Question 1

What is an LLM API?

Accepted Answer

An LLM API is a metered programmatic endpoint that exposes a large language model for completion, chat, embeddings, and tool use — charged per token. Engineering teams call it from applications to add reasoning, generation, classification, and conversational capability without training or hosting a model themselves.

Question 2

How much do LLM APIs cost?

Accepted Answer

Prototype usage runs £8–80 per month. Production SaaS with moderate AI feature density lands between £400 and £4000 per month. High-volume products and agent platforms reach £15000 to several hundred thousand per month, where prompt caching, model routing, and batch processing become the dominant unit-cost levers.

Question 3

How do you choose the right LLM API for production?

Accepted Answer

Route by task rather than by vendor. Use frontier models for hard reasoning, mid-tier models for routine generation, and small fast models for classification and intent routing. Single-model deployments overpay; multi-model routed architectures cut costs sharply at quality parity. Build the router early — retrofitting it is painful.

Question 4

LLM API versus self-hosted open-weight models — which is better?

Accepted Answer

APIs win on operational simplicity, access to the latest models, and zero infrastructure overhead. Self-hosted open-weight models win at extreme volume, strict data residency requirements, and predictable cost ceilings. The economic crossover typically sits in the high six- to seven-figure annual spend range.

Question 5

How is LLM API pricing calculated?

Accepted Answer

Most providers charge per million tokens, with separate rates for input, output, and cached prompts. Output tokens typically cost three to five times input. Cached and batched calls drop dramatically. Tool calls, embeddings, and structured-output overhead add line items on top of the base token price.

Question 6

Are LLM API prompts private?

Accepted Answer

Default consumer tiers often retain prompts for abuse monitoring and may include them in model improvement programmes. Enterprise tiers with zero-retention guarantees, training opt-out, regional data residency, and contractual audit rights are the standard for regulated workloads. Always verify data-handling terms in writing before sending production data.

LLM APIs marketplace

Runpod

Claude AI