Intelligent Cost Optimization
EDDI's Model Cascading system enables cost-aware multi-model routing. Start with fast, inexpensive models and automatically escalate to more powerful (and expensive) models only when confidence is low, reducing AI costs without sacrificing quality.
Cascading Features
- Cost Optimization: Try cheap/fast models first, escalate to powerful models only when confidence is low
- 4 Confidence Strategies: Structured output, heuristic, judge model, or none, choose the evaluation method that fits your use case
- Per-Conversation Budgets: Automatic cost tracking with budget caps and conversation eviction when exceeded
- Tenant Cost Ceilings: Monthly cost budgets per tenant with automatic enforcement in multi-tenant deployments
- 19 LLM Providers: Mix any of them in one cascade, for example a Gemini Flash or DeepSeek tier in front of Claude or GPT. See the model catalog
How It Works
Configure a cascade chain of models ordered by cost. For each user message, EDDI tries the cheapest model first and evaluates confidence. If confidence falls below the threshold, it automatically escalates to the next model in the chain. Published deployments of this pattern report 31% (a 75,000-query production workload) to 58% (an 8-week enterprise pilot) lower inference cost at held quality. How much you save depends on how many of your queries are easy and how far apart your tiers are priced: see the evidence and its caveats.