How is DeepInspect different from Galileo?

Galileo is an evaluation and observability platform. It instruments LLM applications via SDK to capture traces, then scores those traces for hallucination, grounding, and response quality on a monitoring cadence, usually reviewed by an ML or application engineering team iterating on prompts and model choices. DeepInspect is an inline proxy that sits in the live request path between an authenticated user or agent and the LLM. It evaluates identity and policy for each request before the model sees it and can block or redact before generation happens. Galileo tells you whether output was good after the fact, on a dashboard someone checks weekly. DeepInspect decides whether the request was permitted before it happens, at the moment it happens, and writes a signed record of that decision that a regulator can request later. The two operate at different points in the request lifecycle and answer different questions, which is why regulated teams often run both rather than choosing one over the other.

Can Galileo replace an access control and audit layer like DeepInspect?

No, because Galileo's traces are not identity-bound authorization decisions. A trace shows what the application sent and what the model returned. It does not evaluate whether the authenticated caller was permitted to submit that specific prompt against that specific data classification under current policy. Galileo also does not sit inline in a way that blocks a request before it reaches the model; it observes and scores after the fact. If a security review team asks for proof that access to a specific dataset was restricted per role and per policy on a given date, a quality-scoring trace does not satisfy that request, because the trace was never evaluating authorization in the first place. That evidence requires an enforcement point that makes the access decision itself and commits the record independently of the application that issued the request.

Can DeepInspect replace Galileo for output quality monitoring?

No, and it was never built to. DeepInspect evaluates whether a request is authorized and applies policy to prompts and responses; it does not score hallucination rates, grounding accuracy, or response relevance the way a purpose-built evaluation platform does, and it has no dashboard for tracking quality drift across prompt template versions. A team that needs to know whether their model's answers are getting better or worse across iterations, or whether a new system prompt quietly degraded grounding, still needs an evaluation tool built for that job. DeepInspect and Galileo solve adjacent problems that happen to touch the same traffic: one governs who can ask and what happens to the request itself, the other measures whether the answer that came back was actually good. Neither substitutes for the other's core function, and pretending otherwise leaves a real gap on one side or the other.

Do we need both Galileo and DeepInspect?

Teams shipping LLM applications into regulated environments typically end up running both, usually not by original design. Galileo, or a comparable evaluation platform, answers the product question: is the model's output accurate, safe, and improving over time as prompts and models change. DeepInspect answers the compliance and security question: was this specific request authorized under current policy, and can the organization prove that months later during a review. An engineering team optimizing prompt quality cares most about the first question. A CISO facing an EU AI Act Article 12 audit or a Fannie Mae LL-2026-04 review cares most about the second. Running only one tool leaves a real gap: quality tooling alone has no access control story to tell an auditor, and enforcement alone has no visibility into whether the model's answers are actually any good.

Does DeepInspect work with the same LLM providers Galileo supports?

DeepInspect is model-agnostic because it operates on the HTTP layer between the caller and any LLM endpoint, rather than inside a provider-specific SDK. It works in front of OpenAI, Anthropic, Bedrock, Azure OpenAI, Vertex, and self-hosted endpoints running Llama or Mistral, the same broad provider coverage most evaluation platforms advertise. The integration point is where the two approaches diverge. Galileo's SDK has to be instrumented correctly inside every service that calls a model, which means new services or forgotten integrations create blind spots in the trace coverage. DeepInspect enforces at the request boundary itself, ahead of every call that passes through the proxy, so coverage tracks the network path rather than whether a developer remembered to add instrumentation to a new microservice before it shipped. For a platform team running dozens of internal services against the same set of model endpoints, that difference in where coverage comes from tends to matter more over time than the initial provider list.

Where does DeepInspect sit relative to my existing observability stack?

