Cloudflare Workers: Error 1102: Worker exceeded resource limits
The Worker used more CPU time or memory than its plan allows and was stopped mid-request. CPU time counts only while your code is actually computing, so waiting on fetch() is free, but parsing a large JSON body, hashing or rendering a big page is not. A Worker that runs fine in development can hit this in production on larger inputs.
Quick fix
Read the commands before running them. Anything that restarts a service, deletes data or changes permissions should be tried on a non-production system first.
# Watch real CPU time and outcomes per request
npx wrangler tail --format pretty
# Dashboard: Workers > your worker > Metrics > CPU time percentiles
# Paid plans can raise the CPU limit per invocation (wrangler.toml)
# [limits]
# cpu_ms = 300000
# Reduce the work per request instead where you can:
# stream responses rather than building them in memory
# cache computed results with the Cache API or KV
# move heavy jobs to a Queue consumer
# Memory is capped at 128 MB per isolate regardless of planHow to diagnose Serverless errors
Serverless errors are shaped by hard platform limits: execution duration, deployment package size, memory, and concurrency. Unlike a server you control, these cannot be tuned away. The fix is to restructure the workload. VPC-attached functions add a second class of problem, because they lose default internet access and depend on ENI capacity and NAT configuration.
If the quick fix above does not resolve it, work through these steps. They apply to this whole class of error, not just to this one message, which is usually what saves the time.
- Check the platform limit before changing code: timeout, package size and memory are all documented, and the error is usually the limit being reported literally.
- For timeouts, add timing logs around each external call. A function that times out is nearly always waiting on one downstream dependency.
- Reduce package size with layers, tree-shaking, and by excluding dev dependencies and the provider SDK that the runtime already includes.
- For VPC functions with no internet access, confirm there is a NAT Gateway or the relevant VPC endpoints. A function in a private subnet cannot reach the public internet by default.
- Measure cold start separately from warm invocation in your metrics, and use provisioned concurrency only after confirming cold starts are the actual problem.
Tools worth reaching for
CloudWatch Logs Insightsaws lambda get-function-configurationX-Ray / distributed tracingsam local invoke
Authoritative references
Primary documentation for this error, worth reading before applying any fix in production.
Related Serverless errors
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Something missing or wrong?
This entry is maintained by hand. If the fix is out of date, incomplete, or you have a better one, email a correction and it will be reviewed.