---
type: concept
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  pageDirectory: concepts
  sources:
    - debugging-guide-for-model-serving-databricks-on-aws.md
  confidence: 0.9
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  aliases:
    - route-optimized-endpoints
    - ROE
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title: Route Optimized Endpoints
description: A model serving endpoint type that removes the workspace-level parallel requests limit (429 errors), recommended as a solution for high-concurrency workloads.
tags:
  - model-serving
  - endpoints
  - scaling
timestamp: "2026-06-18T11:44:17.674Z"
---

---
title: Route Optimized Endpoints
summary: Route optimized endpoints are a type of model serving endpoint on Databricks that remove the workspace-level parallel requests limit, providing better scalability for high‑traffic AI inference workloads.
sources:
  - debugging-guide-for-model-serving-databricks-on-aws.md
kind: concept
createdAt: "2026-06-18T11:08:33.206Z"
updatedAt: "2026-06-18T11:08:33.206Z"
tags:
  - databricks
  - model-serving
  - scaling
aliases:
  - route-optimized-endpoints
confidence: 0.5
provenanceState: extracted
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---

# Route Optimized Endpoints

**Route optimized endpoints** are a Databricks [Model Serving](/concepts/model-serving.md) endpoint type designed to handle high‑throughput inference workloads without being subject to the workspace‑level limit on the maximum number of parallel requests. ^[debugging-guide-for-model-serving-databricks-on-aws.md]

## Overview

When a workspace exceeds its allowed number of simultaneous inference requests, Databricks returns a `429 Exceeded max number of parallel requests` error. Databricks recommends moving to route optimized endpoints as the primary solution for this limit, because the parallel requests restriction does not apply to them. ^[debugging-guide-for-model-serving-databricks-on-aws.md]

Route optimized endpoints are especially useful for applications that send a large volume of concurrent requests, such as real‑time chatbots, recommendation engines, or AI agents backed by GenAI agents.

## Benefits

- **No parallel‑requests quota.** The workspace limit that triggers the `429` error is removed, allowing more concurrent inference calls without needing a quota increase. ^[debugging-guide-for-model-serving-databricks-on-aws.md]
- **Improved scalability.** By eliminating this bottleneck, route optimized endpoints can better absorb traffic spikes and support higher throughput.

## When to Use

Consider using route optimized endpoints if:

- You frequently receive the error `Exceeded max number of parallel requests. Please contact your Databricks representative to increase the limit`. ^[debugging-guide-for-model-serving-databricks-on-aws.md]
- You need to serve models with unpredictable or bursty request patterns.
- You want to avoid the administrative overhead of requesting a concurrency quota increase from the Databricks account team.

## Related Concepts

- [Model Serving](/concepts/model-serving.md) — The Databricks platform for deploying machine learning models as REST endpoints.
- [Provisioned Concurrency](/concepts/provisioned-concurrency.md) — The number of concurrent model replicas available to handle requests. Route optimized endpoints still respect per‑endpoint provisioned concurrency limits.
- [Inference Tables](/concepts/inference-tables.md) — Logged request/response data that can help diagnose failed requests on any endpoint type.
- Workspace exceeded parallel requests limit — The specific error that route optimized endpoints help resolve.

## 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)