DeepInspect sits inline at the AI request boundary, ahead of the model and ahead of the response reaching the calling application. Observability and evaluation tools like Galileo typically sit inside the application itself, capturing traces via SDK instrumentation as requests flow through the code the application team owns. The two occupy different positions in the stack rather than competing for the same one. DeepInspect's enforcement decisions and audit records operate independently of whatever observability tooling an engineering team has wired up, which matters in practice because SDK instrumentation gets misconfigured, disabled during a refactor, or simply left out of a new service under deadline pressure. An external proxy sitting on the network path does not rely on any application team remembering to instrument anything correctly, which is a meaningfully different failure mode than an SDK trace that simply stops appearing in a dashboard and nobody notices for a week.

Is a "Galileo alternative" the same thing as what DeepInspect does?

Not quite, and the distinction is worth making explicit. Someone searching for a "Galileo alternative" is often actually looking for one of two different things: another evaluation and observability platform doing the same job as Galileo, which is a true like-for-like alternative, or an access-control and audit layer that their evaluation platform was never built to provide in the first place. If the gap is quality monitoring, hallucination tracking, or prompt-iteration scoring, the honest answer is to compare other evaluation platforms against Galileo directly. If the gap is identity-aware enforcement, blocking decisions on live traffic, or audit-ready evidence for a regulatory review, that sits in an entirely different layer of the stack, and DeepInspect is built for exactly that layer. Getting this distinction wrong early in a procurement process tends to cost a team a second buying cycle six months later, once the security review surfaces the actual requirement.

What does DeepInspect's audit record contain that a Galileo trace does not?

A DeepInspect audit record contains the verified identity of the caller, the role and authorization context in effect at the moment of the request, the data classification applied to the prompt, the policy version that governed the decision, the outcome (permit, redact, or deny), and a timestamp precise enough to correlate against other systems, all cryptographically signed and committed before the response reaches the calling application. A Galileo trace typically contains the prompt, the response, and the quality scores computed against that exchange, which is exactly what an engineering team needs to debug a model regression. The Galileo trace stays valuable for understanding model behavior long after the fact. It was simply never evaluating who was authorized to make the request, so it has nothing to offer an auditor asking that specific question, and no amount of dashboard polish changes what the underlying trace was designed to capture.

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August 22, 2026

DeepInspect vs Galileo: Runtime Enforcement vs Output Evaluation

Galileo evaluates whether an LLM output is accurate and safe, scored after generation. DeepInspect enforces identity-bound policy on the live request before it reaches the model and produces a signed audit record. Teams evaluating a Galileo alternative for access control need to know these are different layers, not competing products.

ByParminder Singh· Founder & CEO, DeepInspect Inc.

Comparisons & Alternativesllmllm-securityai-securitypolicy-enforcementforensic-auditinline-enforcement

DeepInspect vs Galileo: Runtime Enforcement vs Output Evaluation

A CISO I spoke with in July had already deployed Galileo across three LLM applications. Hallucination scores were tracked weekly. Prompt quality was trending up. Then their security review team asked a different question: who is allowed to call the underwriting model with a customer's SSN in the prompt, and can you prove that decision six months from now. Galileo had no answer, because Galileo was never built to answer it.

That gap is the subject of this piece. Galileo and DeepInspect get compared because both show up in AI security procurement conversations, and both touch the same LLM traffic. The overlap ends there. Galileo measures whether output is good. DeepInspect decides, per request, whether the request is permitted, and writes down what it decided.

TL;DR

Galileo is an evaluation and observability platform: hallucination detection, prompt and response scoring, quality dashboards, built for teams iterating on LLM application quality. DeepInspect is an inline policy enforcement proxy: identity-bound access control and signed audit records for every request between a user or agent and an LLM. Run Galileo to know if your model's answers are good. Run DeepInspect to know who was allowed to ask, and to prove it during a regulatory review. Many regulated teams run both, one layer for quality, one for access control and evidence.

Galileo

Galileo is an AI evaluation and observability platform for teams building LLM applications. Integration is typically SDK-based: developers instrument their application code so Galileo captures traces of prompts, responses, and intermediate reasoning steps as the application runs.

