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Model Routing
Select models dynamically by task complexity, quality target, latency, privacy and price.
- Fallback and escalation paths
- Managed and self-hosted models
- Policy-aware routing
AI Cost Optimization
Control AI spend.
Protect quality and performance.
Intellivetrix helps enterprises optimize model, token, GPU and platform costs through intelligent routing, semantic caching, prompt efficiency, observability and policy-driven AI FinOps.
Core levers
Optimize the full AI economics stack.
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Semantic Caching
Reduce duplicate inference through exact, semantic and workflow-aware caching patterns.
- Response and tool-output caching
- Embedding-aware similarity
- Freshness and invalidation policies
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Prompt Efficiency
Lower token usage with context trimming, prompt compression and retrieval optimization.
- Context window management
- RAG relevance tuning
- Structured output optimization
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AI FinOps
Establish cost ownership, unit economics and governance across AI products and teams.
- Cost per user and workflow
- Budgets, quotas and alerts
- Showback and chargeback
GPU
Inference Infrastructure
Improve utilization for self-hosted workloads through batching, autoscaling and quantization.
- vLLM and inference servers
- Kubernetes and GPU scheduling
- Managed versus self-hosted analysis
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Quality-Cost Observability
Measure spend alongside latency, accuracy, safety and business outcomes.
- Token and request telemetry
- Model and tenant dashboards
- Regression and anomaly alerts
Reference flow
Optimization belongs in the platform—not in every application.
Centralized controls create consistent economics and governance across every AI product.
Request
IdentityTenantUse caseBudget policy
Optimize
Semantic cachePrompt compressionContext trimming
Route
Quality targetLatency targetCost targetPrivacy tier
Execute
OpenAIAnthropicGeminiAzureSelf-hosted
Observe
CostTokensLatencyQualityBusiness outcome