We keep running into limits with Apigee

Understood — “running into limits with Apigee” can mean a few different things, and the fix depends on which limit you’re hitting.

Common Apigee limits include:

• Traffic / throughput limits: requests per second, concurrent connections
• Payload limits: request/response size
• Quota / SpikeArrest limits: policy-enforced caps
• Timeouts: target backend taking too long
• Environment / org limits: resources, deployments, shared flows, proxies, etc.
• Analytics / logging limits: volume or retention constraints
• Runtime performance: CPU/memory issues in message processors or hybrid runtime

A few ways people usually address this:

1. Identify the exact limit

   • Check the Apigee error code/message
   • Look at policy failures vs platform throttling vs backend timeouts

2. Reduce pressure

   • Cache responses
   • Batch requests
   • Compress payloads
   • Use pagination
   • Avoid unnecessary transformations in flows

3. Tune policies

   • Adjust SpikeArrest/Quota settings
   • Move expensive logic out of request path
   • Reorder policies to reject early

4. Scale or redesign

   • Split traffic across multiple proxies/products
   • Use additional environments/orgs if appropriate
   • Offload heavy processing to downstream services

5. Monitor and test

   • Use Apigee analytics and debug traces
   • Load test to confirm the true bottleneck
   • Watch backend latency separately from Apigee latency

If you want, send me:

• the exact error message/code
• whether you’re on Apigee X, hybrid, or Edge
• the limit type you think you’re hitting
• a rough traffic pattern and proxy setup

…and I can help you pinpoint the likely cause and suggest a fix.