Once captured, those traces get scored. Galileo's metrics cover hallucination detection, factual grounding against source documents, response relevance, and guardrail-style safety checks on generated content. The dashboards let a team watch quality trends across model versions, prompt template changes, and time. If a new system prompt quietly degrades grounding scores, an evaluation platform is how a team catches it before customers do.

The operating cadence is evaluation and observability. Traces get captured and scored, often in batches or on a monitoring loop, to answer "is this model's output good, and is quality holding up over time." That is a genuinely useful question for anyone shipping LLM features into production. It is a different question from "should this specific request, from this specific caller, be allowed to happen right now."

The inline enforcement proxy

DeepInspect is a stateless proxy that sits inline between authenticated users or agents and any LLM. Every HTTP request to a model API passes through it before the model sees it, and every response passes through it before the caller sees it.

The evaluation happens per request, not on a monitoring cadence. DeepInspect looks at who is asking (the identity context supplied by the calling application), what role or authorization they carry, and what policy decision point governs that combination, then makes a pass, redact, or block decision before the traffic reaches the model. The proxy is model-agnostic: it works in front of OpenAI, Anthropic, Bedrock, Azure OpenAI, Vertex, or a self-hosted endpoint, because it operates on the HTTP layer rather than inside any one provider's SDK. This is the same inline enforcement pattern I argued for in 22-Second Breach Windows Mean Your AI Enforcement Must Be Inline. Google Mandiant's M-Trends 2026 report found the median handoff time from initial access to a secondary threat group collapsed to 22 seconds. At that tempo, a decision that happens after generation is a decision that happens too late.

Every decision produces a signed, tamper-evident audit record: identity, policy version, data classification, outcome, timestamp. The record commits before the response reaches the calling application, which keeps the evidence outside the calling application's own custody. That write-path independence is the property a regulator or an internal security review actually wants to see.

Feature comparison

The two products rarely compete for the same line item on a procurement sheet, because they answer different questions about the same traffic. The breakdown below lays out where each one actually operates.

What it measures: Galileo measures output quality (hallucination, grounding, relevance). DeepInspect measures request legitimacy (identity, authorization, policy compliance).
When it acts: Galileo evaluates on a monitoring or batch cadence, after or alongside generation. DeepInspect evaluates per request, before the request reaches the model.
Enforcement capability: Galileo surfaces quality scores and alerts. DeepInspect makes a pass, redact, or block decision inline, before damage is possible.
Identity awareness: Galileo traces are tied to the application and session. DeepInspect decisions are bound to the authenticated caller's identity and role.
Audit output: Galileo produces quality dashboards and trend reports. DeepInspect produces signed, per-decision audit records built for regulatory disclosure.
Model dependency: Both are model-agnostic in the sense of supporting multiple LLM providers, though DeepInspect's enforcement sits at the HTTP boundary rather than inside an SDK trace.
Primary buyer: Galileo is typically bought by ML/AI engineering teams optimizing application quality. DeepInspect is typically bought by security, compliance, or platform teams that own access control and audit obligations.
Integration pattern: Galileo integrates via SDK instrumentation inside the application code. DeepInspect integrates as a proxy in the request path, independent of application code changes to the calling logic.
Failure mode on error: Galileo's evaluation continues to log and score regardless of outcome. DeepInspect can fail closed, denying the request by default when policy evaluation is ambiguous or unavailable.

Pick Galileo if...

Galileo fits teams whose open question is still about the model's answers, not about who is allowed to ask.

Your priority is measuring hallucination rates and factual grounding across model versions and prompt changes.
You need to track output quality drift over time as your team iterates on prompts, fine-tunes, or swaps model providers.
You want evaluation datasets and automated scoring to support prompt engineering and model selection decisions.
Your team's open question is "is the model's answer good," not "who was allowed to ask."

