Prompt Compression Techniques for High-Volume LLM Workloads
Cut token costs at scale while boosting reasoning by pruning redundant prompt content.
Cut token costs at scale while boosting reasoning by pruning redundant prompt content.
Product teams can own LLM evaluation with operational rigor, not ML expertise.
Track AI spend by team, project, model, and provider to surface where costs actually come from.
Prevent runaway AI costs by catching overspend before requests are sent.
Untracked prompt changes quietly inflate costs and hide production failures.
Manage four independent rate limits or watch production fail at 2am.
Enforce spending limits at the gateway layer, not inside the agent itself.
Different providers require different strategies to actually save money on prompt caching.
AI features have real variable costs that compress traditional SaaS margins in unexpected ways.
Engineering and finance need different AI spend data to manage costs effectively.
A gateway between apps and AI providers enables real-time cost tracking by team.
Tagging AI requests at the gateway layer reveals spending by tenant, feature, and model.