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
- 30-70% AI cost reduction without degrading 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
FAQ
How much can I save?
On non-optimized pipelines, we regularly see -50% to -70%. On already refined systems, -15% to -30% is realistic.
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.