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  sources:
    - debugging-guide-for-model-serving-databricks-on-aws.md
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  aliases:
    - provisioned-concurrency-and-parallel-requests-in-model-serving
    - Parallel Requests in Model Serving and Provisioned Concurrency
    - PCAPRIMS
  citations:
    - file: debugging-guide-for-model-serving-databricks-on-aws.md
title: Provisioned Concurrency and Parallel Requests in Model Serving
description: Managing provisioned concurrency quotas and parallel request limits in Databricks Model Serving, including error handling and scaling strategies.
tags:
  - model-serving
  - scaling
  - concurrency
  - quotas
  - databricks
timestamp: "2026-06-19T09:56:29.399Z"
---

---
title: "Provisioned Concurrency and Parallel Requests in Model Serving"
summary: "Understanding and managing provisioned concurrency and parallel request limits in Databricks Model Serving to ensure reliable endpoint performance."
sources:
  - debugging-guide-for-model-serving-databricks-on-aws.md
kind: concept
createdAt: "2026-06-18T08:12:08.590Z"
updatedAt: "2026-06-18T08:12:08.590Z"
tags:
  - model-serving
  - concurrency
  - limits
  - troubleshooting
aliases:
  - provisioned-concurrency-and-parallel-requests-in-model-serving
  - pcaprims
confidence: 0.9
provenanceState: extracted
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---

# Provisioned Concurrency and Parallel Requests in Model Serving

**Provisioned Concurrency** and **Parallel Requests** are two distinct but related capacity limits in [Databricks Model Serving](/concepts/databricks-model-serving.md). When a serving endpoint receives traffic, **provisioned concurrency** controls how many simultaneous inference requests a single endpoint can handle, while the **parallel requests limit** is a workspace-wide cap on the total number of concurrent requests that can be sent to all endpoints in that workspace.

## Provisioned Concurrency

Provisioned concurrency is the number of model replicas (or "served entities") that an endpoint has been configured to run at any given time. Each replica can typically process one request at a time, so the provisioned concurrency effectively sets the throughput ceiling for that endpoint. ^[debugging-guide-for-model-serving-databricks-on-aws.md]

### Exceeding the Provisioned Concurrency Quota

If the workspace has used all of its provisioned concurrency quota across its endpoints, new endpoint creation or scaling may fail with a `Workspace exceeded provisioned concurrency quota` error. ^[debugging-guide-for-model-serving-databricks-on-aws.md]

**Causes:**

- The sum of provisioned concurrency across all active endpoints has reached the workspace limit. ^[debugging-guide-for-model-serving-databricks-on-aws.md]

**Resolution:**

- Free up quota by deleting or stopping unused endpoints. ^[debugging-guide-for-model-serving-databricks-on-aws.md]
- Request a quota increase from your Databricks account team, providing your workspace ID. ^[debugging-guide-for-model-serving-databricks-on-aws.md]

### Too Many Concurrent Requests

If the endpoint's provisioned concurrency is too low for the actual traffic volume, you may see a `429 Too many concurrent requests` error. ^[debugging-guide-for-model-serving-databricks-on-aws.md]

**Causes:**

- Incoming request rate exceeds the sum of all replica capacities. ^[debugging-guide-for-model-serving-databricks-on-aws.md]

**Resolution:**

- **Enable autoscaling:** automatically adds replicas up to a configured maximum. ^[debugging-guide-for-model-serving-databricks-on-aws.md]
- **Manually increase provisioned concurrency:** set a higher static replica count. ^[debugging-guide-for-model-serving-databricks-on-aws.md]

## Parallel Requests Limit

The parallel requests limit is a workspace-wide cap on the total number of concurrent HTTP requests that can be in flight to any endpoint in that workspace. This limit is independent of provisioned concurrency — even if each endpoint has idle capacity, the aggregate number of simultaneous client requests cannot exceed this cap. ^[debugging-guide-for-model-serving-databricks-on-aws.md]

### Exceeding the Parallel Requests Limit

If the workspace receives more parallel requests than its limit, you may see a `429 Exceeded max number of parallel requests` error. ^[debugging-guide-for-model-serving-databricks-on-aws.md]

**Causes:**

- Too many clients or too many concurrent application threads sending requests simultaneously. ^[debugging-guide-for-model-serving-databricks-on-aws.md]

**Resolution:**

- **Move to [Route Optimized Endpoints](/concepts/route-optimized-endpoints.md):** route-optimized endpoints have no parallel requests limit. ^[debugging-guide-for-model-serving-databricks-on-aws.md]
- **Reduce client concurrency:** throttle the number of simultaneous clients or threads. ^[debugging-guide-for-model-serving-databricks-on-aws.md]
- **Request a limit increase:** contact your Databricks representative to raise the workspace-level cap. ^[debugging-guide-for-model-serving-databricks-on-aws.md]

## Distinction Between the Two

| Dimension | Scope | Controls | Error |
|-----------|-------|---------|-------|
| Provisioned Concurrency | Per-endpoint | How many replicas are active | `Workspace exceeded provisioned concurrency quota` |
| Parallel Requests | Workspace-wide | Total concurrent requests across all endpoints | `Exceeded max number of parallel requests` |

Provisioned concurrency is about **capacity** — how many requests an endpoint can serve at once. The parallel requests limit is about **aggregate load** — how many requests the workspace can accept at once before being overwhelmed. ^[debugging-guide-for-model-serving-databricks-on-aws.md]

## Best Practices

- **Monitor usage** via [Inference Tables](/concepts/inference-tables.md) to see request patterns. ^[debugging-guide-for-model-serving-databricks-on-aws.md]
- **Set realistic provisioned concurrency** based on peak traffic, not average. ^[debugging-guide-for-model-serving-databricks-on-aws.md]
- **Prefer route-optimized endpoints** for high‑traffic production workloads to avoid the parallel requests limit entirely. ^[debugging-guide-for-model-serving-databricks-on-aws.md]
- **Use pre-deployment validation** to catch configuration issues before they affect production. ^[debugging-guide-for-model-serving-databricks-on-aws.md]

## Related Concepts

- [Model Serving Endpoints](/concepts/model-serving-endpoint.md)
- Autoscaling
- [Route Optimized Endpoints](/concepts/route-optimized-endpoints.md)
- [Inference Tables](/concepts/inference-tables.md)
- [Pre-deployment Validation](/concepts/pre-deployment-validation-for-model-serving.md)
- Model Serving Limits and Regions

## Sources

- debugging-guide-for-model-serving-databricks-on-aws.md

# Citations

1. [debugging-guide-for-model-serving-databricks-on-aws.md](/references/debugging-guide-for-model-serving-databricks-on-aws-67072c6a.md)
