Databricks Foundation Model Serving
Researched 4d agoPlatformTier 2Databricks
Databricks Foundation Model Serving exposes 13 tracked models (7 with output token pricing in seed data). Task coverage across this catalog includes coding, rag, and agents; open any model detail page for benchmarks, batch tiers, and migration prompts.
Portfolio context: 6 decision-task tags, 13 catalog rows, latest research stamp 2026-05-19.
Use this portfolio page for
- Teams comparing token and batch economics on this surface
- Operators routing coding, rag, and agents workloads through this API
Do not stop here for
- Final benchmark picks without opening the relevant model detail page
Catalog rows
13
Models linked to this provider in seed data
Priced output routes
7
Rows with token_out in seed data
Cheapest output
$0.500
MPT 7B on this route
Batch-ready SKUs
0
No batch pricing tracked
Latest catalog ship
2025-01-20
488d since dated release field
Freshness
2026-05-19
Researched 4d ago
Catalog release signal
Latest ISO-dated model.release in this catalog is 2025-01-20 (488d ago).
Where this host wins
- Coding: 6 tracked models with SWE-bench / HumanEval-style scores.
- RAG: 4 tracked models with ruler / needle retrieval benchmarks.
- Agentic: 1 tracked model with BFCL, tau-bench, and SWE-bench tool-use coverage.
- Long-context: 4 tracked models with context-token or InfiniteBench-class signal.
Getting started
Official entry points from seed metadata — confirm quotas and regions in vendor docs.
Compliance notes (verbatim seed excerpts)
Not yet verified from seed copy — no SOC/ISO/HIPAA-class sentences detected to quote verbatim.
Platform Overview
Databricks offers a comprehensive AI platform that integrates a lakehouse model, combining the flexibility of data lakes with the management capabilities of data warehouses. The platform features a natural language interface for conversational data querying, automated infrastructure management for optimized performance, and robust governance tools ensuring data privacy and compliance. It supports a wide range of functionalities including data engineering, real-time streaming, and a marketplace for data sharing, while enabling seamless collaboration among data scientists, engineers, and DevOps teams . The platform's capabilities extend to advanced machine learning operations (MLOps), facilitating the entire lifecycle of AI model development. It includes built-in support for popular libraries like TensorFlow and PyTorch, tools for monitoring data quality and model performance, and automated workflows for building production-ready ETL pipelines. The platform also integrates with large language models (LLMs) for generative AI applications, emphasizing cost efficiency and ease of use. This comprehensive suite of tools empowers organizations to effectively leverage AI while maintaining control over their data and models .
Available Models(13)
View all →| Model | Input (per 1M) | Output (per 1M) | Type |
|---|---|---|---|
| DeepSeek R1 | Serverless | ||
| Llama 3.1 405B Instruct | Provisioned | ||
| Llama 3.1 70B Instruct | Provisioned | ||
| Llama 3.1 8B Instruct | Provisioned | ||
| Llama 3 70B Instruct | $1 | $3 | Serverless |
| Llama 3 8B Instruct | Provisioned | ||
| DBRX | Provisioned | ||
| DBRX Instruct | $0.75 | $2.25 | ServerlessProvisioned |
| Mixtral 8x7B | $0.5 | $1 | Serverless |
| Llama 2 13B Chat | $0.95 | $0.95 | Serverless |
Platform Details
Organization
Databricks offers a comprehensive Data Intelligence Platform that unifies data, analytics, and AI capabilities. Their platform, known as the Databricks Lakehouse, combines the best features of data lakes and data warehouses, enabling organizations to handle large-scale data processing, analytics, and machine learning workloads in a single, unified environment. Key features of Databricks' AI platform include: 1. Apache Spark integration: As the creators of Apache Spark, Databricks provides optimized performance for big data processing and analytics. 2. Delta Lake: An open-source storage layer that brings reliability to data lakes, ensuring data quality and consistency. 3. MLflow: An open-source platform for managing the machine learning lifecycle, including experimentation, reproducibility, and deployment. 4. Collaborative notebooks: Interactive environments for data scientists and analysts to work together on data exploration, model development, and visualization. 5. AutoML: Automated machine learning capabilities to streamline the model development process. 6. Generative AI support: Tools and frameworks for developing and deploying generative AI models. 7. Data governance: Unity Catalog provides centralized governance and security controls across the entire data estate. 8. Scalable infrastructure: Cloud-native architecture that allows for elastic scaling of compute resources. Databricks' platform is designed to democratize data and AI, making it accessible to organizations of all sizes. It's used by over 10,000 organizations worldwide, including more than 50% of the Fortune 500 companies, for various use cases such as data engineering, machine learning, and business analytics.