Pick DeepInspect if...

DeepInspect fits teams whose question has shifted from output quality to access control and evidence.

You need per-request, identity-aware enforcement and blocking or redaction decisions on live AI traffic, not after-the-fact scoring.
A regulator, auditor, or security review team can ask "who accessed this data through the model, under what policy, and can you prove it" and you need a real answer.
You are running toward the EU AI Act's Article 12 logging deadline, which takes effect August 2, 2026, and your compliance evidence depends on records the application itself does not control.
You need enforcement that works the same way in front of any LLM endpoint, independent of which team's SDK instrumentation happens to be wired up correctly that week.

Teams that run both layers rarely planned it that way from day one. Quality tooling arrived first because an engineering team needed it to ship. Enforcement arrived later once someone in security or legal asked the access-control question that quality tooling was never built to answer. Watching a well-instrumented Galileo rollout stall in a security review over a question it was never designed to answer is an avoidable, and slightly frustrating, way to lose a quarter.

DeepInspect

This is the identity-aware enforcement gap Galileo was never built to close. Galileo's SDK traces tell you what a model said. They do not tell you whether the caller was authorized to ask the question in the first place, and an evaluation dashboard has no mechanism to block a prompt carrying restricted data before it reaches the model.

DeepInspect sits at the AI request boundary as a model-agnostic proxy, evaluating identity, role, and data classification on every request and producing a signed audit record for every decision. It runs alongside output-quality tooling rather than replacing it: Galileo answers whether the model's output is good, DeepInspect answers who was allowed to generate it and proves the answer later. The same distinction applies to model-side safety training, which I covered in Model Guardrails Are Not a Security Control: probabilistic behavior inside the model is not the same thing as a deterministic, externally auditable decision at the request boundary.

