Polylane for Cloudflare

Polylane for Cloudflare positions itself as an AI on‑call engineer that keeps your Workers and the services they rely on in a healthy state. The platform pulls metrics, logs and tracing data from every Cloudflare resource linked to your application and applies automated fixes when an abnormal pattern appears. The integration begins with a single Cloudflare API token; Polylane pre‑selects the necessary read‑only permissions and then continuously monitors the account.

When a Worker experiences an error, a sudden spike in normal CPU time, or the 99th‑percentile latency jumps more than 18 times compared to its baseline, Polylane flags the event automatically. It records every deploy and configuration change that preceded the issue, then scans the code repository associated with the Worker. If the problem originates in code, the tool opens a pull request that writes the corrective patch to your repository. The pull request contains the original code location, the proposed change, and a concise description of the issue. For non‑code errors, such as a broken binding, a D1 database without backups, or a queue that is stuck retrying, Polylane identifies the affected resource and suggests the appropriate fix. The dashboard surfaces all of these incidents in a unified view, grouped by resource type and severity.

Polylane builds a graph of every resource your Worker binds to: Workers, Durable Objects, D1 databases, R2 buckets, KV namespaces, Zones, Queues, Tunnels, and more. This graph shows the DNS relationships between bindings, making it easier to trace a failure back to its root component. The platform also watches for risky configurations, such as missing observability or missing backups, and flags them for review. Users can connect their preferred coding agents, Claude, GitHub Copilot, OpenCode, etc., to query Cloudflare data while working on fixes. The service offers short guide articles on testing Cron Triggers, deploying Vite apps with Void, rolling back Workers, and profiling CPU and memory usage, providing practical context for the monitoring data it collects.

Key features

• 18‑fold P99 latency anomaly detection across Workers and bound resources
• End‑to‑end monitoring of metrics, logs, traces and configuration history
• Pull‑request creation with code fixes written directly to the repository
• Display of a full resource binding graph, including DNS relationships
• Built‑in support for multiple AI coding agents (Claude, Copilot, OpenCode, etc.)