DeepInspect vs Helicone: Where LLM Observability Stops and Regulatory Audit Starts

Helicone is an open-source LLM observability and gateway platform. The product proxies LLM API calls (Helicone-hosted async proxy or the self-hosted gateway) and captures the request, the response, the latency, the cost, and any user-supplied metadata. The dashboard exposes the data by user, model, route, custom property, and time window for cost analysis, latency monitoring, prompt quality assessment, and per-customer attribution. The platform supports caching, rate limiting, retries, fallbacks, custom evaluators, and request scoring on top of the captured data. DeepInspect sits at the HTTP request boundary 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.

I want to walk through what Helicone does, what DeepInspect does, and where the two layers compose for regulated workloads.

TL;DR

Helicone is an LLM observability and gateway platform: proxy-based call capture, dashboards by user and model, caching, rate limiting, retries, fallbacks, custom properties, and evaluators. DeepInspect enforces identity-bound policy on prompt content at the request boundary and produces per-decision audit records formatted for regulatory review. Production deployments that need both observability and regulatory audit records run DeepInspect in front of Helicone, or run Helicone as the observability layer alongside DeepInspect at the request boundary.

Helicone: what it is and where it sits

Helicone runs in two deployment modes. The async proxy mode runs as a hosted gateway that the application addresses; Helicone forwards the request to the upstream LLM provider and captures the request and response asynchronously to its backend. The self-hosted gateway mode runs the same proxy as a container the team operates. In either mode, the gateway sits between the calling application and the LLM provider, capturing the call metadata.

The Helicone feature surface covers the operational and observability concerns. The dashboard exposes captured calls by time, user, model, route, custom property, and evaluation score. Caching reuses responses across identical requests. Rate limiting applies per-key or per-property limits. Retries and fallback chains handle transient failures. Custom properties let the application attach arbitrary metadata to the call (user ID, session ID, route name, request type) that the dashboard groups against. Custom evaluators score responses asynchronously for offline quality review. Prompt management versions prompt templates and tracks which version produced which output.

The architectural sweet spot for Helicone is the AI engineering team that needs visibility into LLM application behavior, cost attribution, latency monitoring, and prompt experimentation on a single observability surface. The product is the application-side observability layer for the LLM traffic.

What DeepInspect is and where it sits

DeepInspect sits at the HTTP request boundary as a separate enforcement layer. 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, not the API key alone), 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 run as an HTTP layer in front of LLM providers. Both products capture request and response data. The overlap is at the surface level. The audit format and the responsibility differ.

Helicone's identity context is the API key the application sends or the custom property (user ID, session ID) the application attaches. The captured record carries the request, the response, the latency, the cost, and the custom metadata. The audit surface is the dashboard, which the engineering team consults to review behavior.

DeepInspect's identity context is the natural-person identity attached at the application's identity primitive, with the tenant, the role, the route, and the policy version active at the time of the decision. The audit record carries the policy decision, the data classification outcome, the cryptographic integrity signature, and the route context. The audit format is what a regulator reviewing the deployment under Article 12 expects to see.

Both products produce records. Only one of them produces a record format that a regulator under Article 12 or Fannie Mae LL-2026-04 will accept without translation.

Feature comparison

| Feature | Helicone | DeepInspect | |---|---|---| | HTTP proxy for LLM traffic | Yes | Yes | | Multi-provider observability | Yes (200+ providers) | Forwards to a configured upstream | | Caching | Yes | Out of scope | | Rate limiting | Yes | Out of scope | | Retries and fallbacks | Yes | Out of scope | | Custom property metadata | Yes | Yes (policy version, classification) | | Custom evaluators and scoring | Yes | Out of scope | | Prompt template versioning | Yes | Out of scope | | Identity attribution at model API call | API key or custom user property | 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 | Captured call record | Cryptographically signed audit record | | Article 12 audit format | Capture plus translation | Native format | | Fannie Mae LL-2026-04 lender record format | Capture plus translation | Native format | | Self-hosted | Yes (open-source gateway) | Yes |

Pick Helicone if

Pick Helicone if the team's primary need is application-side LLM observability with dashboards by user, model, route, and custom property, and the regulatory audit requirement is satisfied elsewhere or the workload does not trigger a regulatory audit requirement. Helicone is the strongest open-source choice for the observability layer with the proxy-based deployment model that does not require SDK changes to most applications.

Pick Helicone if the AI engineering team wants caching, rate limiting, retries, fallbacks, custom evaluators, and prompt management on the same observability surface as the captured call data.

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. DeepInspect produces the record format that the regulator accepts. Helicone's captured call data covers the observability use case; it falls short of the per-decision audit record format the regulator under Article 12 expects.

Pick DeepInspect if the security team needs identity-bound policy enforcement at the request boundary, with the policy decision producing the audit evidence independently of the application's observability path.

Pick both if the deployment needs application-side observability and regulated-workload audit. The composition pattern works in production today.

Composition pattern in production

The deployment topology that runs in production combines the two layers. The application points its HTTP client at DeepInspect. DeepInspect verifies the caller's identity from the application's identity primitive, applies the data classification rules, evaluates the policy bundle for the route, commits the per-decision audit record, and forwards the cleared request to Helicone (or directly to the upstream LLM provider, with Helicone running as an async proxy that observes the call). Helicone captures the call data and writes it to the observability backend. The response flows back through Helicone to DeepInspect to the application.

The audit record carries the natural-person identity, the route, the policy version, the data classification outcome, the policy decision outcome, the upstream provider that served the request, and the integrity signature. The Helicone captured call carries the application-side custom properties, the latency, the cost, the cache hit or miss, and the evaluator scores. The two record formats serve different audit pipelines: DeepInspect's feeds the regulatory audit obligation; Helicone's feeds the AI engineering team's observability surface.

Pricing approach

Helicone publishes pricing tiers (free hobby tier, paid growth tier, enterprise tier) on its website. The self-hosted gateway is available under an open-source license.

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 Helicone by running in series (DeepInspect at the request boundary, Helicone immediately behind) or in parallel (DeepInspect at the request boundary, Helicone observing via the async proxy mode).

The composition gives organizations the application-side observability they want from Helicone 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 Helicone observability surface produces the AI engineering team's review.

If you are running Helicone today and the EU AI Act August 2 deadline applies to the workload, let's talk.