DeepInspect vs Langfuse: Where LLM Observability Stops and Inline Enforcement Starts
Langfuse is an open-source LLM observability platform. It captures traces, spans, prompts, completions, and evaluation results, and lets a team review and score LLM application behavior offline. DeepInspect sits at the HTTP request boundary in front of LLM endpoints and answers a different question: identity-bound policy on prompt content, per-route data classification, and a per-decision audit record formatted for EU AI Act Article 12 review. Langfuse observes after the fact. DeepInspect enforces inline. This piece walks through what each one does and how the two layers compose.
Langfuse is an open-source LLM observability platform. The product captures traces and spans from LLM application code (via the Langfuse SDKs for Python, JavaScript, and OpenAI), stores prompts, completions, metadata, evaluation scores, and user feedback, and exposes a review console where teams audit LLM application behavior offline. The platform supports prompt versioning, dataset management, evaluation runs, and side-by-side comparison of completions. DeepInspect sits at the HTTP request boundary in front of LLM endpoints and answers a different question. It enforces identity-bound policy on prompt content, classifies prompt data against the regulated data types the organization recognizes, and commits a per-decision audit record that a reviewer under EU AI Act Article 12 or a Fannie Mae LL-2026-04 review accepts.
Langfuse observes after the fact. DeepInspect enforces inline. I want to walk through what each one does, where the responsibilities split, and how the two layers compose in production.
TL;DR
Langfuse captures LLM application traces (prompts, completions, evaluations, scores) for offline review and prompt experimentation. DeepInspect enforces identity-bound policy on prompt content at the HTTP request boundary and produces per-decision audit records formatted for regulatory review. The two products answer different questions: Langfuse helps the AI engineering team see what their app did; DeepInspect produces the regulatory evidence that the app was governed when it did it. Production deployments run both, with DeepInspect at the request boundary and Langfuse capturing the application-side trace.
Langfuse: what it is and where it sits
Langfuse sits inside the application code path. The Langfuse SDK wraps OpenAI client calls or Anthropic client calls, captures the request and response, attaches a trace identifier, and ships the trace to the Langfuse backend (Langfuse Cloud or self-hosted Langfuse). The SDK also captures user-defined metadata (user ID, session ID, tags, custom attributes) that the application attaches. Traces compose into spans for multi-step LLM workflows (retrieval, multiple model calls, post-processing).
The Langfuse feature set covers the offline review surface. The dashboard exposes traces by time, user, model, route, and custom metadata. Evaluation pipelines run LLM-as-judge or custom evaluators against traces. Datasets capture trace inputs and outputs for regression testing. Prompt management versions prompt templates and tracks which prompt version produced which output. The scoring system attaches numerical scores to traces for offline quality assessment.
The architectural sweet spot for Langfuse is the AI engineering team that needs to see what their LLM application did, why a specific output occurred, and how a prompt change affected outputs at scale. The product is the offline review surface for the application-side trace data.
What DeepInspect is and where it sits
DeepInspect sits at the HTTP request boundary, addressable from any application that calls any LLM endpoint over HTTP. It evaluates identity-bound policy on every request before the request reaches the model provider, classifies prompt data against the regulated data types the organization recognizes, and commits a per-decision audit record with cryptographic integrity. The decisions are deterministic, fail-closed, and independent of the model's behavior.
The feature set covers identity attribution at the model API call from the application's identity primitive (the natural-person identity, the tenant, the role, the route context), per-route policy enforcement for different application surfaces (the support route, the developer route, the legal route, the underwriting route), prompt-level data classification (PII, PHI, MNPI, source code, source-licensed content, regulated jurisdictional data), policy decisions that pass, block, or modify the request, and the per-decision audit record format that downstream audit pipelines consume.
