DeepInspect vs Aporia: Identity-Aware Enforcement Versus AI Observability for Enterprise Programs

DeepInspect and Aporia show up on the same shortlist when an enterprise is building an AI governance program. The two products started from different problem statements: DeepInspect from runtime HTTP enforcement and identity binding, Aporia from ML observability and the move into LLM guardrails. The shortlist conversation tracks which product produces the per-decision record the audit program will sample against.

TL;DR

DeepInspect is an identity-aware HTTP-proxy that authenticates the caller at the request boundary and commits per-decision audit records. Aporia is an AI observability platform with LLM guardrails that monitors model behavior, output quality, and policy violations across deployed AI systems. Pick DeepInspect if the program centers on identity-bound enforcement records under Article 12 or HIPAA. Pick Aporia if the program centers on model output quality, drift detection, and observability across production AI deployments. Programs sometimes run both.

Where DeepInspect sits

DeepInspect sits inline on the HTTP path between authenticated users or agents and any LLM. The proxy terminates TLS, authenticates against the corporate IdP, classifies the prompt content, evaluates policy against identity and classification, and commits a per-decision record before the model receives the request. The records carry identity, classification, policy version, decision, timestamp, and an integrity signature on a tamper-evident series.

The product's purpose is the runtime enforcement boundary plus the audit record series. The placement is at the request point, not on the inference output side.

Aporia: where it sits

Aporia ships an AI observability platform with LLM guardrails. The platform monitors model outputs, evaluates responses against custom policies (hallucination detection, PII leakage, brand-safety rules, organizational policy violations), and surfaces drift and quality signals over time. The LLM guardrail module can sit as an SDK in the application or as a proxy in front of the LLM, with the observability surface aggregating telemetry across deployments.

Aporia's lineage in ML observability shows up in the platform's strength on output quality, drift detection, and the analytical view across many models in production. The guardrail surface is the runtime enforcement piece and overlaps with the area DeepInspect occupies.

Feature comparison

| Axis | DeepInspect | Aporia | |---|---|---| | Primary surface | Runtime HTTP enforcement | AI observability plus LLM guardrails | | Primary placement | HTTP proxy at request boundary | SDK / proxy at inference path | | IdP integration | Built in at proxy | Application or proxy-side integration | | Identity binding on every record | Yes by default | When the integration carries it through | | Classification | Deterministic categories (PII, PHI, source code, customer, custom) | Custom policy evaluation including hallucination and brand safety | | Output evaluation | On the response on the same path | Strong output evaluation surface | | Drift detection | Outside primary scope | Yes | | Multi-provider coverage | Yes | Yes | | Tamper-evident record series | Yes (signed) | Available as platform feature | | Article 19 natural-person field | Yes by default | Yes when wired through | | Latency overhead | Under 50 ms in internal testing | Comparable on proxy path |

Pick Aporia if

• The program needs strong AI observability across many deployed models, including quality metrics, drift detection, and analytical dashboards.
• The risk model centers on output evaluation: hallucination detection, brand safety, custom output policies.
• The data science and ML platform teams own the AI governance program and want a tool that fits the ML lifecycle surface.

Pick DeepInspect if

• The program centers on identity-bound per-request records under EU AI Act Article 12 or HIPAA audit obligations.
• The deployment spans multiple LLM providers and the policy surface needs to be canonical.
• Enforcement happens at the request boundary before the model receives the request, with the identity context bound to every record by default.
• The IdP integration belongs at the inspection boundary rather than inside each application team's code.

Running both

Aporia's observability surface and DeepInspect's enforcement surface address adjacent problems. Aporia tells the program how the deployed models are behaving across many production paths over time. DeepInspect tells the program who made which request under which policy at which moment. The two surfaces can co-exist with DeepInspect carrying the per-decision audit record series and Aporia carrying the observability and quality monitoring view.

Regulatory framing

EU AI Act Article 12 requires automatic recording of events sufficient to ensure traceability. Article 19 specifies identification of natural persons involved on the record. Article 17 describes the quality management system the high-risk provider operates, including monitoring of model performance over time.

The Article 12 record series sits naturally on the HTTP-proxy enforcement layer because the identity context and the policy state are present at the request boundary. The Article 17 quality management view sits naturally on the observability layer because performance monitoring over time is the surface the platform was built for. A real program references both surfaces during an audit, with the proxy supplying per-decision records and the observability platform supplying quality and drift evidence.

Pricing approach

Both vendors quote against the deployment after scoping. DeepInspect prices per protected endpoint and request volume tier. Aporia prices across observability tiers and guardrail modules. Public price lists are not available for either product.

DeepInspect

DeepInspect is the identity-aware HTTP-proxy enforcement gateway. The proxy authenticates the caller against the corporate IdP, classifies the prompt content, evaluates policy against identity and classification, and commits a per-decision audit record before the response returns. The records carry the fields EU AI Act Article 12 and Article 19 expect on the series HIPAA Security Rule references.

For programs comparing DeepInspect to Aporia, the framing I find useful is to separate the audit record obligation from the observability obligation and pick the placement that supplies each one. DeepInspect supplies the per-decision record. Aporia supplies the observability surface across many production paths over time.

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