Architecture Helps Reduce 90% of Costs While Maintaining Quality

Delphi Digital’s research has a significant impact on the market. However, when they developed an AI product for in-depth crypto analysis, the project nearly got “shut down” due to the harsh economic challenges. Complex queries about on-chain data, tokenomics, or valuation models can cost several USD per question. Scaling to thousands of users would make costs far beyond sustainable operation. They didn’t choose the simple option of switching to a cheaper model. Instead, they redesigned the entire system architecture.

Three Layers of Architecture to Solve Cost Issues 1️⃣ Intelligent Query Router Over 60% of queries don’t need to touch the LLM. Price data → call API directly Basic definitions and concepts → retrieve from cache Only truly complex analyses → activate inference model Principle: Use the right tool for the right task. Not every question requires a “heavy” AI. 2️⃣ Tiered Caching Most questions are repeated multiple times. Unchanged content → pre-generate Slow-changing content → cache Real-time dynamic content → generate fresh Results: 70% reduction in response latency More stable system Significant decrease in inference costs 3️⃣ Blind Model Testing Delphi sends the same query to multiple different models. Experts evaluate results without knowing the source. Surprising conclusion: Smaller models often perform as well as larger models. Based on this, they route queries to the cheapest model that still meets the required quality threshold. Core Factor: Accuracy Verification Cost optimization is truly effective only when reliability is ensured. This is where @mira_network comes into play. Decentralized consensus mechanism helps verify output results, allowing Delphi to trust cheaper models without expanding manual review teams. Results 90% reduction in costs Maintain analysis quality Accelerate response speed Operational models become sustainable Key Lesson Technological capability alone, without economic deployment ability, remains just research. Delphi proves that: Deployment issues are just as important as model issues. And with verification layers like #Mira behind the scenes, both problems can be solved. $MIRA