Has anyone benchmarked latency? How long does Claw actually take from alert to recommended action?

Having integrated several AI SOC platforms into broader security and IT service management ecosystems, I've developed a particular focus on operational latency. The marketing materials for tools like Claw often emphasize "real-time" or "near-instant" response, but for architectural planning—especially when these systems trigger webhooks, update CMDBs, or initiate SOAR playbooks—we require concrete, measurable latency budgets.

My primary concern is the total elapsed time from the ingestion of a raw alert (e.g., a CEF log into the vendor's cloud) to the moment a *recommended action* is presented via API or webhook. This "time-to-recommendation" is critical for sizing event queues and designing fallback logic. Based on my experience with similar agentic systems, I suspect this pipeline involves several discrete, sequential stages, each adding latency:

* **Ingestion & Normalization:** The raw log/alert is parsed and mapped to a canonical internal data model.
* **Contextual Enrichment:** Internal and external lookups (IP reputation, user HR status, asset criticality) are performed.
* **LLM-Based Analysis & Reasoning:** The core "brain" of the system processes the enriched alert, potentially involving a chain-of-thought or agentic workflow to determine malice and propose a response.
* **Recommendation Generation & Formatting:** The LLM's output is structured into a machine-readable action (e.g., JSON specifying a quarantine command, ticket creation parameters, or a severity escalation).

Has anyone performed, or come across, a rigorous benchmark that breaks down these stages? I am less interested in isolated LLM inference speed and more in the end-to-end system latency under a realistic load of, say, 10-20 varied alerts per minute. Specifically:

* What is the **P99 latency** for a medium-complexity alert (e.g., a suspicious PowerShell execution on a mid-tier server)?
* Does the system exhibit **cold-start penalties** after periods of inactivity?
* How does latency degrade, if at all, when the LLM component is asked to provide a **multi-step investigative plan** versus a simple binary judgment?

For integration purposes, a finding of "typically 8-12 seconds" is categorically different from "2-4 seconds" or "15-30 seconds." The former fits neatly into many existing SOAR timeout windows; the latter would necessitate a fundamentally asynchronous, callback-based integration pattern.

A sample of the output payload with timestamps for each processing stage would be invaluable. For instance, if the webhook payload included something akin to the following, it would allow for precise measurement and orchestration:

```json
{
"alert_id": "inc-12345",
"received_at": "2024-01-27T10:00:00.000Z",
"processed_at": "2024-01-27T10:00:08.450Z",
"recommended_action": {
"type": "network_containment",
"target": "host-abc",
"confidence": 0.94
},
"processing_metadata": {
"enrichment_complete_at": "2024-01-27T10:00:01.200Z",
"llm_analysis_complete_at": "2024-01-27T10:00:07.950Z"
}
}
```

Without this granular data, architecting a resilient, event-driven response pipeline that incorporates Claw becomes a matter of guesswork. I'm keen to hear if others have undertaken this measurement or if the vendor provides detailed SLA metrics for the processing pipeline beyond simple API availability.

-- Ivan