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technologyreview.com·3h ago
Cloud & Infrastructure
The New Stack·Jun 25·5 min read
How we cut AI costs by 80%
TL;DR
AI costs are increasing due to inefficient use and messy context rather than just expensive models. A new approach was implemented to streamline context management, significantly reducing operational expenses.
✦ Why It Matters
Engineers can reduce AI operational costs by focusing on improving data context management.
Key Takeaways
- Consider using a context lake to streamline data access.
- Evaluate if cheaper AI models meet your query needs.
- Optimize data structure to reduce agent query costs.
- Assign platform engineering to manage context effectively.
How It Works
A context lake integrates data from multiple sources into a single model, allowing agents to access pre-joined information without redundant queries. This structure eliminates the need for agents to resolve mismatches between different tools, significantly reducing token consumption and costs.