AWS adds GPT-5.5, GPT-5.4 and Codex to Amazon Bedrock
Summary
Amazon Web Services (AWS) has announced the general availability of OpenAI's GPT-5.5, GPT-5.4, and the Codex coding agent on its Amazon Bedrock platform. This transition follows a limited preview phase that started on April 28, after OpenAI's exclusive cloud arrangement with Microsoft concluded. Enterprise customers can now access these models natively within the Bedrock catalog, using unified APIs similar to those for Anthropic and Meta models. GPT-5.5 is optimized for complex, autonomous, multi-step tasks and demanding cognitive workloads, while GPT-5.4 offers price-to-performance efficiency. Both language models are available at direct OpenAI per-token rates, with no extra platform fees, and consumption counts towards existing AWS cloud spending commitments. The Codex agent, also pay-per-token, routes inference through AWS infrastructure, supporting development in IDEs like Visual Studio Code, JetBrains, and Xcode.
Key takeaway
For MLOps Engineers deploying large language models or coding agents within AWS, this Bedrock expansion simplifies integration and cost management. You can now access OpenAI's GPT-5.5, GPT-5.4, and Codex directly, consolidating billing against your existing AWS commitments. This eliminates separate vendor contracts and streamlines API management through a unified interface. Consider migrating existing OpenAI integrations to Bedrock for operational efficiency and simplified governance.
Key insights
AWS Bedrock now offers OpenAI's GPT-5.5, GPT-5.4, and Codex, providing enterprise access with integrated billing and unified APIs.
Principles
- Unified APIs simplify multi-model deployment.
- Cloud spending commitments can absorb model costs.
In practice
- Integrate Codex into VS Code or JetBrains.
- Use GPT-5.4 for price-to-performance tasks.
Topics
- Amazon Bedrock
- OpenAI GPT Models
- Code Generation
- Enterprise AI
- Cloud AI Services
Best for: CTO, AI Architect, Machine Learning Engineer, AI Engineer, MLOps Engineer, Software Engineer
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Editorial summary, takeaway, and curation by AIssential. Original article published by Dataconomy.