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

Frequently asked questions

How is DeepInspect different from Galileo?: Galileo is an evaluation and observability platform. It instruments LLM applications via SDK to capture traces, then scores those traces for hallucination, grounding, and response quality on a monitoring cadence, usually reviewed by an ML or application engineering team iterating on prompts and model choices. DeepInspect is an inline proxy that sits in the live request path between an authenticated user or agent and the LLM. It evaluates identity and policy for each request before the model sees it and can block or redact before generation happens. Galileo tells you whether output was good after the fact, on a dashboard someone checks weekly. DeepInspect decides whether the request was permitted before it happens, at the moment it happens, and writes a signed record of that decision that a regulator can request later. The two operate at different points in the request lifecycle and answer different questions, which is why regulated teams often run both rather than choosing one over the other.
Can Galileo replace an access control and audit layer like DeepInspect?: No, because Galileo's traces are not identity-bound authorization decisions. A trace shows what the application sent and what the model returned. It does not evaluate whether the authenticated caller was permitted to submit that specific prompt against that specific data classification under current policy. Galileo also does not sit inline in a way that blocks a request before it reaches the model; it observes and scores after the fact. If a security review team asks for proof that access to a specific dataset was restricted per role and per policy on a given date, a quality-scoring trace does not satisfy that request, because the trace was never evaluating authorization in the first place. That evidence requires an enforcement point that makes the access decision itself and commits the record independently of the application that issued the request.
Can DeepInspect replace Galileo for output quality monitoring?: No, and it was never built to. DeepInspect evaluates whether a request is authorized and applies policy to prompts and responses; it does not score hallucination rates, grounding accuracy, or response relevance the way a purpose-built evaluation platform does, and it has no dashboard for tracking quality drift across prompt template versions. A team that needs to know whether their model's answers are getting better or worse across iterations, or whether a new system prompt quietly degraded grounding, still needs an evaluation tool built for that job. DeepInspect and Galileo solve adjacent problems that happen to touch the same traffic: one governs who can ask and what happens to the request itself, the other measures whether the answer that came back was actually good. Neither substitutes for the other's core function, and pretending otherwise leaves a real gap on one side or the other.
Do we need both Galileo and DeepInspect?: Teams shipping LLM applications into regulated environments typically end up running both, usually not by original design. Galileo, or a comparable evaluation platform, answers the product question: is the model's output accurate, safe, and improving over time as prompts and models change. DeepInspect answers the compliance and security question: was this specific request authorized under current policy, and can the organization prove that months later during a review. An engineering team optimizing prompt quality cares most about the first question. A CISO facing an EU AI Act Article 12 audit or a Fannie Mae LL-2026-04 review cares most about the second. Running only one tool leaves a real gap: quality tooling alone has no access control story to tell an auditor, and enforcement alone has no visibility into whether the model's answers are actually any good.
Does DeepInspect work with the same LLM providers Galileo supports?: DeepInspect is model-agnostic because it operates on the HTTP layer between the caller and any LLM endpoint, rather than inside a provider-specific SDK. It works in front of OpenAI, Anthropic, Bedrock, Azure OpenAI, Vertex, and self-hosted endpoints running Llama or Mistral, the same broad provider coverage most evaluation platforms advertise. The integration point is where the two approaches diverge. Galileo's SDK has to be instrumented correctly inside every service that calls a model, which means new services or forgotten integrations create blind spots in the trace coverage. DeepInspect enforces at the request boundary itself, ahead of every call that passes through the proxy, so coverage tracks the network path rather than whether a developer remembered to add instrumentation to a new microservice before it shipped. For a platform team running dozens of internal services against the same set of model endpoints, that difference in where coverage comes from tends to matter more over time than the initial provider list.
Where does DeepInspect sit relative to my existing observability stack?: DeepInspect sits inline at the AI request boundary, ahead of the model and ahead of the response reaching the calling application. Observability and evaluation tools like Galileo typically sit inside the application itself, capturing traces via SDK instrumentation as requests flow through the code the application team owns. The two occupy different positions in the stack rather than competing for the same one. DeepInspect's enforcement decisions and audit records operate independently of whatever observability tooling an engineering team has wired up, which matters in practice because SDK instrumentation gets misconfigured, disabled during a refactor, or simply left out of a new service under deadline pressure. An external proxy sitting on the network path does not rely on any application team remembering to instrument anything correctly, which is a meaningfully different failure mode than an SDK trace that simply stops appearing in a dashboard and nobody notices for a week.
Is a "Galileo alternative" the same thing as what DeepInspect does?: Not quite, and the distinction is worth making explicit. Someone searching for a "Galileo alternative" is often actually looking for one of two different things: another evaluation and observability platform doing the same job as Galileo, which is a true like-for-like alternative, or an access-control and audit layer that their evaluation platform was never built to provide in the first place. If the gap is quality monitoring, hallucination tracking, or prompt-iteration scoring, the honest answer is to compare other evaluation platforms against Galileo directly. If the gap is identity-aware enforcement, blocking decisions on live traffic, or audit-ready evidence for a regulatory review, that sits in an entirely different layer of the stack, and DeepInspect is built for exactly that layer. Getting this distinction wrong early in a procurement process tends to cost a team a second buying cycle six months later, once the security review surfaces the actual requirement.
What does DeepInspect's audit record contain that a Galileo trace does not?: A DeepInspect audit record contains the verified identity of the caller, the role and authorization context in effect at the moment of the request, the data classification applied to the prompt, the policy version that governed the decision, the outcome (permit, redact, or deny), and a timestamp precise enough to correlate against other systems, all cryptographically signed and committed before the response reaches the calling application. A Galileo trace typically contains the prompt, the response, and the quality scores computed against that exchange, which is exactly what an engineering team needs to debug a model regression. The Galileo trace stays valuable for understanding model behavior long after the fact. It was simply never evaluating who was authorized to make the request, so it has nothing to offer an auditor asking that specific question, and no amount of dashboard polish changes what the underlying trace was designed to capture.

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