Workflow Caching in Self-Hosted Roboflow Inference

Brian Guilfoyle's article, published on June 18, 2026, addresses the common issue of stale results when updating Roboflow Workflows deployed to the edge with a self-hosted inference server. This situation often arises due to caching mechanisms, where the server continues to serve an older workflow definition despite updates. The article explains how caching works in the context of three dependencies: the server image, the workflow definition, and model weights, each of which updates independently. The workflow definition is stored in a cache with a time-to-live (TTL) and may not immediately reflect changes made on the Roboflow platform. To address this, users can bypass the cache by setting `use_cache=False` or by restarting the server. The article also suggests using version tags in workflow outputs to easily identify the version being used. Guilfoyle emphasizes that seemingly stale predictions are usually a result of a local dependency being out of sync rather than a system failure, and can be resolved with patience or a deliberate refresh.

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