The infrastructure that keeps
your AI feature reliable
Rate limiting, caching, token cost control, monitoring, and evals, the unglamorous backend work that decides whether your AI feature survives real traffic or falls over on day two.
Production AI infrastructure,
not just an API call.
Calling an LLM API is the easy part. The infrastructure around it, cost control, reliability, and observability, is what determines whether the feature is sustainable.
Production AI Backend Architecture
Rate limiting, request queuing, and graceful degradation so traffic spikes and API outages don't take your AI feature, or your whole app, down with them.
Token Cost Control & Caching
Response caching, prompt compression, and usage budgets so AI costs scale predictably with your business, not your traffic spikes.
Evals & Quality Monitoring
Ongoing evaluation pipelines that catch silent quality drift after launch, not just a one-time test before going live.
Cloud Deployment & Scaling
Production deployment on your cloud of choice, with autoscaling and security hardening built around AI-specific traffic patterns.
Observability & Incident Alerts
Latency, error rate, and cost dashboards with alerts, so you find out about a problem before your users complain about it.
Security Hardening for AI Features
Prompt injection defenses, input sanitisation, and access control around AI endpoints, closing the gaps generic web security misses.
06Existing AI Feature Audits
Already live but unreliable or expensive? We audit the architecture and fix the specific bottleneck, cost, latency, or quality.
07Ongoing AI Maintenance
Model version upgrades, prompt regression testing, and continuous monitoring so your AI feature stays reliable as providers change underneath you.
Most AI features don't fail at the model, they fail at the infrastructure
Runaway costs, silent quality drift, and outages during traffic spikes are infrastructure problems, not model problems. We build the layer that prevents them.
Built for Real Traffic
Rate limiting and queuing designed around your actual usage patterns, not a happy-path demo.
Predictable Cost Curves
Caching and usage budgets so a viral moment doesn't turn into a surprise five-figure API bill.
Drift Detection
Continuous evals catch quality degradation after a model update, before your users notice it first.
Full Code Ownership
Infrastructure, monitoring setup, and configuration are yours, no proprietary platform dependency.
From architecture to a monitored production system in 4 stages
Every AI backend build follows the same process, because reliability is designed in, not patched in after an incident.
Architect, Harden, Deploy, Monitor
Map traffic patterns and cost exposure
We review expected usage, peak load, and worst-case cost exposure before designing the backend.
Add rate limiting, caching, and failover
Rate limits, caching layers, and multi-provider failover built in before the system sees real traffic.
Ship to production with security hardening
Cloud deployment with prompt injection defenses and access control around every AI endpoint.
Track cost, latency, and quality continuously
Dashboards and alerts so cost or quality problems are caught immediately, not discovered a month later.
AI Backend & Deployment, Frequently Asked Questions
Answers to the questions US & UK clients ask us most.
What does AI backend and deployment actually cover?
Everything around the model call itself: rate limiting and request queuing, token cost control and caching, evals and quality monitoring, cloud deployment and scaling, and observability with incident alerts. As an AI infrastructure partner, we treat the API call as the easy part, the backend around it is what decides whether the feature survives real traffic.
How do you stop AI API costs from spiking unexpectedly?
Response caching, prompt compression, and usage budgets so costs scale with your business instead of with traffic spikes. We also add rate limiting and request queuing up front, so a viral moment or bot traffic doesn't turn into a surprise five-figure bill.
What happens if our AI provider has an outage or rate-limits us?
We build multi-provider failover and graceful degradation into the backend architecture itself, so a single provider outage doesn't take your AI feature, or your whole app, down with it. This is part of the core backend build, not an add-on bolted on after an incident.
How do you catch AI quality drift after a model gets updated?
Ongoing evaluation pipelines run continuously after launch, not just as a one-time test before go-live. When a provider updates a model underneath you, the evals catch the quality drift before your users notice it, and we handle the prompt regression testing that comes with it.
Our AI feature is already live but unreliable or expensive, can you fix it without a rebuild?
Usually, yes. We audit the existing architecture to find the specific bottleneck, cost, latency, or quality, and fix that layer rather than rebuilding from scratch. Book a free consultation and we'll tell you honestly whether it's a targeted fix or a rebuild before any work starts.