OpenClaw Cost Optimization: Cut AI API Costs by 90%

DeepInfra's approach to optimizing OpenClaw AI API costs involves strategic use of different model tiers and prompt caching to significantly reduce expenses. By understanding the cost drivers of token usage, including system prompts, conversation history, and output, users can implement a two-tier model strategy, utilizing a smart primary model for main tasks and budget models for sub-tasks. This method reduces unnecessary costs by routing requests to the most economical models capable of completing the tasks. The guide also emphasizes the importance of maintaining compact system prompts and conversation histories, alongside the benefit of prompt caching to cut input costs by up to 60%. Users are advised to regularly audit their SOUL.md files and tool registrations to minimize overhead, adjust heartbeat frequencies to suit task needs, and consider local or free-tier cloud providers for non-critical agents. Overall, these strategies provide a potential 90% reduction in AI API costs, making it financially viable for users to maintain efficient and cost-effective AI operations.