Can Datadog LLM Observability block a prompt?

It observes and alerts rather than enforcing inline. Guardrail and evaluation features score traces after the call completes, which produces a monitoring signal and a forensic record. Blocking requires a component on the request path with the authority to refuse, which is a different architectural position.

Does DeepInspect replace our observability platform?

DeepInspect replaces none of it. DeepInspect records the policy decision for every call, which is narrower than what an observability platform gives you. It carries no hallucination scoring, no drift analysis, and none of the correlation with infrastructure telemetry that makes Datadog valuable. The two answer different questions about the same traffic.

What happens to calls that skip the SDK?

Datadog records nothing for them, so they are invisible in the trace view. A proxy-based control still sees them, because reaching the provider means traversing the proxy. Uninstrumented services are common in practice, which is why coverage differs so much between the two models in real deployments.

Is sampling a problem for compliance evidence?

Sampling breaks compliance evidence. Sampling is correct for performance telemetry, where a representative subset answers the question. Compliance evidence answers questions about one specific request on one specific date, so a sampled store fails whenever the request under review fell outside the sample. Audit records have to be unsampled.

How does this compare to other observability tools?

The same read-path versus decision-path split applies across the category. Related breakdowns are in DeepInspect vs Langfuse and DeepInspect vs Helicone, and the general treatment is in AI gateway observability.

DeepInspect vs Datadog LLM Observability: The Record of What Happened and the Decision That Allows It

Datadog shipped LLM Observability into a platform that already held the infrastructure metrics, APM traces, and logs for most of the enterprises evaluating it. That distribution advantage is the product's strongest feature. An SRE who already lives in Datadog gets model call tracing, token accounting, latency breakdowns, and error rates next to the Kubernetes dashboards they had open anyway.

Teams then ask whether that covers the AI governance requirement landing on them in August. It covers part of it, and the part it leaves open is the part regulators ask about first.

I want to walk what each product does to a model call, where the two sit relative to each other, and how to decide.

TL;DR

Datadog LLM Observability instruments your application to record what your AI system did, then makes those traces searchable alongside the rest of your telemetry. DeepInspect sits inline on the HTTP path and decides whether each call proceeds, binding the decision to an authenticated identity and committing an independent audit record before the response returns.

Datadog LLM Observability: telemetry with context

Datadog's approach follows its APM heritage. You instrument the application with an SDK, spans flow into the platform, and a chain of LLM calls renders as a trace with the same shape as a distributed request trace. Prompt and completion payloads attach to spans. Token counts roll into cost dashboards. Latency percentiles behave like any other service metric.

The correlation is where it earns its money. When p99 latency on a customer-facing endpoint doubles, the ability to pivot from the application trace into the model call inside it, and then into the host metrics underneath, collapses a debugging session that would otherwise span three tools. Datadog also brings Sensitive Data Scanner to bear on payloads, so prompts carrying obvious PII patterns can be flagged or redacted in the telemetry pipeline.

Quality evaluation runs on sampled traces: hallucination checks, toxicity scoring, prompt-injection heuristics applied after the fact. These produce a monitoring signal you can alert on.

The architectural boundary is the SDK. Datadog observes what the instrumented application chose to report, which makes it excellent at describing the system you built and blind to the model call that skipped instrumentation entirely.

The decision before the call

DeepInspect runs as a stateless proxy on the wire between authenticated users or agents and any LLM endpoint. Nothing is sampled and nothing depends on the application volunteering a span.

Three consequences follow from that placement.

Enforcement becomes possible at that position. A policy violation returns a denial instead of an alert. Mandiant's M-Trends 2026, built on more than 500,000 hours of incident response, put the median handoff from initial access to a secondary threat group at 22 seconds, down from over eight hours in 2022 (Help Net Security). An alert that fires ninety seconds after a prompt exfiltrates a customer list is a forensic artifact.

