Azure Databricks · Rate Limits

Microsoft Azure Databricks Rate Limits

Azure Databricks REST APIs apply per-workspace and per-API rate limits. Most APIs target a few hundred requests per second per workspace; Jobs API and Files API have lower per-second targets. Some APIs (Statement Execution, Model Serving) use endpoint-specific limits. SCIM and account-level APIs are throttled separately.

Microsoft Azure Databricks Rate Limits is the machine-readable rate-limit profile for Azure Databricks on the APIs.io network, in the API Commons Rate Limits shape.

It captures 5 rate-limit definitions, measuring requests_per_second, concurrent_runs, concurrent_statements, and queries_per_second.

Limit state is signalled in the retryAfter response header, and an exhausted limit returns HTTP 429.

This profile was written by API Evangelist and has not been verified against a page the provider published; treat every limit as unconfirmed until a dated transcription replaces it.

The profile also includes 4 backoff/retry policies defined and response codes documented for throttled and serviceUnavailable.

Tagged areas include Rate Limiting, Analytics, and Microsoft Azure.

5 Limits Exhausted: HTTP 429 retryAfter
Method: generated, dated 2026-05-05.
Written by API Evangelist on 2026-05-05 and not verified against a page the provider published. Treat every limit as unconfirmed until a dated transcription replaces it.
View Source
Rate LimitingAnalyticsMicrosoft Azure

Limits

Workspace API requests workspace
requests_per_second
see Databricks limits page (typical hundreds RPS per workspace)
Jobs API runs workspace
concurrent_runs
workspace-configurable max concurrent runs
Statement Execution API workspace
concurrent_statements
see Statement Execution API limits
Model Serving endpoint QPS endpoint
queries_per_second
configurable per endpoint with concurrency budget
SCIM (account) account
requests_per_second
see SCIM provisioning limits

Policies

Honor Retry-After
When the API returns 429, wait the duration specified in Retry-After before retrying.
Backoff with jitter
Apply exponential backoff with jitter to avoid thundering herds against the workspace control plane.
Bulk endpoints over loops
Prefer batch endpoints (e.g. Jobs runs list with filters) and Delta Live Tables over polling individual resources.
Workspace partitioning
Heavy multi-tenant workloads should be partitioned across multiple workspaces to avoid control-plane throttling.

Sources

Work with this as data

Every rate limit here is available over the APIs.io API and to AI agents over MCP.

MCP server

One button, every client — Claude, Cursor, VS Code and the rest.

https://apis.io/mcp

Tools for rate limits

4 MCP tools reach this
  • find_rate_limitsBrowse and filter every rate limit in the catalog.
  • apis_io_searchSTART HERE — APIs, providers and tags for one query, each with its total.
  • resolveTurn a domain, URL or GitHub org into the provider it belongs to.
  • find_cohortsEvery scored population of providers in the catalog.
All 92 tools →

Call it yourself

curl for this page
This rate limit 
curl "https://apis.io/api/v1/rate-limits/microsoft-azure-databricks-rate-limits"
All rate limits 
curl "https://apis.io/api/v1/rate-limits?limit=25"

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