Ragas · Rate Limits

Ragas Rate Limits

Not applicable from the Ragas side — there is no hosted Ragas API. Effective rate limits are entirely determined by the LLM and embedding providers Ragas calls (OpenAI, Anthropic, Azure OpenAI, Cohere, etc.). Ragas exposes concurrency controls and caching helpers to keep evaluation runs within those upstream limits.

Ragas Rate Limits is the machine-readable rate-limit profile for Ragas on the APIs.io network, conforming to the API Commons Rate Limits specification.

It captures 2 rate-limit definitions, measuring n/a and requests.

The profile also includes 2 backoff/retry policies defined and response codes documented for throttled.

Tagged areas include AI Evaluation, LLM, RAG, Open Source, and Python.

2 Limits Throttle: 429
AI EvaluationLLMRAGOpen SourcePythonMetricsRate LimitingQuotasThrottling

Limits

N/A (no Ragas-hosted API) n/a
n/a
library only
Ragas runs locally; rate limits come from the model provider.
Upstream LLM provider external
requests
provider-defined
OpenAI/Anthropic/etc. limits apply during evaluation runs.

Policies

Concurrency Control
Use Ragas RunConfig to set max_workers and enable caching to stay within upstream model limits.
Caching
Cache evaluation results and embeddings to avoid repeat upstream calls.

Sources