# Resource-based engineering: Auto-tuning for Temporal Workers

DevFeed: [Resource-based engineering: Auto-tuning for Temporal Workers](<https://devfeed.tech/articles/resource-based-engineering-auto-tuning-for-temporal-workers-35962.md>)

Original publisher: [Read original article](<https://temporal.io/blog/resource-based-auto-tuning-for-workers>)

Author: Spencer Judge

Published: 2024-11-12T07:00:00Z

Content type: article

Language: en

Sources: [Temporal Blog](<https://devfeed.tech/sources/temporal-blog.md>)

Topics: [Workers](<https://devfeed.tech/topics/workers.md>), [Monitoring](<https://devfeed.tech/topics/monitoring.md>), [autoscaling](<https://devfeed.tech/topics/autoscaling.md>)

Tags: [capacity](<https://devfeed.tech/tags/capacity.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [performance](<https://devfeed.tech/tags/performance.md>), [product-news](<https://devfeed.tech/tags/product-news.md>), [scaling](<https://devfeed.tech/tags/scaling.md>), [workers](<https://devfeed.tech/tags/workers.md>)

## AI overview

The article introduces resource-based auto-tuning for Temporal Workers. It explains how Workers, task queues, and task slots relate to capacity, and describes horizontal and vertical scaling based on workload backlog and available capacity.

## Source excerpt

Explore how resource-based engineering powers auto-tuning for Temporal Workers, streamlining performance and management.