How to Choose an AI API for Your Application
Compare AI APIs using your actual workload, quality criteria, response requirements, and operating costs, then choose a service your team can support.
Read the field noteConnect model capabilities to a measurable task.
AI & LLMs
AI integration begins with a useful question: what result does the application need, and how will you evaluate it? These field notes connect API choice with token usage, retrieval, and production operations. They explain the concepts that help a team move from an interesting output to a repeatable product behavior.
Read the API selection guide to shape an evaluation, the token guide to understand usage, and the retrieval guide to connect content with a language workflow. The production checklist then brings monitoring and operational decisions into the picture. Use the AI, prompts, and token hubs as a wider map of the topic.
AI & LLMs field notes
LLM Tokens Explained: Context, Usage, and Cost Planning
Understand LLM tokens, context limits, input and output usage, and practical budgeting methods for AI applications without relying on rough word counts.
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Embeddings APIs and Vector Search: From Text to Retrieval
Learn how embeddings, document chunks, permissions, ranking, and evaluation work together to build a useful and maintainable vector search workflow.
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AI API Production Checklist: Quality, Privacy, and Monitoring
Prepare an AI API feature for production with defined quality checks, data controls, resource budgets, monitoring, incident ownership, and a rollback plan.
Read the field noteContinue exploring: developer resources, provider guides, and the API glossary connect these field notes with the wider depot.