Choosing & integrating LLM APIs: a practical guide

LLM APIs provide hosted model inference and related capabilities such as embeddings, tool calling, multimodal inputs, streaming, and batch processing, typically charging separately for input and output tokens. Selecting a provider requires balancing task-specific quality, latency including tail performance, cost, data retention and compliance needs, and operational control across direct proprietary APIs, cloud-mediated services, open-model platforms, and self-hosted models. Effective production integrations use secure credentials, streaming, schema validation, guarded tool execution, and selective retries with exponential backoff for temporary failures rather than client or authentication errors. The guide emphasizes reducing cost and latency through provider prompt caching and semantic caching, managing multi-dimensional rate limits, and maintaining external session state because many APIs are stateless and require conversation history to be resent. At larger scale, organizations may centralize routing, caching, rate limiting, identity, guardrails, auditing, and observability in a GenAI gateway, while smaller deployments may keep these functions within applications. Redis is presented as an in-memory option for semantic caching, vector search, and shared agent memory, including managed Redis Iris services, though the broader argument is that the infrastructure around API calls is as important as model selection itself.

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