AI Cost and Performance Optimization
We cut the costs and latency of your AI systems already in production: quantization, distillation, semantic caching, intelligent model routing, and continuous monitoring. Improve business KPIs and reduce your cloud bill simultaneously.
Keep the performance, latency, and costs of your AI models under control with continuous monitoring.
Use cases
- AI SaaS with margins under pressure
- High-volume chatbots
- Expensive batch pipelines (massive summaries, embedding)
- Mobile apps with latency constraints
- Annual cloud budget compliance
Measurable benefits
- Lower AI costs without degrading the user experience
- Halved p95 latency
- Surgical visibility into what costs what
- Data-driven optimization roadmap
Technical details
Model optimization
- INT8/INT4 quantization
- Distillation: small models mimicking large ones
- Pruning and LoRA adapters
- Speculative decoding
Caching
- Semantic cache (Redis + embeddings)
- Prompt cache (provider-side)
- CDN for generated assets
- Invalidation policies
Routing
- Cheap model for simple tasks
- Premium model for complex cases
- Automatic fallback on provider downtime
- A/B testing between models
Observability
- LangSmith, Langfuse, Helicone
- Traces, costs, latency per request
- Alerts on budget anomalies
- Finance-friendly dashboards
How we improve an AI system already in production
- Diagnosis — We start from production data: unresolved cases, complaints, cost per request, latency, drop-off points.
- Error reproduction — We collect problem cases into a stable set: without reproducibility every fix is guesswork.
- Targeted changes — We act in cheapest-first order: source quality, retrieval, instructions, model choice, and only then training.
- Cost control — We analyse context size, redundant calls and caching opportunities — savings usually come from here, not from switching vendor.
- A/B comparison — Changes are compared on the same evaluation set, with documented outcomes.
- Knowledge transfer — We leave the measurement tooling with your team, so the next optimisation can be done internally.
We deliver a diagnostic report, regression case set, documented changes and a before/after cost comparison.
Working with us
The team that analyses the process is the team that builds and maintains it: product, engineering, integration with your existing systems, governance of automated decisions and post-release support.
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FAQ
How much can I save?
It depends on the starting point: never-optimised pipelines leave a lot of room, already refined systems much less. We run an assessment on your usage logs before quoting a number.
Will quality decrease?
No, provided optimization is done with benchmarks and A/B testing. Often quality improves because you force faster, more specialized models.
How long does an audit take?
2-4 weeks for analysis + 4-8 weeks for implementation of priority optimizations.