Discuss your app

High-value capability

Caching Strategy

Implement caching layers to improve response times and reduce database load.

2-4 daysTypical timeline
$500 - $1,500Typical range
4Approaches compared

SCOPE DECISION

Does this belong
in the first release?

A capability is only essential when the product promise depends on it. Use these signals to keep the decision tied to user value.

INCLUDE WHEN

The product needs it now

  • 01
    Repeated database queries for same data
  • 02
    Slow API responses
  • 03
    High traffic expected
  • 04
    Expensive computations
LEAVE OUT WHEN

The release works without it

  • 01
    Very early MVP with low traffic
  • 02
    Mostly unique, personalized data
  • 03
    Real-time data requirements

IMPLEMENTATION OPTIONS

Choose the approach
that fits the product.

The right technology depends on ownership, scale, integration depth, and how much operational responsibility the team wants to carry.

RedisIn-memory data store

Strengths
  • Very fast
  • Rich data structures
  • Pub/sub support
  • Widely used
Tradeoffs
  • Additional infrastructure
  • Memory costs
  • Cache invalidation complexity

CDN CachingEdge caching for static and API responses

Strengths
  • Global distribution
  • No code changes
  • Managed infrastructure
Tradeoffs
  • Cache invalidation
  • Cost at scale
  • Not for personalized content

Vercel/Next.js CachingBuilt-in caching for Next.js apps

Strengths
  • Zero config
  • ISR/SSG support
  • Integrated
Tradeoffs
  • Vercel-optimized
  • Limited control

In-memory (Node)Application-level caching

Strengths
  • Simple
  • No infrastructure
  • Fast
Tradeoffs
  • Lost on restart
  • Memory limits
  • Single server only

DELIVERY SEQUENCE

A practical path
through the work.

  • 01Identify cacheable data and endpoints
  • 02Choose caching strategy (read-through, write-through)
  • 03Set up caching infrastructure
  • 04Implement cache keys and TTLs
  • 05Add cache invalidation logic
  • 06Monitor cache hit rates
  • 07Handle cache failures gracefully
  • 08Document caching behavior

WATCH FOR

Common implementation mistakes.

  • 01
    Caching personalized data without user-specific keys
  • 02
    Too long TTLs causing stale data
  • 03
    Not handling cache failures (fallback to DB)
  • 04
    Caching errors
  • 05
    Missing cache invalidation on updates
  • 06
    Over-caching (complexity for little gain)

QUESTIONS

Before you commit the scope.

When should I add caching to my MVP?+

When you see slow queries or high database load. Dont pre-optimize. Start with database indexes, then add caching for specific bottlenecks.

How do I handle cache invalidation?+

Use short TTLs for frequently changing data. Invalidate explicitly on writes. Consider event-driven invalidation for complex relationships.

Redis vs. in-memory caching?+

In-memory for single-server MVPs and session-specific data. Redis when you need persistence, multiple servers, or complex data structures.

RELATED DECISIONS

Continue mapping the release.

Core capability

Database Design

Choosing and designing databases for scalability, performance, and data integrity.

DISCUSS YOUR APP

Turn this decision
into working software.

Bring the product context and current scope. We will choose the approach after the release is clear.

Discuss your app