ZenLLM
Stop paying for AI tokens you do not 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 leak.
Where AI spend gets wasted
ZenLLM is built for the failure modes that native provider dashboards flatten into one bill.
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 leaks. Specific savings.
Customer-reported outcomes from the findings buyers ask about first: context growth, model routing, retry churn, and stale prompts.
Corvia Health Technologies reported $41k/month saved after finding context accumulation across Azure OpenAI and Bedrock.
Stackline Commerce reported $28k/month saved after rerouting CI code-review calls from GPT-4o to GPT-4o mini.
Meridian Logistics Group reported $19k/month saved after finding retry loops and stale system prompts on Vertex AI.