AI observability (Langfuse, LangSmith, Arize, Datadog LLM) and AI audit trails solve different problems. Observability optimises for operator visibility: latency, cost, traces, evals. An audit trail must be tamper-evident and independently verifiable by regulators, customers, and auditors who do not trust the operator. NexArt produces the audit trail; keep the observability tool for the operator view.
Side-by-side comparison
| Dimension | AI observability | AI audit trail (NexArt CER) |
|---|---|---|
| Primary audience | Internal engineers and SRE | Auditors, regulators, customers, courts |
| Integrity model | Trust the platform database | SHA-256 canonical hash + Ed25519 signature from the NexArt attestation node |
| Mutability | Records can be edited, deleted, or resampled | Any change to protected fields invalidates the certificate hash |
| Independent verification | Requires platform account and access | Any party can verify offline with the public attestation key |
| Retention | Governed by pricing tier and sampling | Portable JSON artifact; verifies indefinitely, no vendor lock-in |
| Data exposure | Typically stores raw prompts and outputs | Confidential mode stores only SHA-256 commitments |
| Answers 'prove what the AI executed' | No | Yes |
Frequently asked
Does NexArt replace LangSmith, Langfuse, or Arize?
No. Observability platforms remain the right tool for operator-facing metrics, traces, and evals. NexArt sits alongside them and produces the tamper-evident evidence layer they are not designed to provide.
Why can't observability logs serve as an audit trail?
Observability data lives inside the operator's account with mutable, samplable storage. An audit trail must survive the operator, be verifiable without their cooperation, and be resistant to selective edits. Observability tools are not built to meet any of those requirements.
Add verifiable execution to your AI stack
Produce Certified Execution Records anyone can verify without access to your infrastructure.
Related