Azure Redis Cache
We implement Redis caching that improves performance without creating reliability issues. Correct cache strategy, key design, TTLs, and monitoring so your app doesn’t depend on a fragile cache.
- Caching Strategy
- Performance
- Observability
Cache improves latency only when invalidation, TTLs, and fallback behavior are designed upfront.
Redis Work We Deliver
Caching is a system design decision — we implement patterns that are safe and measurable.
Caching Patterns
Read-through, write-through, and cache-aside patterns aligned to consistency needs.
Key Design & TTL
Key conventions, TTL strategy, invalidation rules, and eviction behavior.
Security & Access
Private access where needed, RBAC and network controls, and secure configuration.
Monitoring & Tuning
Hit rate, latency, memory pressure, and eviction alerts with dashboards.
How We Implement Redis
- 1
Identify Cache Candidates
Pick the right endpoints and data for caching based on latency and load.
- 2
Design Strategy
Choose patterns, TTLs, invalidation, and fallback behavior for correctness.
- 3
Implement & Validate
Add caching with feature flags and measure hit rate and latency improvements.
- 4
Operate
Dashboards, alerting, and runbooks for cache failures and regressions.
What You Get
A caching strategy aligned to correctness and consistency needs.
Key/TTL standards to avoid cache sprawl.
Monitoring for hit rate, memory pressure, and eviction.
Fallback patterns so cache outages don’t become outages.
Common Cache Problems
Low hit rate
Cache provides little value because keys/TTLs don’t match access patterns.
Stale data bugs
Invalidation strategy is missing or incorrect.
Cache dependency
Apps fail when cache is down due to missing fallback behavior.
Redis Questions
Reduce Latency With the Right Cache Design
Tell us your endpoints and performance targets. We’ll propose a caching strategy with safe invalidation and observability.
Book a Call