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Integrating AI Platforms: Claude, OpenAI, Gemini and Open Models via API
Production-grade integration of Claude, OpenAI, Gemini, cloud platforms and open models: formats, tools, cost, reliability, security
- Advanced
- 8 h 40 min
- 19 lessons in 7 modules
- 2 h 51 min of video lectures
- Updated Sep 2026
About this course
Most AI products now depend on more than one model provider, and the hard part is integration, not the prompt. This advanced, hands-on course teaches you to integrate Anthropic Claude, OpenAI and Google Gemini through their official SDKs, plus cloud platforms (Amazon Bedrock, Microsoft Foundry, Gemini Enterprise Agent Platform) and open-weight models via hosted APIs. You will compare message formats side by side, stream responses safely, implement tool calling and structured outputs across providers, send images, PDFs and audio, and build semantic search with embeddings. You will cut cost with batch APIs and prompt caching, handle rate limits and failures with backoff and circuit breakers, track spend per feature and tenant, and design a multi-provider abstraction with fallbacks and model routing. You will finish with security, privacy and a production checklist, then build, test and deploy a provider-agnostic content-generation microservice with a TypeScript client.
Tools you’ll use
- Claude API
- OpenAI API
- Gemini API
- Anthropic Python SDK
- OpenAI Python SDK
- Google Gen AI SDK
- Amazon Bedrock
- Microsoft Foundry
- Gemini Enterprise Agent Platform
- vLLM
- Ollama
- FastAPI
- TypeScript
- Pydantic
- Docker
Skills
- LLM API integration
- Multi-provider architecture
- Structured outputs
- Tool calling
- LLM cost optimization
- API reliability engineering
- AI data privacy
- Embeddings and semantic search
What you’ll be able to do
- Choose providers and models with your own evals and cost per completed task
- Call Claude, OpenAI and Gemini correctly, including streaming, tools and structured outputs
- Process images, PDFs and audio and build embeddings-based semantic search
- Reduce cost and latency with batch APIs, prompt caching, routing and budgets
- Handle rate limits and failures with typed errors, backoff, circuit breakers and fallbacks
- Integrate via Bedrock, Microsoft Foundry, Gemini Enterprise Agent Platform and open-weight hosts
- Ship a secure, privacy-aware, observable multi-provider AI microservice
Curriculum
Syllabus
- Modules
- 7
- Lessons
- 19
- Reading time
- 5 h
- Assessment questions
- 30
The provider landscape and how to choose models with your own evals, plus authentication, key management and secrets done safely.
- The AI platform landscape: providers, APIs and how to chooseVideo lecture, 9′14 min
- API keys, authentication and secrets managementVideo lecture, 9′13 min
Messages and conversation state, streaming, tool calling and structured outputs with Claude, OpenAI and Gemini side by side.
- Messages, roles and conversation state across Claude, OpenAI and GeminiVideo lecture, 9′16 min
- Streaming responses to users and between servicesVideo lecture, 9′15 min
- Tool calling across Claude, OpenAI and GeminiVideo lecture, 9′17 min
- Structured outputs: reliable JSON from every providerVideo lecture, 9′15 min
Images, PDFs and audio across providers, and embeddings APIs for semantic search in production.
- Multimodal inputs: images, PDFs and audioVideo lecture, 9′16 min
- Embeddings APIs and semantic search in productionVideo lecture, 9′15 min
Batch APIs, prompt caching, rate limits and retries, and cost tracking with budgets and unit economics.
- Batch APIs: bulk processing at a discountVideo lecture, 9′14 min
- Prompt caching across providersVideo lecture, 9′14 min
- Rate limits, errors, retries and backoffVideo lecture, 9′15 min
- Cost tracking, attribution and budgetsVideo lecture, 9′14 min
A thin abstraction layer with fallbacks, and model routing strategies measured against simple baselines.
- Multi-provider abstraction and fallbacksVideo lecture, 9′17 min
- Model routing: the right model for each requestVideo lecture, 9′14 min
Amazon Bedrock, Microsoft Foundry and Gemini Enterprise Agent Platform, plus open-weight models via hosted APIs and self-hosting.
- Cloud AI platforms: Amazon Bedrock, Microsoft Foundry and Gemini Enterprise Agent PlatformVideo lecture, 9′16 min
- Open-weight models via hosted APIs and self-hostingVideo lecture, 9′15 min
Security, privacy and the production checklist, then a provider-agnostic content-generation microservice built, tested and deployed.
- Security, data privacy and the production checklistVideo lecture, 9′16 min
- Capstone part 1: build a provider-agnostic content-generation microserviceVideo lecture, 9′26 min
- Capstone part 2: test, evaluate, deploy and ship a TypeScript clientVideo lecture, 9′22 min
- Final assessment
Your certificate
Finish with a credential anyone can check
Earn the Certified AI Platform Integration Engineer badge: The holder can integrate Claude, OpenAI, Gemini, cloud AI platforms and open-weight models in production: correct message formats, streaming, tool calling, structured outputs, multimodal inputs and embeddings; cost control with batch, caching, routing and budgets; resilient error handling and fallbacks; and secure, privacy-aware deployment of a multi-provider AI microservice.
Completed all lessons and scored at least 80% on the final assessment.
- A public verification page
- A PDF certificate to download
- An Open Badge you can share
- One click to your LinkedIn profile
Final assessment
- 30questions drawn from a larger pool
- 45 mintime limit
- 80%pass mark
- 3attempts per 24 hours
Start learning today. It’s free.
Every lesson is free to read. A free account saves your progress, unlocks the final assessment and issues your certificate.