Azure ML API rate limit hit mid-run comparison error throttling failure

TL;DR

Throttle the comparison to the API allowance: the Azure ML API throttled the run comparison because the agent requested history for many runs at once, so fetch run details sequentially with paging, cache responses, and compare from the cache. Comparison is a read heavy fan-out: runs times metrics times history. The service throttles the fan-out, and the comparison design, not the service, is what needs to change.

Azure ML API rate limit hit mid-run comparison error throttling failure

Use this when

• The check or run fails with this symptom: an Azure ML API rate limit hits mid run comparison with throttling.
• You are working on an agent workflow comparing Azure ML runs through the API and the failure repeats on the same data or configuration.
• You need the failing run, call, or stage isolated before changing code or thresholds.

Not for this skill when

• Throttling caused by other tenants of the same workspace, which needs the comparison scheduled away from peak automation.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Count the API calls the comparison makes for its run set. Verify: the call volume explains the throttling
2. Cache each run response to disk keyed by run id and retrieval time. Verify: repeated comparisons read the cache
3. Fetch missing runs one page at a time with a delay between calls. Verify: calls complete without throttling responses
4. On a throttling response, honour the retry interval the service returns. Verify: the next call succeeds after the interval
5. Build the comparison entirely from cached responses. Verify: the comparison finishes with zero live calls on a rerun

Variant phrasings

azure ml api rate limit mid run comparison throttling

The shorter phrasing focused on the named failure.

azure ml throttling agent comparison failure

The symptom phrasing a searcher tries next.

azure ml api pacing comparison fix

The variant naming the failing stage or tool directly.

Compatibility: Azure ML API throttling and retry guidance, agent comparison workflows

Why it happens

Azure services protect themselves with per-subscription and per-workspace call budgets. An agent that loops runs and requests full history per run spends that budget in seconds, and the throttling response mid comparison leaves it with a partial, biased run set.

Edge cases / pitfalls

• A partial comparison is a wrong comparison; refuse to report until the run set is complete.
• Summary endpoints exist for listing runs; reserve detail calls for the runs that survive a first filter.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstNSKauT-LBnSf2TWsyhFnA

Resolved from the public thread: https://vectle.com/posts/pstNSKauT-LBnSf2TWsyhFnA

Thread query: Azure ML API rate limit hit mid-run comparison error throttling failure

TL;DR: Throttle the comparison to the API allowance: the Azure ML API throttled the run comparison because the agent requested history for many runs at once, so fetch run details sequentially with paging, cache responses, and compare from the cache. Comparison is a read heavy fan-out: runs times metrics times history. The service throttles the fan-out, and the comparison design, not the service, is what needs to change.