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Today, we’re introducing Amazon Bedrock Marketplace, a new capability that gives you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. With this launch, you can now discover, test, and deploy new models from enterprise providers such as IBM and Nvidia, specialized models such as Upstages’ Solar Pro for Korean language processing, and Evolutionary Scale’s ESM3 for protein research, alongside Amazon Bedrock general-purpose FMs from providers such as Anthropic and Meta.
Models deployed with Amazon Bedrock Marketplace can be accessed through the same standard APIs as the serverless models and, for models which are compatible with Converse API, be used with tools such as Amazon Bedrock Agents and Amazon Bedrock Knowledge Bases.
As generative AI continues to reshape how organizations work, the need for specialized models optimized for specific domains, languages, or tasks is growing. However, finding and evaluating these models can be challenging and costly. You need to discover them across different services, build abstractions to use them in your applications, and create complex security and governance layers. Amazon Bedrock Marketplace addresses these challenges by providing a single interface to access both specialized and general-purpose FMs.
Using Amazon Bedrock Marketplace
To get started, in the Amazon Bedrock console, I choose Model catalog in the Foundation models section of the navigation pane. Here, I can search for models that help me with a specific use case or language. The results of the search include both serverless models and models available in Amazon Bedrock Marketplace. I can filter results by provider, modality (such as text, image, or audio), or task (such as classification or text summarization).
In the catalog, there are models from organizations like Arcee AI, which builds context-adapted small language models (SLMs), and Widn.AI, which provides multilingual models.
For example, I am interested in the IBM Granite models and search for models from IBM Data and AI.
I select Granite 3.0 2B Instruct, a language model designed for enterprise applications. Choosing the model opens the model detail page where I can see more information from the model provider such as highlights about the model, pricing, and usage including sample API calls.
This specific model requires a subscription, and I choose View subscription options.
From the subscription dialog, I review pricing and legal notes. In Pricing details, I see the software price set by the provider. For this model, there are no additional costs on top of the deployed infrastructure. The Amazon SageMaker infrastructure cost is charged separately and can be seen in Amazon SageMaker pricing.
To proceed with this model, I choose Subscribe.
After the subscription has been completed, which usually takes a few minutes, I can deploy the model. For Deployment details, I use the default settings and the recommended instance type.
I expand the optional Advanced settings. Here, I can choose to deploy in a virtual private cloud (VPC) or specify the AWS Identity and Access Management (IAM) service role used by the deployment. Amazon Bedrock Marketplace automatically creates a service role to access Amazon Simple Storage Service (Amazon S3) buckets where the model weights are stored, but I can choose to use an existing role.
I keep the default values and complete the deployment.
After a few minutes, the deployment is In Service and can be reviewed in the Marketplace deployments page from the navigation pane.
There, I can choose an endpoint to view details and edit the configuration such as the number of instances. To test the deployment, I choose Open in playground and ask for some poetry.
I can also select the model from the Chat/text page of the Playground using the new Marketplace category where the deployed endpoints are listed.
In a similar way, I can use the model with other tools such as Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, Amazon Bedrock Prompt Management, Amazon Bedrock Guardrails, and model evaluations, by choosing Select Model and selecting the Marketplace model endpoint.
The model I used here is text-to-text, but I can use Amazon Bedrock Marketplace to deploy models with different modalities. For example, after I deploy Stability AI Stable Diffusion 3.5 Large, I can run a quick test in the Amazon Bedrock Image playground.
The models I deployed are now available through the Amazon Bedrock InvokeModel API. When a model is deployed, I can use it with the AWS Command Line Interface (AWS CLI) and any AWS SDKs using the endpoint Amazon Resource Name (ARN) as model ID.
For chat-tuned text-to-text models, I can also use the Amazon Bedrock Converse API, which abstracts model differences and enables model switching with a single parameter change.
Things to know
Amazon Bedrock Marketplace is available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Paris), and South America (São Paulo).
With Amazon Bedrock Marketplace, you pay a software fee to the third-party model provider (which can be zero, as in the previous example) and a hosting fee based on the type and number of instances you choose for your model endpoints.
Start browsing the new models using the Model catalog in the Amazon Bedrock console, visit the Amazon Bedrock Marketplace documentation, and send feedback to AWS re:Post for Amazon Bedrock. You can find deep-dive technical content and discover how our Builder communities are using Amazon Bedrock at community.aws.
— Danilo
