behind the scenes. Most support popular open-source models like Llama, Mistral, and Mixtral alongside proprietary offerings.
The competitive landscape has shifted toward speed and cost efficiency. Providers differentiate on time-to-first-token latency, throughput (tokens per second), supported model catalog, and pricing models (per-token, per-request, or reserved capacity). Some specialize in specific hardware like custom ASICs or high-memory GPUs for long-context workloads.
For teams building production applications, the choice of inference provider impacts user experience directly. Factors like geographic availability, uptime SLAs, streaming support, function calling capabilities, and batch inference pricing all matter when selecting a provider.
Frequently Asked Questions
What is an LLM inference API?
An LLM inference API is a hosted service that runs large language model predictions on your behalf. You send prompts via HTTP requests and receive generated text back. The provider handles GPU allocation, model loading, scaling, and optimization, so you can focus on building your application.
How do I choose between inference API providers?
Consider latency requirements, supported models, pricing structure, geographic availability, and features like streaming, function calling, and batch processing. Run benchmarks with your actual workload, as performance varies significantly by model size and prompt length.
Is it cheaper to self-host or use an inference API?
For most teams, inference APIs are more cost-effective until you reach consistent high-volume usage. Self-hosting requires GPU procurement, ops expertise, and handling idle capacity. APIs let you pay per token and scale elastically. The break-even point depends on your traffic patterns and model choices.
What is time-to-first-token and why does it matter?
Time-to-first-token (TTFT) measures the delay between sending a request and receiving the first token of the response. Lower TTFT creates a more responsive user experience, especially in chat interfaces. It is influenced by model size, hardware, request queue depth, and whether the provider uses speculative decoding or other optimization techniques.
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