ZenLLM

Stop paying for AI tokens your app doesn't need.

Your AI application is probably wasting 15-40% of its token spend. Provider dashboards show the bill; ZenLLM shows the request path causing the waste.

Where AI spend gets wasted

ZenLLM connects the invoice to the application behavior behind it: prompts, retries, routes, workflows, and customers.

Context accumulation: conversations grow every turn because old context is carried forward without compression.

Wrong model selection: low-risk routes stay on premium models long after cheaper models are enough.

Retry loops and agent routing mistakes multiply token spend inside multi-step workflows.

Specific issues. Specific savings.

The findings buyers ask about first: context growth, model routing, retry churn, and stale prompts.

Example: a support workflow spending heavily on Azure OpenAI and Bedrock could start by checking context accumulation.

Example: a high-volume code-review route could validate whether GPT-4o mini handles low-risk checks before using GPT-4o by default.

Example: a Vertex AI workflow with repeated failures could separate retry cost from successful request cost before scaling traffic.