Cache Strategy Planner
Model TTL, headers, and keys to predict your Apigee cache hit rate
100% Client-Side
Cache Configuration
5m
10s1h
50%
Low (few unique keys)High (many unique keys)
70%
Static dataFrequently changing
Key: request.uri
21.8%
Estimated Hit Ratio
78.2%
Cache Miss Ratio
Performance Impact
Backend Load Reduction21.8%
383.91
MB saved/hour
22
Calls/sec avoided
Generated Policies
CacheLookup Policy
<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<LookupCache name="CL-LookupResponse">
<DisplayName>Lookup Cached Response</DisplayName>
<CacheResource>cache-resource</CacheResource>
<Scope>Exclusive</Scope>
<CacheKey>
<Prefix>api</Prefix>
<KeyFragment ref="request.uri"/>
</CacheKey>
<ExpirySettings>
<TimeoutInSeconds>300</TimeoutInSeconds>
</ExpirySettings>
</LookupCache>PopulateCache Policy
<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<PopulateCache name="PC-PopulateResponse">
<DisplayName>Populate Response Cache</DisplayName>
<CacheResource>cache-resource</CacheResource>
<Scope>Exclusive</Scope>
<CacheKey>
<Prefix>api</Prefix>
<KeyFragment ref="request.uri"/>
</CacheKey>
<Source>response</Source>
<ExpirySettings>
<TimeoutInSeconds>300</TimeoutInSeconds>
</ExpirySettings>
</PopulateCache>💡 Optimization Tips
- • Longer TTL = higher hit ratio, but risk serving stale data
- • Per-user caching drastically increases cardinality
- • Consider cache invalidation strategies for dynamic content
- • Monitor actual hit ratios in production and adjust
About this Tool
Purpose
Caching is powerful but often mis-tuned. This planner lets you visualize how cache settings impact latency and hit ratio before deployment.
Features
- •Adjust TTL, cardinality (headers + params), and backend freshness.
- •View dynamic hit/miss chart and byte savings estimate.
- •Generate recommended CacheLookup and PopulateCache policy XML.
- •Explore scenarios like per-user cache vs global cache.
Learning Tip
Pair this planner with the Quota Simulator to balance performance and fairness in API traffic design.