We rolled out Claw for observability across our sales and support chatbots last month. Our primary goal was to get a handle on token usage and latency for OpenAI and Anthropic calls, especially as we scale automated outreach.
Here’s the raw cost breakdown for 30 days, covering ~500K LLM calls:
* **Claw Platform Cost:** $1,200 (flat fee for our volume tier)
* **Identified Waste:** ~$1,850 in unnecessary GPT-4 usage where GPT-3.5-turbo was sufficient. Claw's trace comparison flagged the performance delta as negligible for those specific tasks.
* **Anomaly Surcharge:** ~$400 from a recurring, faulty prompt in a legacy workflow that was hitting 5x average tokens. Their alerting caught it in week two.
* **Latency Insight:** The biggest win wasn't direct cost savings. We pinpointed a third-party data enrichment step adding 300ms to every call in a critical flow. Fixing that improved conversion.
The tool is solid for the data plumbing. The dashboards are functional, not pretty, and setting up custom cost attribution to our internal departments took some API wrangling. If you need to move from "LLMs are a black box" to "here's the bill and the bottlenecks," it's effective. If you're under 100K calls monthly, the value proposition gets shaky.
Bottom line: Paid for itself in waste identified, but you need to build the processes to act on the data. Their strength is in the granular trace, not the high-level reporting.
- RML
- RML