What Would Redis Save You?
A well-designed Redis cache reduces database load 60–80% and cuts read latency to under 1ms. Enter your numbers and see the actual ROI before you commit to anything.
60–80%
DB load reduction
<1ms
cache read latency
Month 1
typical ROI positive
Your Current Database Setup
Total read queries hitting your DB under normal traffic
P50 response time from your DB monitoring or slow query log
RDS, Aurora, Cloud SQL, or equivalent monthly bill
Achievable with TTL-based caching for repetitive reads. 80% is realistic for most apps.
Cache hit rate depends on your data access patterns. Hot reads (repeated lookups of same records) can achieve 90–99%. Mixed workloads typically hit 70–85%.
DB load reduction
80%
400 q/s served by cache — only 100 q/s hit the DB
Avg latency (now)
80ms
Every read hits the database
Avg latency (cached)
16.4ms
80% improvement
DB cost reduction
$640
Via DB right-sizing
Redis cost
$115
Selected tier
Monthly net savings
+$525
Redis pays for itself and saves $6K/year
Before vs After: Database Load
What your database sees at 80% cache hit rate.
What the calculator measures
Database load reduction
Cache hits serve from memory at sub-millisecond speed — those queries never reach the database. Your DB processes only cache misses. At 80% hit rate, the database handles 1/5th of its previous query volume.
Average response time
Cache reads take ~0.5ms. DB reads take 10ms–2s depending on query complexity. The weighted average response time drops proportionally to your hit rate — most requests get sub-millisecond reads.
Monthly net savings
DB cost reduction comes from right-sizing your database instance after load drops. Not all DB costs scale linearly with queries, so this calculator uses a conservative 40% conversion factor.
Frequently Asked Questions
What is a good Redis cache hit rate?
A hit rate above 90% is the target for production caching layers; 80–90% is generally considered healthy. Below 80% often signals misconfiguration — TTLs set too short, poor key design, or caching the wrong access patterns. Below 60%, the cache is providing limited value and needs a redesign before it's worth the added infrastructure.
How does adding Redis reduce database load?
Cache hits are served from memory in under a millisecond and never reach the database — only cache misses do. At an 80% hit rate, your database handles roughly one-fifth of its previous query volume, which is why database load reduction tracks closely with hit rate in this calculator.
Why does the calculator use a 40% cost conversion factor for savings?
Database costs don't scale perfectly linearly with query volume — a lot of RDS or Aurora cost is fixed (base instance, storage, standby). The 40% factor is a conservative estimate of how much of the freed-up capacity translates into an actual instance downsize, rather than assuming a 1:1 cost reduction.
Does adding Redis always save money?
No — for lower-traffic workloads, the Redis hosting cost can exceed what you save on database compute, even though the latency gain is real. This calculator flags that case directly: if your inputs produce negative net savings, it shows the added monthly cost so you can weigh it against the response-time improvement rather than assuming caching is free.
What's the hardest part of adding a Redis caching layer?
Not the cache itself — the invalidation strategy. Serving stale data after the source data changes is the most common production issue with caching. TTL-based expiry is simplest but risks staleness; write-through and event-driven invalidation are more precise but require more implementation work matched to your specific data access patterns.