The architectural sweet spot for DeepInspect is the regulated workload. An organization that is the data controller for prompts crossing into a model provider needs evidence that satisfies the deployer obligations under Article 26, the audit obligations under Article 12, the lender record obligations under Fannie Mae LL-2026-04, and the sector-specific regimes (HIPAA, DORA, FedRAMP, ISO 42001) that the workload is subject to.
Where the two products overlap
Both products produce records of LLM interactions. Both products attach metadata to the record. The overlap is at the surface level. The responsibility differs fundamentally.
Langfuse runs after the request has reached the LLM. The trace captures what the application sent and what the application received. The SDK is in-process, owned by the application, and the trace is shipped to Langfuse asynchronously. If the application skips the SDK call, no trace exists. If the LLM returns sensitive content the application should not have produced, Langfuse records it but the data has already left the model.
DeepInspect runs before the request reaches the LLM. The policy decision blocks, rewrites, or passes the request based on identity, classification, and route. The audit record is independent of the application's logging path and cryptographically signed. The application cannot bypass the audit record by skipping a SDK call because the audit lives at the network layer, outside the application's control plane.
Both products produce records. One observes the past; the other enforces the present and produces the evidence of enforcement.
Feature comparison
| Feature | Langfuse | DeepInspect | |---|---|---| | Inline HTTP enforcement | No | Yes | | Block sensitive prompts before they reach the LLM | No | Yes | | Application-side trace capture (prompts, completions, spans) | Yes | Out of scope | | Prompt version management | Yes | Out of scope | | LLM-as-judge evaluations | Yes | Out of scope | | Dataset and regression testing | Yes | Out of scope | | Identity attribution at the model API call | Application-supplied user ID | Natural-person from IdP | | Per-route policy bundle | None | Yes, policy bundle per route | | Prompt data classification | None | Classification engine for PII, PHI, MNPI | | Per-decision audit record | Application trace | Cryptographically signed audit record | | Article 12 audit format | Application trace plus translation | Native format | | Fannie Mae LL-2026-04 lender record format | Application trace plus translation | Native format | | Self-hosted | Yes (open-source) | Yes |
Pick Langfuse if
Pick Langfuse if the team's primary need is offline observability and review of LLM application traces, prompt version management, and evaluation pipelines. Langfuse is the strongest open-source choice for the application-side trace surface and the self-hosted deployment fits teams that want to own the data.
Pick Langfuse if the AI engineering team is iterating on prompts and the workflow needs side-by-side comparison of outputs, scoring, and regression testing across prompt versions.
Pick DeepInspect if
Pick DeepInspect if the workload is subject to EU AI Act Article 12, Fannie Mae LL-2026-04, HIPAA, DORA, FedRAMP, ISO 42001, or any sector regime that requires identity-bound per-decision audit records produced independently of the application's logging path. DeepInspect produces the record format that the regulator accepts. Langfuse traces help the engineering team review behavior offline; they fall short of the audit format the regulator under Article 12 expects.
Pick DeepInspect if the security team needs to block sensitive prompts before they reach the LLM. Langfuse captures the trace after the LLM has processed it; the data has already left the application boundary by the time the trace is written.
Pick both if the deployment needs production policy enforcement and application-side observability. The composition pattern works in production today.
Composition pattern in production
The deployment topology runs both layers in parallel. The application points its HTTP client at DeepInspect, which evaluates the policy, classifies the prompt data, commits the per-decision audit record, and forwards the cleared request to the upstream LLM provider. The Langfuse SDK inside the application code captures the trace for the same call (prompt, completion, span metadata, user ID, custom attributes) and ships it to the Langfuse backend asynchronously.
The DeepInspect audit record carries the natural-person identity, the policy version, the data classification outcome, the policy decision outcome, and the cryptographic integrity signature. The Langfuse trace carries the application-side metadata, the prompt content, the completion, the evaluation scores, and the user feedback. The two record formats serve different audit pipelines: DeepInspect's audit pipeline feeds the regulatory audit obligation; Langfuse's trace feeds the AI engineering team's offline review.
