Databricks Foundation Model APIs

This article provides an overview of the Foundation Model APIs in Databricks. It includes requirements for use, supported models, and limitations.

What are Databricks Foundation Model APIs?

Databricks Model Serving now supports Foundation Model APIs which allow you to access and query state-of-the-art open models from a serving endpoint. With Foundation Model APIs, you can quickly and easily build applications that leverage a high-quality generative AI model without maintaining your own model deployment.

The Foundation Model APIs are provided in two pricing modes:

  • Pay-per-token: This is the easiest way to start accessing foundation models on Databricks and is recommended for beginning your journey with Foundation Model APIs. This mode is not designed for high-throughput applications or performant production workloads.

  • Provisioned throughput: This mode is recommended for all production workloads, especially those that require high throughput, performance guarantees, fine-tuned models, or have additional security requirements. Provisioned throughput endpoints are available with compliance certifications like HIPAA.

See Use Foundation Model APIs for guidance on how to use these two modes and the supported models.

Using the Foundation Model APIs you can:

  • Query a generalized LLM to verify a project’s validity before investing more resources.

  • Query a generalized LLM in order to create a quick proof-of-concept for an LLM-based application before investing in training and deploying a custom model.

  • Use a foundation model, along with a vector database, to build a chatbot using retrieval augmented generation (RAG).

  • Replace proprietary models with open alternatives to optimize for cost and performance.

  • Efficiently compare LLMs to see which is the best candidate for your use case, or swap a production model with a better performing one.

  • Build an LLM application for development or production on top of a scalable, SLA-backed LLM serving solution that can support your production traffic spikes.



For provisioned throughput workloads that use the DBRX Base model, see Foundation Model APIs limits for region availability.

Use Foundation Model APIs

You have multiple options for using the Foundation Model APIs.

The APIs are compatible with OpenAI, so you can even use the OpenAI client for querying. You can also use the UI, the Foundation Models APIs Python SDK, the MLflow Deployments SDK, or the REST API for querying supported models. Databricks recommends using the MLflow Deployments SDK or REST API for extended interactions and the UI for trying out the feature.

See Query foundation models for scoring examples.

Pay-per-token Foundation Model APIs


This feature is in Public Preview.

Pay-per-tokens models are accessible in your Databricks workspace, and are recommended for getting started. To access them in your workspace, navigate to the Serving tab in the left sidebar. The Foundation Model APIs are located at the top of the Endpoints list view.

Serving endpoints list

The following table summarizes the supported models for pay-per-token. See Supported models for pay-per-token for additional model information.

If you want to test out and chat with these models you can do so using the AI Playground. See Chat with supported LLMs using AI Playground.


Task type


DBRX Instruct









Mixtral-8x7B Instruct



MPT 7B Instruct



MPT 30B Instruct



BGE Large (English)



Provisioned throughput Foundation Model APIs

Provisioned throughput is generally available and Databricks recommends provisioned throughput for production workloads. Provisioned throughput provides endpoints with optimized inference for foundation model workloads that require performance guarantees. See Provisioned throughput Foundation Model APIs for a step-by-step guide on how to deploy Foundation Model APIs in provisioned throughout mode.

Provisioned throughput support includes:

  • Base models of all sizes, such as DBRX Base. Base models can be accessed using the Databricks Marketplace, or you can alternatively download them from Hugging Face or another external source and register them in the Unity Catalog. The latter approach works with any fine-tuned variant of the supported models, irrespective of the fine-tuning method employed.

  • Fine-tuned variants of base models, such as LlamaGuard-7B. This includes models that are fine-tuned on proprietary data.

  • Fully custom weights and tokenizers, such as those trained from scratch or continued pretrained or other variations using the base model architecture (such as CodeLlama, Yi-34B-Chat, or SOLAR-10.7B).

The following table summarizes the supported model architectures for provisioned throughput.

Model architecture

Task types



Chat or Completion

See Foundation Model APIs limits for region availability.

Meta Llama 3

Chat or Completion

Meta Llama 2

Chat or Completion


Chat or Completion


Chat or Completion


Chat or Completion

BGE v1.5 (English)