Scalability & Reliability Benchmarks
Ensure your AI systems perform under load, meet uptime requirements, and scale with your business growth.
The Reliability Engineering Stack
We build reliability from the ground up — each layer depends on the one below it being solid.
What we deliver
AI systems that work in development often fail under production load. We design and execute scalability and reliability programs that validate your systems against real-world load conditions, define clear SLAs, and build the architecture needed to meet them consistently.
Our benchmarking programs establish the performance baselines your business needs to make confident scaling decisions — and the monitoring infrastructure to know when those baselines are at risk.
Key deliverables
- Load testing and stress testing for AI and application systems
- Capacity planning and scaling architecture design
- SLA definition and monitoring implementation
- Resilience and failover architecture (circuit breakers, retries, fallbacks)
- Performance regression testing in CI/CD pipelines
- Reliability dashboards and incident response runbooks
Real-Life Use Cases
Scalability and reliability engineering preventing costly failures.
Black Friday Load Testing
An e-commerce platform discovered their AI recommendation engine would fail at 3× normal load — exactly what Black Friday brings. We redesigned the inference pipeline with caching and async processing. The platform handled 8× normal load without degradation.
Streaming AI Reliability
A streaming platform's AI content moderation system had no circuit breakers. When the model endpoint degraded, it cascaded to the upload pipeline. We implemented bulkhead patterns and fallback logic. Subsequent incidents were contained in under 90 seconds.
Clinical AI SLA Compliance
A hospital's AI diagnostic tool had no formal SLA. Clinicians experienced unpredictable response times. We defined SLOs, implemented monitoring, and redesigned the inference stack. P99 latency dropped from 8 seconds to 400ms.
Payment AI Resilience
A payment app's AI fraud detection had a single point of failure. We implemented a multi-region active-active architecture with automatic failover. The system now maintains 99.99% availability even during regional cloud outages.
Know your system's limits before your users do
We'll benchmark your AI systems, define your SLAs, and build the architecture to meet them reliably.
Benchmark Your Systems