Coverage becomes structural rather than optional. Traffic reaches the model through the proxy or it fails, so a service that forgot to install the SDK still gets policy applied. Shadow usage shows up as traffic rather than as absence of traffic.

The record gains independence from the application. The audit entry is written by a system the application cannot edit, which is what separates evidence from self-attestation. An application that generates a decision and also writes the log attesting to that decision has produced a document with a structural weakness that any competent auditor will find.

Feature comparison

| Capability | Datadog LLM Observability | DeepInspect | |---|---|---| | Model call tracing | Yes, deep and mature | Yes, per decision | | Token and cost accounting | Yes | Yes | | Latency and error monitoring | Yes, the deepest on this list | Basic | | Correlation with infra and APM telemetry | Yes, the core strength | Via export | | Quality and hallucination evaluation | Yes, sampled | No | | Sensitive data detection | Sensitive Data Scanner on payloads | Classification against customer data classes | | Blocking a request inline | No | Yes | | Identity bound per request to enterprise IdP | No | Core | | Coverage without app instrumentation | No, SDK required | Yes, proxy path | | Audit record independent of the application | No | Core | | Fail-closed behaviour | Not applicable | Configurable | | Policy versioning on each decision | No | Yes | | Primary buyer | Platform and SRE teams | Security and compliance |

The Article 12 test

The cleanest way to separate these two is to hold both against a specific regulatory requirement rather than a feature list. EU AI Act Article 12 requires automatic recording of events over the lifetime of a high-risk system to ensure traceability, including period of use, input data, and identification of the natural persons involved (Practical AI Act). High-risk obligations apply from August 2, 2026, with Tier 2 penalties reaching 15 million euro or 3% of global annual turnover under Article 99.

Datadog traces carry timestamps and payloads, and they carry them for the calls the application instrumented, sampled at whatever rate you configured, in a store the application team controls and with retention tuned for cost. Identification of the natural person is present only if the application attached it as a span tag.

DeepInspect writes one record per decision, unsampled, keyed on the authenticated identity, with the policy version and the outcome, committed before the response returns. That difference is not a quality judgement about Datadog. It is a description of two systems built for different jobs.

Pick Datadog LLM Observability if

You already run Datadog and want AI telemetry in the same pane as everything else.
Your pressing problem is latency, cost attribution, or debugging agent chains.
You need quality evaluation, hallucination scoring, and drift monitoring over time.
Your AI workloads sit outside a regulated boundary today.
Your team's operating model is alert and respond rather than prevent.

Pick DeepInspect if

You need to stop a request rather than learn about it.
Compliance requires per-decision records tied to a natural person or agent identity.
You cannot guarantee every service that calls a model is instrumented.
Different roles need enforced differences in model and data access.
Your evidence has to survive the question of who wrote it.

Running both

Most regulated teams I would advise end up with both, and the split is clean. Datadog answers how the AI system is performing. DeepInspect answers whether a given call was permitted and produces the record proving it. DeepInspect decisions can export into Datadog so the security signal lands in the same place as the operational one, which gives the SRE the pivot they want without moving the system of record for compliance evidence into a telemetry platform priced and retained as telemetry.

That last point is worth stating plainly, because retention is where this quietly breaks. Audit evidence for a high-risk AI system has a retention obligation measured in years. Observability data has a retention window measured in weeks, tuned down whenever the bill grows. Storing your compliance record in a system whose cost model encourages deleting it is a structural mismatch.

DeepInspect

This is the problem DeepInspect was built to solve. DeepInspect is a model-agnostic control plane that enforces usage policy in real time at the AI request boundary and produces audit-ready evidence for every AI decision.

For each request, DeepInspect resolves the identity the application presents, classifies the prompt against your own data classes, evaluates policy covering who is asking and what data is involved, and returns a pass or block decision before the request reaches the provider. Enforcement overhead measures under 50 ms in internal testing, against inference times of 500 ms to several seconds. Every decision commits a structured record containing identity, role, data classification, policy version, and outcome, written before the response returns on a path the calling application has no access to.

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