Databricks publishes 57 APIs on the APIs.io network, described in the catalog as a “collection of Databricks REST APIs for managing workspaces, clusters, jobs, and data operations.” That is not a single product with a few endpoints bolted on. It is a control surface for an entire lakehouse, sliced into dozens of narrowly-scoped APIs that each own one job.
What’s in the surface
The catalog carries a Databricks OpenAPI definition alongside labeled specs for the Clusters API, the Jobs API, and the Workspace API. Those are the load-bearing three: Clusters manages the compute that runs the work, Jobs is the primary mechanism for running automated workloads including notebooks, JARs, and Python scripts, and Workspace handles importing, exporting, and deleting notebooks and folders.
Everything else fans out from there. Unity Catalog alone accounts for a long run of the entries: Catalogs, Schemas, Tables, Volumes, Functions, Grants, External Locations, Storage Credentials, Metastores, and Connections. The SQL side brings SQL Warehouses, Statement Execution, Queries, and Alerts. The AI side brings Serving Endpoints, Vector Search Indexes and Endpoints, Model Versions, Registered Models, Experiments, and Online Tables. Delta Sharing shows up as Shares, Recipients, Providers, and Clean Rooms.
What’s interesting
Databricks has decomposed its platform into governance-shaped pieces rather than feature-shaped ones. The presence of a dedicated Grants API, Permissions API, Cluster Policies API, IP Access Lists API, and Token Management API tells you this is a surface built for administrators who need to constrain and audit access, not just call endpoints. Unity Catalog being exposed as its own family of APIs is the clearest signal: data governance is treated as a first-class control plane, on equal footing with the compute and SQL planes.
The catalog also records a Databricks Agentic Access profile: 25 operations, 15 acting, 2 human-in-the-loop. That last number matters. Databricks isn’t declaring every operation agent-safe; it is flagging which ones should stop and ask a human. For a platform whose Jobs and Clusters APIs can spin up, edit, and permanently delete cloud compute, that restraint is the right posture.
The tagged areas back this up: AI, Analytics, Apache Spark, Big Data, Clean Rooms, Data Governance, Delta Lake, Delta Sharing, Lakehouse, Machine Learning, MLflow, Model Serving, Unity Catalog, and Vector Search. Read the tag list top to bottom and you can see the whole data-intelligence arc, from raw Spark compute to governed sharing to vector search for AI workloads.
Takeaway
Databricks on APIs.io is a case study in what a mature data platform looks like as an API surface: not one big API, but 57 small ones organized around compute, SQL, AI, and governance planes. If you are evaluating how to structure a broad platform for programmatic and agentic access, the Databricks provider page is worth walking end to end.