Try the new console experience in Amazon Bedrock, optimized for Anthropic- and OpenAI-compatible APIs

Amazon Bedrock just rolled out a redesigned console that makes it easier to explore, test, and deploy foundation models without leaving the AWS interface. If you’ve found yourself juggling multiple browser tabs to compare models, copy-paste API documentation, or remember which code snippets work with which services, this update addresses those friction points directly. The new experience is specifically optimized for Anthropic and OpenAI-compatible APIs, meaning whether you’re using Claude, GPT models, or others in the compatible ecosystem, you’ll find a more cohesive workflow.

Here’s what’s changed under the hood. The console now lets you browse and compare models side by side with their capabilities, pricing, and performance characteristics visible at a glance. More importantly, the interface introduces a project-based organization system that keeps your work compartmentalized—much like how you’d structure repositories in Git or separate folders in your codebase. Each project comes with its own live documentation that auto-populates code snippets based on your selected model and parameters. These snippets are pre-filled and ready to run, which means you can validate an API call or test model behavior without manually hunting through docs or wrestling with configuration details.

Practically speaking, this matters for several common scenarios. If you’re a data scientist evaluating different models for a specific task—say, comparing Claude’s performance against OpenAI’s on entity extraction—you can now run side-by-side tests within the same interface without switching contexts. For developers building chatbots or content generation pipelines, the project-aware snippets mean faster iteration cycles. You’re not rewriting authentication or payload structures each time you switch models; the console handles that scaffolding. Teams prototyping multiple AI features simultaneously can organize experiments by project, making it easier to track which model choices worked best for which use cases. If you’re coming from a background where you typically test APIs through curl or Postman, this console experience bridges the gap between exploration and production-ready code.

The real value emerges when you think about your actual workflow. Instead of opening a terminal, writing Python to test an API, checking documentation in another tab, and manually copying boilerplate code into your application, you now have a unified space where evaluation and code generation happen together. For teams getting comfortable with AI integrations, this lowers the barrier to experimentation—you can test assumptions about model behavior before committing to implementation work. The compatibility with both Anthropic and OpenAI APIs also means your team isn’t locked into one provider’s tooling or conventions, which matters as the AI landscape continues to evolve.