Third-Party AI API Integration Guide

Building your own AI models is expensive, slow, and usually unnecessary. Most AI features can be built by integrating best-in-class APIs for OCR, speech, translation, moderation, and language tasks.

The challenge isn't training. It's integration.

Categories of AI APIs worth integrating

OCR and document AI — AWS Textract, Google Document AI, Azure Form Recognizer

Speech-to-text — OpenAI Whisper, Deepgram, AssemblyAI

Text-to-speech — ElevenLabs, Google TTS, Amazon Polly

Translation — DeepL, Google Translate, AWS Translate

Moderation — OpenAI Moderation, AWS Rekognition

Language models — OpenAI, Anthropic, Cohere

Vendor selection criteria

Accuracy — Benchmark on your actual data

Cost — Per-unit pricing, volume discounts

Rate limits — Will they scale with your traffic?

Latency — P50 and P99 response times

Compliance — SOC 2, HIPAA, GDPR, data residency

Vendor lock-in — How hard is it to swap providers?

Cost modeling

Every AI API has a cost structure:

• Per call
• Per unit (page, minute, character)
• Per token

Model cost at 1x, 10x, 100x current usage. Add 30% buffer for retries and fallbacks.

Rate limits and fallbacks

Vendors enforce rate limits. Design for graceful degradation:

• Exponential backoff for retries
• Fallback to secondary vendor
• Queue non-urgent requests
• Cache common requests

Testing AI API integrations

• Contract tests (does the response shape match?)
• Error handling tests (timeouts, rate limits, auth failures)
• Cost tests (track spend per request)
• Accuracy tests (does output meet business requirements?)

Vendor lock-in mitigation

• Abstract API calls behind your own interface
• Store vendor-specific logic in one place
• Design for multi-vendor (even if you start with one)
• Negotiate exit terms upfront

Common mistakes

• Not testing on real data before committing
• Ignoring rate limits in capacity planning
• No fallback vendor
• No cost ceiling per user
• Deep coupling to one vendor's API shape