Best Caching Strategy for a High-Traffic Backend

By The Saqarmax Team · September 2026 · 7 min read

Direct answer: Use Valkey or Redis as your shared cache layer for session data, hot rows, and rate limits, and put Cloudflare or Fastly in front of anything servable as a static or semi-static response. Layer an in-process cache like Caffeine underneath for the handful of lookups your app hits thousands of times per second, but never treat it as a source of truth across instances. Skip database-native query caching beyond materialized views — MySQL dropped its query cache years ago, and Postgres never had one worth relying on. Most high-traffic backends need three of these four layers working together, not one “winner.” The mistake that actually breaks production is skipping invalidation design and bolting caching on afterward.

Last updated: September 2026

Caching a high-traffic backend isn’t really a single decision — it’s four separate ones stacked on top of each other, and each layer answers a different question. Consequently, teams that pick “just Redis” or “just a CDN” tend to hit a wall the moment traffic gets uneven. Below is what each approach is actually good for in 2026, including the pricing and tooling realities that changed since the Redis license fight kicked off.

1. Redis / Valkey as a Shared Cache Layer

Redis Ltd. moved the project off BSD-3-Clause in March 2024, shipping 7.4 and later under RSALv2/SSPL, with an AGPLv3 option added in May 2025. That move pushed AWS, Google Cloud, Oracle, and a long list of contributors to fork Redis 7.2 into Valkey and hand it to the Linux Foundation under BSD-3-Clause. Consequently, AWS, Google Cloud, and DigitalOcean now default new managed deployments to Valkey, and Valkey runs roughly 20% cheaper than Redis OSS on AWS ElastiCache nodes, about a third cheaper on serverless. Meanwhile, the wire protocol is identical, so client code doesn’t change either way. Use this layer for session state, hot rows, leaderboards, and rate limiting — anything needing sub-millisecond reads shared across every app instance. The tradeoff is operational: you’re running a stateful service, and cache invalidation across keys is entirely your job.

2. CDN Edge Caching (Cloudflare / Fastly)

Cloudflare prices by flat monthly tier ($0, $25, $250) with unmetered bandwidth, and Tiered Caching — a multi-layer cache hierarchy that cuts origin requests — is free from Business up. That said, purge latency on non-enterprise plans runs 2-4 seconds, and up to 30 seconds on Free or Pro, so instant invalidation isn’t guaranteed unless you pay for Enterprise, which starts near $5,000/month on an annual commitment. Fastly takes the opposite tradeoff: it charges a metered $0.12/GB in North America and Europe, pricier than Cloudflare, but delivers sub-150ms global purges and Compute@Edge for request-level logic. Therefore, if traffic is heavy but content changes rarely, Cloudflare’s flat fee wins on cost. If you need to invalidate fast and often — flash sales, live pricing — Fastly’s purge speed earns its premium. Either way, this layer only helps content cacheable by URL or header, not personalized responses.

3. In-Process (Application-Level) Caching

An in-process cache like Caffeine on the JVM, or an equivalent in-memory map in Node or Go, lives inside your app’s own process, so reads skip the network entirely. Naturally, that makes it the fastest layer available — nanoseconds instead of milliseconds. However, a local cache wins on latency and loses on correctness the moment you run more than one instance, since updates on one node never reach the others. This is why most teams pair it with Redis or Valkey in a hybrid setup: check the local cache first, fall back to the distributed cache, then the database, using short TTLs plus event-driven invalidation to keep nodes roughly in sync. Reserve this layer for reference data — feature flags, config, small lookup tables — that tolerates a few seconds of staleness. Anything user-specific or frequently written doesn’t belong here.

4. Database Query Caching

MySQL’s built-in query cache is gone — removed for good reason, since it serialized writes across the whole cache and fell apart under concurrency. Postgres never shipped an equivalent; instead, it relies on shared_buffers, typically sized to 15-25% of system RAM, to keep hot data pages in memory automatically. For expensive aggregations, materialized views are the practical substitute: they run the query once, store the result on disk, and serve it in milliseconds until refreshed. Still, this only fits data that doesn’t need to be real-time — a dashboard, not a live order count. Refreshing a materialized view can be a write-locking operation on older Postgres versions, so schedule refreshes carefully and treat this layer as a targeted fix, not a general caching strategy.

Comparison Table

How to Choose

• Start with Redis or Valkey if you need one fact — a session, a counter, a rate limit — visible to every instance instantly.
• Add CDN edge caching the moment any response is identical for multiple users, even briefly; it removes load before it ever reaches your servers.
• Layer in Caffeine or similar only after you’ve profiled and found a specific hot path that a network hop is genuinely slowing down.
• Reach for materialized views for slow aggregate queries, not as a first-resort caching layer.
• Default to Valkey over Redis for new managed deployments unless you have a specific reason to pay for Redis Ltd.’s enterprise features.
• Design invalidation before you write cache code — a fast cache with the wrong invalidation strategy causes more incidents than no cache at all.

Getting this stack wrong shows up as either stale data users complain about or an origin server that falls over under load — both are architecture problems, not library problems. If you’re deciding on caching alongside your broader data layer, it pairs directly with our guide on choosing a database for high-load backends, and if you’d rather have someone design and wire up the whole thing, that’s what our backend API development work covers.

About Saqarmax — Saqarmax is a blockchain and automation studio building smart contracts, full-stack dApps, and custom bots and AI apps for founders who need working software, not theory.

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