Design a Multi-Model Inference Cost Optimization System
Last updated: May 21, 2025
Quick Overview
Design a system that minimizes inference costs across multiple LLM providers while maintaining quality SLAs. Cover model selection, caching, request batching, and cost monitoring.
Cursor
May 21, 202510
8
3,972 solved
Design a system that minimizes inference costs across multiple LLM providers while maintaining quality SLAs. Cover model selection, caching, request batching, and cost monitoring.
At $2B ARR, Cursor's inference costs are a significant line item. This question tests your ability to optimize costs in a system that makes millions of LLM calls per day across multiple providers. The interviewer expects practical cost reduction strategies that do not sacrifice user experience.
What the Interviewer Expects
- Design a cost-aware routing system across multiple providers
- Implement semantic caching for repeated or similar requests
- Use request batching to reduce per-request overhead
- Build cost monitoring and alerting dashboards
- Balance cost reduction against quality and latency SLAs
Key Topics to Cover
How to Approach This
- Start by clarifying functional and non-functional requirements with the interviewer.
- Estimate the scale: QPS, storage, bandwidth. This drives your design decisions.
- Draw a high-level architecture first, then deep dive into 1-2 critical components.
- Discuss trade-offs explicitly (e.g., consistency vs availability, SQL vs NoSQL).
- Address failure scenarios, monitoring, and how the system handles 10x traffic spikes.
Possible Follow-up Questions
- How would you handle a sudden price increase from a model provider?
- How would you decide when to invest in training your own model vs using a third-party API?
- What is your cache invalidation strategy when the underlying model is updated?
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Practice System Design ProblemsSample Answer
Requirements
Functional Requirements:
- Multi-Provider Inference: Ability to route inference requests to various LLM providers (e.g., OpenAI, Google, AWS) based on cost and quality metrics.
- **Request B...
Capacity Estimation
Assuming Cursor handles 10 million inference requests per day:
- Average inference cost per request across providers: $0.01 (varies by provider and model).
- Daily cost without optimization: 10,000,00...
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