How to choose an LLM API for agents

Selecting an API for an agent that makes repeated model calls requires evaluating the full workflow rather than individual request prices, including tool use, retries, context expansion, provider reliability, privacy, and total completed-task cost. The guidance recommends comparing models on representative fixed tasks, confirming support for required capabilities such as streaming, tool calling, structured outputs, images, and context length through a live catalog, and recognizing that OpenAI-compatible syntax does not guarantee identical provider features. It advises defining routing and fallback policies, checking privacy and jurisdiction for all potential providers, setting project-specific keys and spend limits, and limiting tool iterations and retries to prevent runaway costs. Reliable evaluation should track aggregate tokens, latency, errors, and output quality across entire runs, while migrations between gateways should test authentication, routing, and recoverability from failures such as timeouts, budget exhaustion, or unavailable providers.

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