We engineer web platforms designed for millions of concurrent users — with horizontal scaling, intelligent caching, and zero-downtime architecture baked in from the start.
Discuss your projectWe size infrastructure for 10× the expected peak load, not average load. This isn't over-engineering — it's the difference between a smooth traffic spike and an all-hands incident at 2am. Auto-scaling policies then right-size costs during normal operation.
Every data access pattern is modelled before we write the first query. We identify hot paths, design the cache key strategy, and ensure that the database is never a single point of contention.
Application servers hold no local state. Sessions live in Redis, uploads go directly to object storage, and any node can handle any request. This makes horizontal scaling trivial — adding capacity is just incrementing a replica count.
We use blue/green or canary deployments to achieve zero-downtime releases, even for schema migrations, using techniques like expand-contract and online schema change tools.
For most applications, reads outnumber writes 10:1 or more. We route read queries to one or more streaming replicas and keep the primary database focused on writes. This distributes load and provides a natural failover target.
Every system we build ships with structured logging (JSON → Elasticsearch), distributed tracing (OpenTelemetry → Jaeger), and metrics dashboards (Prometheus → Grafana). You see throughput, latency percentiles, and error rates — not just uptime.
We model your traffic: peak RPS, request distribution, read/write ratio, data volume growth rate. This defines the architecture before any code.
Every significant decision (database choice, caching strategy, queue selection) is documented with alternatives considered and the reasoning behind the choice.
Working, testable software every sprint. We demo on Friday and ship to staging. No multi-month integration phases.
Gatling or k6 load tests run in CI against staging. Performance regressions are caught before they reach production.
We document the system architecture, operational procedures, and on-call playbook so your team can own it confidently after handoff.
We use k6 and Gatling to simulate realistic traffic patterns — spike tests, soak tests, and gradual ramp-up scenarios. Target: sustain 10× average load for 30 minutes with p99 latency under 200ms.
We deliberately kill nodes, saturate network interfaces, and simulate database failovers to verify that the system degrades gracefully rather than failing catastrophically.
Every slow query (>50ms on staging) is investigated. We use EXPLAIN ANALYZE, index analysis, and query rewriting to eliminate N+1 patterns and full-table scans.
Unit tests for business logic, integration tests for API contracts, and end-to-end tests for critical user flows. Coverage targets are defined per service, not globally.
Tell us about your traffic, your pain points, and your timeline. We'll respond within one business day with a technical assessment.