DeepInspect vs Traceloop: OpenTelemetry Observability and Runtime Enforcement

Traceloop instruments LLM calls the way OpenTelemetry instruments any service. Its open-source SDK, OpenLLMetry, auto-instruments model providers, vector databases, and frameworks, then emits traces and metrics in the OpenTelemetry format to any compatible backend: Datadog, Honeycomb, Grafana, or Traceloop's own hosted platform. The library carries roughly 7,300 GitHub stars, ships under Apache 2.0 with Python, TypeScript, and Go SDKs, and extends the OpenTelemetry GenAI semantic conventions. Traceloop also ships Hub, a high-throughput LLM gateway written in Rust with the same observability built in.

That is observability done in the open standard, and it is a clean version of that idea. Someone has to see the spans: which model was called, how long it took, what it cost, what came back.

Teams comparing Traceloop to DeepInspect are usually holding a tracing library next to a policy control point. These answer different questions. I want to make the split explicit.

TL;DR

Traceloop builds OpenLLMetry, an open-source OpenTelemetry instrumentation layer that traces LLM calls and exports to any OTel backend, plus a hosted platform and a Rust gateway. DeepInspect sits inline between authenticated callers and model providers, decides whether each request proceeds based on identity and data, and commits a per-decision audit record. One records what happened, in the open telemetry standard. The other decides whether it may happen, and produces the ruling.

Traceloop: traces in the OpenTelemetry standard

OpenLLMetry's model is the span. When your application calls a model, the SDK wraps the call and emits a trace: the prompt, the response, token counts, latency, and the surrounding chain of retrieval and tool steps, all in the OpenTelemetry format your existing observability stack already speaks. That standard alignment is the differentiator worth naming, because it means LLM telemetry lands in the same backend as the rest of your services with no separate pane of glass.

The hosted platform adds evaluation and monitoring on top of that trace stream, and Hub offers a Rust gateway for teams that want routing and observability at the proxy layer. All of it centers on seeing and measuring calls.

Observability records what happened. It gives responders a detailed forensic trail and it gives engineers a performance picture, and it does neither of two other things: it does not decide whether a call should proceed, and it does not prevent one at machine speed. The forensic-versus-prevention distinction is the argument in why AI security must be inline.

Recording a call versus authorizing it

DeepInspect is a stateless proxy sitting between authenticated users or agents and any LLM endpoint. Every call traverses it, and the decision happens before the provider sees the request.

The distinction is authority. OpenLLMetry observes a call and records it in the open standard. DeepInspect governs the call and decides, per request, whether this authenticated identity in this role may send this class of data to this model. A trace tells you, after the fact, that regulated data went to a model. An identity-bound layer refuses that call before it lands, keyed to who is asking, which is described in identity-aware AI gateway architecture.

The record is the second gap. An OpenTelemetry trace is telemetry an application emits about itself, which brings back the self-attestation problem: the system under audit is generating its own record, and a trace can be dropped, sampled out, or lost on a crash after the action but before the span exports. A regulator under the EU AI Act asks for a per-decision record committed independently of the application, which is developed in signed audit logs for AI requests.

Feature comparison

| Capability | Traceloop | DeepInspect | |---|---|---| | Open source | Yes (OpenLLMetry, Apache 2.0) | No | | OpenTelemetry-standard tracing | Yes, core | No | | Exports to any OTel backend | Yes, core | No | | Rust gateway (routing + observability) | Yes (Hub) | Policy proxy | | Identity-bound per-request policy | No | Core | | Inline blocking of a request | No | Yes | | Application-independent audit record | No, app-emitted traces | Core, tamper-evident | | Data classification against custom classes | No | Yes | | Model-agnostic across HTTP LLM endpoints | Yes | Yes | | Primary buyer | Platform and ML engineering | Security and compliance |

The overlap is thin because the two do different jobs. Traceloop records calls in the open standard. DeepInspect authorizes them and produces the independent evidence.

Where Traceloop is the better answer

If the requirement is tracing LLM calls into the observability stack you already run, in the OpenTelemetry standard, with an open-source SDK and no lock-in, Traceloop is a strong fit and a policy proxy is not the tool for that job. For a team that wants LLM telemetry alongside the rest of its services and values the open standard, OpenLLMetry is exactly right.

Every team running LLMs in production should have observability. Seeing what the AI does is the baseline, and Traceloop delivers it in a form your existing tooling understands.

Where the gap opens

Two requirements move a team past what tracing provides.

The first is prevention. A trace records that a disallowed call happened; it cannot stop the next one. At a 22-second attack tempo, an asynchronous record is a report, and refusing the call before it reaches the model is an inline job, keyed to identity, which is the post-authentication gap.

The second is evidence independence. An application-emitted OpenTelemetry trace is a claim the application makes about itself. A per-decision record committed on storage the application cannot alter is what a regulator treats as proof, and the retention requirements are in EU AI Act Article 19 logging.

Pick Traceloop if

• You want open-source, OpenTelemetry-standard LLM tracing.
• LLM telemetry should land in your existing observability backend.
• No vendor lock-in is a stated requirement.
• Performance, cost, and chain visibility are the immediate need.
• Per-decision authorization evidence is already handled elsewhere.

Pick DeepInspect if

• You need live model calls refused, not just traced.
• An auditor or enterprise customer wants per-decision records tied to an identity.
• You need audit evidence independent of the application that emitted the traffic.
• Policy must differ by role and be enforced at request time.
• You operate under the EU AI Act, DORA, HIPAA, or a comparable mandate.

DeepInspect

This is exactly what DeepInspect does. DeepInspect sits inline between your users or agents and the LLM APIs they call. For every request and response, it evaluates identity, data classification, model authorization, and organizational policy, then makes a pass or block decision before the traffic reaches the model.

The record is the second half of the product. Every decision writes a structured entry containing the authenticated identity, the resolved role, the data classes found in the prompt, the policy version in force, and the outcome. That entry commits before the response returns, on storage the application cannot write to, which is what separates it from an application-emitted trace. Enforcement adds under 50 ms in internal testing against inference times of 500 ms to several seconds. Open-standard observability and identity-bound enforcement address different needs and can run on the same traffic, with the tracing watching and the proxy deciding.

If you are facing the August deadline, let's talk.