Caching Strategies for SaaS: A 2026 Engineering Guide

There are two hard things in computer science, the joke goes, and cache invalidation is one of them. In SaaS the stakes are higher than performance: a cache key that collides across tenants is a data-leak bug. We build multi-tenant platforms for a living, and our SaaS platform practice treats cache correctness with the same care as database isolation. If tenant isolation is your concern, start with building multi-tenant SaaS on Postgres RLS.

Caching strategies differ mostly in how writes flow. Pick based on the consistency you need and the write volume you carry.

• Cache-aside (lazy). The app reads through to the DB on a miss and populates the cache; writes invalidate the entry. Simple, resilient to cache outages, caches only what is asked for. The right default.
• Write-through. Every write updates cache and DB together, keeping the cache warm and consistent. Costs you writes of data that may never be read.
• Write-behind (write-back). Writes hit the cache and are flushed to the DB asynchronously. Maximum write throughput, but a cache failure can lose not-yet-persisted data — use only when you understand that risk.

The canonical read path is a handful of lines. The discipline is in the key design and the TTL, not the control flow.

// Cache-aside read, namespaced by tenant + schema version
async function getProject(tenantId, projectId) {
  const key = `t:${tenantId}:v3:project:${projectId}`;
  const cached = await redis.get(key);
  if (cached) return JSON.parse(cached);

  const row = await db.projects.findOne({ tenantId, id: projectId });
  if (row) {
    // jittered TTL avoids synchronized expiry (see thundering herd)
    const ttl = 300 + Math.floor(Math.random() * 60);
    await redis.set(key, JSON.stringify(row), "EX", ttl);
  }
  return row;
}

Note the t:<tenantId> prefix and the v3 schema version. The tenant prefix makes cross-tenant collisions impossible; the version lets a deploy that changes the cached shape invalidate everything by bumping a single token rather than scanning keys.

Use both mechanisms together. TTLs bound how stale data can ever get; explicit invalidation keeps it fresh between expirations. Relying on TTL alone serves stale data for the whole window; relying on invalidation alone means one missed delete caches a wrong value forever.

• On write, delete or update the affected keys. Deleting (and letting the next read repopulate) is simpler and avoids caching a value nobody reads.
• Use a versioned namespace to invalidate a whole class at once: bump listVersion and every cached list for that tenant is logically gone.
• Be paranoid about derived data — counts, rollups, dashboards. The classic bug is caching an aggregate and forgetting to bust it when a contributing row changes.

When a hot key expires, every concurrent request misses at once and stampedes the database to recompute the same value. On a busy endpoint this can take the database down. Three defenses, often combined:

• Single-flight. A short lock so only one request recomputes the value while the rest wait for it to land in the cache.
• Jittered TTLs. Add random spread to expiry so popular keys do not all lapse on the same tick.
• Stale-while-revalidate. Serve the slightly stale value immediately and refresh it in the background — the same idea HTTP caching exposes via the stale-while-revalidate directive.

These pair naturally with a background refresh job — see background jobs and queues in production for the worker side.

A cache earns its keep only if you measure it:

• Track hit rate. A cache below roughly 80% hit rate on a hot path is often miskeyed or has too short a TTL — instrument it. See observability for startups.
• Pick an eviction policy on purpose. Set a maxmemory policy (such as allkeys-lru) so the cache sheds cold keys instead of erroring when full.
• Cache after you index. A cache is not a substitute for a missing database index — fix the query first. Our SaaS database scaling guide covers the order of operations.