For the cross-record consolidation, the audit pipeline can join on the request identifier that both products emit. The DeepInspect audit record carries the request ID; the Langfuse trace carries the same request ID if the application threads the ID through the trace metadata. The joined record gives the regulator the enforcement evidence and the engineering team the application-side context.
Pricing approach
Langfuse is open-source under the MIT license. Self-hosted deployment is free. The Langfuse Cloud hosted offering has its own pricing that the Langfuse team publishes separately.
DeepInspect's pricing is communicated through sales conversations and depends on the deployment regime, the workload volume, and the audit-record retention requirements. The cost is meaningfully lower than the cost of an audit miss under EU AI Act Article 12, Fannie Mae LL-2026-04, or a sector regime.
DeepInspect
DeepInspect sits between calling applications and any LLM endpoint over HTTP. It evaluates identity-bound policy on every request, classifies prompt data against the regulated data types the organization recognizes, commits per-decision audit records with cryptographic integrity, and produces the record format that EU AI Act Article 12 and Fannie Mae LL-2026-04 reviewers accept. The architecture composes with Langfuse by running in parallel at different layers: DeepInspect at the request boundary, Langfuse inside the application code.
The composition gives organizations the application-side observability they want from Langfuse and the per-decision audit records they need for the workload to survive regulatory review. The DeepInspect audit pipeline produces the regulator-facing evidence; the Langfuse traces produce the AI engineering team's review surface. The two coexist without overlap.
If you are running Langfuse today and the EU AI Act August 2 deadline applies to the workload, let's talk.
Frequently asked questions
- How is Langfuse different from DeepInspect?
Langfuse is an open-source LLM observability platform that captures application traces (prompts, completions, spans, evaluations, user feedback) via in-process SDKs and exposes a review dashboard for offline analysis. DeepInspect is an identity-bound policy enforcement layer at the HTTP request boundary that classifies prompt data, evaluates per-route policy bundles, and commits per-decision audit records formatted for EU AI Act Article 12 review and sector audit requirements. Langfuse observes what the application did. DeepInspect enforces what the application was allowed to do and produces the regulatory evidence.
- Can Langfuse replace DeepInspect for a regulated workload?
For workloads where the audit format the regulator accepts is the application-side trace, possibly. For workloads subject to EU AI Act Article 12, Fannie Mae LL-2026-04, HIPAA, DORA, FedRAMP, or any sector regime that requires identity-bound per-decision audit records, the application-side trace falls short. The trace lives inside the application's logging path and depends on the application calling the SDK. The audit format the regulator expects is independent of the application's control, with the natural-person identity attribution, the policy version active at decision time, the data classification outcome, and the cryptographic integrity signature.
- Can DeepInspect replace Langfuse?
For the offline review surface that Langfuse provides (prompt version management, LLM-as-judge evaluations, datasets, regression testing, scoring), DeepInspect is out of scope. The two products serve different audiences: Langfuse serves the AI engineering team's iteration workflow; DeepInspect serves the security and compliance team's policy enforcement and audit obligation. The two compose.
- How does the deployment topology work when both are in production?
The application points its HTTP client at DeepInspect. DeepInspect evaluates the policy, commits the audit record, and forwards the cleared request to the upstream LLM provider. The Langfuse SDK inside the application captures the trace for the same call (prompt, completion, metadata, user ID) and ships it to the Langfuse backend asynchronously. The DeepInspect audit record covers the regulatory audit obligation; the Langfuse trace covers the application-side observability.
- What about the request identifier for cross-record consolidation?
Both DeepInspect and Langfuse emit per-call identifiers. DeepInspect attaches a request ID to the audit record. The Langfuse SDK accepts a trace ID and span ID from the application. If the application threads the DeepInspect request ID through the Langfuse trace metadata, the two records join on the request ID for combined review. The regulator sees the DeepInspect audit record; the engineering team sees the Langfuse trace with the matching ID for application-side context.