BlackLake (cloud) vs AI observability
Observability is read-only.
AI control is read-write.
LangSmith, Langfuse, and the wider AI-observability category report what happened. BlackLake’s control plane decides whether it should happen — and the analytics half proves it afterward. Different jobs, often complementary on the same trace data.
Feature comparison
AI control & analytics vs AI observability
One reads the trace; the other gates the call.
| Feature | BlackLake | AI observability tools |
|---|---|---|
| Captures every consequential AI action | Yes — including non-LLM tool calls and shell / CI actions | Yes — usually LLM traces only |
| Decides whether the call is allowed (allow / deny / approval) | Yes — declarative policies at govern() time | No — read-only |
| Routes high-risk actions to a human approver | Yes — magic-link, push, console | No |
| Caps spend before the LLM call | Yes — budgets deny pre-spend | No — alerts only |
| Cost cryptographically bound to a signed receipt | Yes — v2 decision tokens | No — cost is a graph |
| Trace / waterfall visualisation of LLM calls | Limited — focused on the decision record | Yes — that's the whole product |
| Prompt / completion debugging UX | Limited — shows the action shape, not full prompts | Yes — usually first-class |
| Per-(AI Actor, tool) baselines + anomaly detection | Yes — token-spike, retry, cache-miss, long-tail, idle-context | Limited — generic dashboards |
| Designed to be an SOC 2 / audit artifact | Yes — signed receipts, signed exports, decision tokens | No — designed for debugging |
Why this matters
A trace can't survive an audit. A signed receipt can.
Observability tools were designed for engineers debugging an LLM workflow. The output is a trace, a span, a token graph — useful when the question is “why did this prompt return that”. They were not designed to be an audit artifact, and they do not pretend to be.
The receipt is the artifact a customer’s vendor-risk team or an external auditor reads. It is signed, the policy snapshot is bound, the cost is bound, the chain reads back. Observability is the tool engineering uses to debug; AI control and analytics is the system of record every other reader plugs into.
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