OpenAI GPT-4 and Codex Models Now Available on AWS Bedrock

intermediate 5 min read updated 3 Jun 2026
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In a significant move poised to reshape enterprise AI adoption, OpenAI’s frontier models and Codex have achieved general availability on Amazon Bedrock as of June 3, 2026. This integration empowers AWS customers with seamless, direct access to cutting-edge AI capabilities, embedding them deeply within their established AWS security, compliance, governance, procurement, and billing frameworks. The aim is clear: to dramatically accelerate the journey of AI projects from experimental evaluation to robust production deployment, turning innovative concepts into tangible business value faster than ever before.

What’s New: OpenAI Models on AWS Bedrock

This pivotal development marks the general availability of select OpenAI models and Codex directly through Amazon Bedrock. The collaboration between OpenAI and AWS is strategically designed to dismantle common operational barriers, streamlining the adoption of advanced generative AI and code generation capabilities for enterprises deeply invested in the AWS ecosystem. It signifies a concerted effort to remove the complexities often associated with integrating third-party AI services, making sophisticated AI more accessible and manageable for large-scale enterprise use.

Key Models Available

The general availability includes specific OpenAI models that developers can now leverage directly through Amazon Bedrock:

  • GPT-4: An advanced language model, offering capabilities for complex reasoning, content generation, summarization, and conversational AI.
  • GPT-3.5 Turbo: Another powerful language model, suitable for a wide range of natural language processing tasks, often serving as a robust foundation for various AI applications.
  • Codex: A model specifically designed for code generation and understanding, enabling developers to translate natural language instructions into code, complete code, or debug existing codebases.

These models are now accessible via the Bedrock API, allowing developers to integrate them into applications with familiar AWS tooling.

Implications for Enterprises and Developers

For enterprises, this integration significantly lowers the barrier to entry for deploying cutting-edge AI. It means that teams can leverage OpenAI’s powerful models without needing to build custom integrations or navigate separate procurement and security processes. This accelerates the journey from proof-of-concept to production for AI-driven initiatives.

Developers benefit by being able to build on OpenAI’s models using the AWS infrastructure they already know. This includes using AWS SDKs, CLIs, and existing deployment pipelines, reducing the learning curve and operational overhead. The focus shifts from infrastructure management to innovative application development.

Integration with AWS Workflows

A core advantage of this general availability is the deep integration with established AWS operational workflows. Enterprises can now manage their OpenAI model usage through:

  • AWS Security: Leveraging existing AWS security controls, identity and access management (IAM), and network configurations.
  • Compliance: Adhering to organizational and regulatory compliance standards already in place for their AWS environments.
  • Governance: Applying established AWS governance policies for model usage, data handling, and cost management.
  • Procurement and Billing: Consolidating AI spending and procurement through existing AWS agreements and billing cycles, simplifying financial operations and providing a unified view of all cloud expenditures. This centralized approach streamlines budgeting, cost allocation, and financial reporting for AI initiatives across the enterprise.

This unified approach is critical for large organizations looking to scale AI safely and efficiently.

Benefits for Production AI

The general availability on Amazon Bedrock directly addresses the challenges of moving AI projects into production. It provides a robust and scalable environment for deploying applications powered by OpenAI models.

  • Accelerated Deployment: Reduced friction in security reviews and procurement means faster development cycles and quicker time-to-market for AI products.
  • Operational Consistency: Developers can maintain consistent operational practices across their entire AWS footprint, including their AI workloads.
  • Scalability and Reliability: Leveraging AWS’s global infrastructure provides the scalability and reliability needed for production-grade AI applications.
  • Reduced Overhead: Centralized management of security, compliance, and billing reduces the administrative burden on development and operations teams.

This integration is a significant step toward making advanced AI capabilities more accessible and manageable for large-scale enterprise adoption. This robust framework is essential for organizations aiming to scale their AI initiatives with confidence, security, and efficiency.

Getting Started for Developers and AWS Customers

Leveraging these new capabilities is straightforward for AWS customers and developers. Here’s how you can begin integrating OpenAI’s models into your applications:

  • Access Amazon Bedrock Console: Navigate to the Amazon Bedrock service in your AWS Management Console. This is your central hub for managing models, provisioned throughput, and usage.
  • Enable Model Access: Before using a model, you must explicitly enable access to the desired OpenAI models (GPT-4, GPT-3.5 Turbo, Codex) within the Bedrock console under the “Model access” settings.
  • Utilize AWS SDKs and APIs: Integrate the models into your applications using familiar AWS SDKs (Python boto3, Java, Node.js, Go, etc.) or direct API calls. Bedrock provides a unified API endpoint (e.g., <https://bedrock-runtime>.<region>.amazonaws.com/model/openai.<model_id>/invoke) for all available models, simplifying integration.
    • Example (Python boto3):
        import boto3
        import json

        # Initialize the Bedrock runtime client
        bedrock_runtime = boto3.client('bedrock-runtime', region_name='us-east-1')

        # Specify the OpenAI model ID
        model_id = 'openai.gpt-4' # Options: 'openai.gpt-4', 'openai.gpt-3-5-turbo', 'openai.codex'
        prompt_text = "Explain the concept of quantum entanglement in simple terms."

        # Prepare the request body for the model
        body = json.dumps({
            "prompt": prompt_text,
            "max_tokens_to_sample": 500,
            "temperature": 0.7,
            "top_p": 0.9
        })

        # Invoke the model
        response = bedrock_runtime.invoke_model(
            body=body,
            modelId=model_id,
            accept='application/json',
            contentType='application/json'
        )

        # Parse and print the response
        response_body = json.loads(response.get('body').read())
        print(response_body.get('completion'))
  • Explore Documentation and Examples: Refer to the comprehensive Amazon Bedrock documentation for detailed API references, SDK guides, code examples across various languages, and best practices for prompt engineering, fine-tuning (where applicable), and model deployment. Key resources include:
  • Experiment with Use Cases: Start building prototypes for content generation, code completion, summarization, chatbots, and more, leveraging the power of OpenAI models within your secure AWS environment. Consider Bedrock’s playground for quick experimentation and iterating on prompts.

This streamlined access ensures that developers can quickly move from experimentation to building production-ready AI applications, fully integrated into their existing AWS infrastructure.

What To Watch Next

  • Broader Model Expansion: Monitor for additional OpenAI models or new versions becoming available on Amazon Bedrock, indicating deeper partnership and expanded capabilities.
  • Specific Enterprise Case Studies: Look for early adopter enterprises sharing concrete use cases and performance metrics, particularly around security, compliance, and cost efficiency.

The general availability of OpenAI’s frontier models and Codex on Amazon Bedrock represents a landmark moment for enterprise AI. By deeply integrating these powerful capabilities into the AWS ecosystem, the collaboration effectively democratizes access to advanced generative AI, empowering organizations to innovate faster, operate more securely, and scale their AI ambitions with unprecedented ease. This move not only simplifies the technical and operational overhead but also sets a new standard for how cutting-edge AI can be seamlessly adopted and leveraged across industries, promising a future where advanced AI is not just possible, but practically accessible for every enterprise.

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