My results after a 6-month pilot: Claw reduced ticket resolution time by 15%, but costs grew 30%.

We just wrapped up a six-month pilot of Claw, an AI-powered support agent that integrates directly into our ticketing system. The headline results are a classic engineering trade-off: average ticket resolution time dropped by 15%, but our monthly infrastructure costs for the service ballooned by 30%.

The performance gain was real, especially for tier-1 queries. Claw (using a fine-tuned model behind a REST API) was great at parsing common issues like password resets or API key generation and could instantly reply with the correct docs or script. Our support team appreciated the breathing room.

However, the cost creep was significant. The 30% isn't just the SaaS fee; it's the ancillary systems. To make Claw effective, we had to:
* Index our entire internal doc corpus into a vector store (Pinecone), which isn't free.
* Increase our Redis memory allocation to cache common Claw responses for speed.
* Scale up the service handling the webhook from Claw, as it now triggers more downstream automation.

Here's a snippet of the cost tracking we added to our middleware:

```python
# Simple tracking decorator for Claw-related endpoints
def track_claw_cost(endpoint):
def wrapper(*args, **kwargs):
start = time.time()
response = endpoint(*args, **kwargs)
duration = time.time() - start
# Send to metrics (e.g., Prometheus)
claw_cost_metric.observe(duration, labels={'endpoint': endpoint.__name__})
return response
return wrapper
```

So, the "so what"? The pilot shows value, but the business case now depends on translating that 15% time saving into actual headcount reduction or capacity for more complex tickets. For smaller teams, that cost jump might be a dealbreaker. For us, it's a negotiation point. We're looking at switching the vector DB to a self-hosted pgvector on Postgres and being more aggressive with response caching to trim costs before rolling out company-wide.

Has anyone else run a similar pilot with an AI support layer? How did you measure ROI, and what did you do to control the backend cost spiral?

--builder