Hey folks! We've been running LLM features in production for about six months now, and our "homegrown" visibility setup is starting to creak at the seams. We're a mid-market team, so we need power but can't have a 3-person squad just to manage the observability tool itself.
I'm looking at dedicated AI observability platforms and would love your thoughts on what's truly essential. Here's my starter list of must-haves, based on the pain points we've hit:
**Core Features for Us:**
* **Cost Attribution & Token Tracking:** This is non-negotiable. We need to see cost-per-request, token usage (prompt/completion) broken down by model, feature, and even end-customer. A simple dashboard like this is our holy grail:
```json
{
"feature": "chat_summary",
"avg_tokens_per_request": 1250,
"cost_per_1000_requests": "$4.20",
"top_model_used": "gpt-4-turbo"
}
```
* **Latency Breakdown:** Not just total time, but time spent in the LLM API vs. our pre/post-processing, with clear percentiles (p95, p99).
* **Prompt/Response Capture & Search:** Must store and index full prompts/completions (with PII redaction options) to debug weird outputs without replaying live traffic.
* **Simple, Powerful Alerting:** Alert on sudden spikes in cost, latency, or error rates. Bonus points for anomaly detection on output quality scores or sentiment drift.
**Nice-to-Haves:**
* **Tracing for Complex Chains:** Visualizing multi-step LLM calls (e.g., an agentic workflow) would be huge.
* **Integration with our existing stack:** We use Datadog for everything else, so easy correlation there is a big plus.
* **Synthetic Monitoring for Key Flows:** Proactively test that our AI features are returning expected formats before users notice.
What else should we be prioritizing? Are we missing any critical features that became apparent once you scaled? I'm especially curious about teams who started with one provider and switched—what was the gap you didn't see at first?
Sharing screenshots of your setup would be amazing!
Dashboards or it didn't happen.
You've hit on the absolute critical pillars, especially the cost attribution. A vital addition to that dashboard spec is the ability to **normalize token counts across different model families and providers**. The raw token number for `gpt-4` versus `claude-3-opus` versus a local Llama model is meaningless for cost comparison; you need the platform to automatically apply the correct pricing multiplier per model to show actual spend.
On latency breakdown, I'd push for the tool to break down the LLM API time itself into time-to-first-token (TTFT) and generation time per token. This is key for identifying if your bottlenecks are in network overhead or the model's generation speed, which dictates very different optimization strategies.
For prompt capture, ensure the search function supports semantic similarity, not just keyword matching. When debugging a failure, you often need to find similar-but-not-identical prompts to see if the issue is systemic.
every dollar counts