Independent benchmarks reflect Gemma 4’s intelligence-per-parameter focus: Artificial Analysis reports an Intelligence Index of 39 for Gemma 4 31B, well above the median of 15 in the 4B–40B open-weights class.

Organizations adopting open-weight foundation models (FMs) for production face a constant trade-off: access the leading models, but without compromising on data protection, regulatory alignment, or operational control. Amazon Bedrock removes that trade-off. It gives you leading open-weight FMs through a fully managed service, with inference running entirely on infrastructure operated by AWS and the security and privacy controls you expect from Amazon Bedrock.

In this post, we walk through how to get started with Gemma 4 models on Amazon Bedrock. We cover the capabilities supported by these models, the service tiers available, how on-demand inference scales to handle your workloads, and the different APIs you can use to access them. With these models, you can build multimodal agents, lightweight applications, document understanding pipelines, and software engineering workflows on Amazon Bedrock. Your prompts and completions are not used to train any models, and your content is not shared with third parties.

The Gemma 4 family on Amazon Bedrock spans a 2.3B-effective-parameter compact model up to a 30.7B-parameter dense model, giving you a choice of variants to match different cost and latency profiles. All variants support a built-in reasoning mode, native function calling for agentic workflows, multimodal input that combines text and image, and out-of-the-box support for over 35 languages with pre-training across 140+. Because the models are open-weight, you can independently evaluate the model architecture and training methodology, benchmark on your own workloads, and fine-tune on proprietary data when customization is required. You can access the models through a fully managed AWS service without provisioning infrastructure, hosting model weights, or operating inference stacks yourself.anno

For the latest list of supported models, refer to the Amazon Bedrock model catalog.