Why Bun + Fastify is the Fastest Backend Stack You're Not Using Yet

The Problem With "Good Enough" Backends Most backend stacks are built for average traffic on an average day. Express.js running on Node.js, a PostgreSQL database, maybe a Redis cache bolted on later — it works fine until it doesn't. You get a product launch, a flash sale, or a spike on social media, and suddenly your server is struggling at 800 requests per second while thousands of users stare at a loading spinner. The usual fix? Throw more servers at it. Scale horizontally. Pay bigger cloud bills. But what if the real fix is simply choosing a faster runtime and framework from the start? That's exactly what the Bun + Fastify stack delivers — and after running it through a rigorous benchmark, the numbers are hard to argue with. Meet the Stack Bun — the Runtime Think of a JavaScript runtime as the engine that runs your code. Node.js has been that engine for over a decade. Bun is a brand-new runtime built from scratch in Zig, a low-level systems language, using JavaScriptCore (Apple's JS engine from Safari) instead of Node's V8. The result: Bun starts faster, executes JavaScript faster, and handles I/O — reading files, making network calls, talking to databases — significantly faster than Node.js. It's also a drop-in replacement, meaning most Node.js code runs on Bun without any changes. Fastify — the Framework Express.js is comfortable and familiar, but it quietly leaves 40% of your server's performance on the table. Fastify was engineered specifically for speed. It uses a radial tree for route matching instead of a slow linear scan. It serializes JSON using pre-compiled schemas instead of calling JSON.stringify() on every single response. In practice, Fastify handles nearly 3× more requests per second than Express under the same conditions. Redis — the Speed Layer Redis is an in-memory data store. Unlike PostgreSQL, which reads and writes to disk, Redis keeps everything in RAM — which is orders of magnitude faster. The strategy is straightforward: the first time someone requests a piece of data, fetch it from the database. Every subsequent request? Serve it from Redis directly. In our benchmark, this produced the most dramatic result of all — cached requests hit 6,781 req/s compared to 4,330 req/s for uncached reads. A 57% throughput improvement just from serving warm data. PostgreSQL — the Source of Truth While Redis handles the speed, PostgreSQL handles the reliability. Full ACID compliance, complex joins, transactions, foreign keys — the stuff that makes sure your data is actually correct. In a well-tuned system, PostgreSQL only gets hit when Redis doesn't have the answer, which is a small minority of real-world requests. Redis is the expressway. PostgreSQL is the secure vault underneath it. The expressway handles 95% of the traffic; the vault handles the things that can't afford to go wrong. The Benchmark Results, Explained We ran the stack under six different scenarios — each representing a real-world usage pattern — with 50 concurrent connections over 10 rounds. Zero failures across all 3,250 requests. ScenarioReq/sAvg LatencyP99 LatencyCached GET6,7814.42 ms11.92 msUncached GET4,3308.74 ms33.17 msPOST (Write)4,3139.57 ms13.84 msMixed 80/203,41711.43 ms24.62 msBurst (500 conn)3,142127.26 ms152.95 msBurst (1,000 conn)6,36599.42 ms145.31 ms Cached reads at 6,781 req/s — this is the stack at its theoretical best. Redis serves data directly from memory. At 4.42ms average, each response is faster than a single frame rendering at 60fps (16.67ms). Database reads and writes at ~4,300 req/s — even when cache misses force a PostgreSQL query, throughput stays above 4,300 req/s with sub-10ms average latency. The burst paradox — 1,000 simultaneous connections produced double the throughput of 500. The reason: Bun's event loop warms up and parallelizes better under sustained high concurrency. Like a kitchen where every chef and every burner is fully engaged only when a big rush hits. How Much Faster Is This, Really? To put it in perspective: Node.js + Express (no cache): ~900 req/s Node.js + Express (with Redis): ~1,800 req/s Node.js + Fastify: ~4,800 req/s Bun + Fastify + Redis: 6,781 req/s That's roughly a 7.5× difference between the worst and best case. The same server hardware that comfortably serves 100 users on a basic Express setup can serve 750 users on this stack — before you even think about scaling out. When Should You Use This Stack? High-read APIs — product catalogues, user profiles, public content feeds. Anything read far more often than it's written. Redis caching makes these endpoints essentially free once the cache is warm. Real-time products — dashboards, notification systems, live order tracking, leaderboards. Sub-10ms latency at thousands of requests per second means users experience the UI as instant, even under heavy load. Cost-sensitive projects — with this stack, a single mid-range server handles traffic that would normally require a horizontally scaled cluster. That's a meaningful infrastructure cost difference, especially at early stages. One thing to be aware of: Bun is newer software. Some Node.js packages have compatibility quirks, and the ecosystem tooling is less mature than Node.js. For greenfield projects the trade-off is absolutely worth it. For migrating a large existing codebase, test carefully first. Conclusion The Bun + Fastify + PostgreSQL + Redis stack isn't a silver bullet — no stack is. But if you're building an API that needs to be fast, reliable under load, and cost-efficient to operate, this combination is one of the strongest available in the JavaScript ecosystem today. 6,781 requests per second at 4.42ms latency with zero failures isn't just a benchmark number — it's headroom. It's the difference between your server handling today's traffic and handling next month's growth without an emergency infrastructure call at 2am. The stack is ready. The benchmarks are public. The only question is whether you are. Benchmark environment: 50 concurrent connections × 10 rounds · Bun runtime · Fastify v5 · PostgreSQL 16 · Redis 7 · 0 failures across 3,250 total requests.