AI observability tools, compared.
AI failures look like 'the assistant got weird,' not a stack trace. These are the tools that turn AI behavior into something you can actually page on.
Pure observability tools (Helicone, Langfuse, Arize) are great for visibility; full control planes (stackcontrolai) unify traces with routing, policy, and cost. Datadog and New Relic are catching up where your APM already lives.
End-to-end traces, evals, drift, and SLOs for every model call — joined with the routing, policy, and cost ledger so on-call sees the whole story in one timeline.
Read the platform pageWhat separates serious vendors from demos.
End-to-end traces
Prompts, tool calls, retries, and approvals in one timeline.
Evals
Rule-based, classifier, model-graded, and human-graded scoring.
Drift detection
Distribution shifts on inputs, outputs, and embedding spaces.
SLOs + alerting
p50/p95/p99 latency and quality SLOs per route.
Replay
One-click replay of any failed run against a different model or prompt.
Exports
Stream traces to Datadog, Honeycomb, SIEM, and the warehouse.
AI observability tools at a glance.
| Vendor | Best for | Deployment | Governance | Pricing | Link |
|---|---|---|---|---|---|
stackcontrolaifeatured | Observability on the same plane as routing, policy, and cost | SaaS · VPC · self-host | Policy DSL · RBAC · tamper-proof audit | Usage + enterprise | Open |
Langfuse | Open-source LLM observability with a strong eval workflow | OSS · SaaS · self-host | Workspace roles | OSS + Cloud tiers | Visit |
Helicone | Drop-in LLM observability with clean analytics | SaaS · self-host | Workspace roles | Usage tiers | Visit |
Arize AI | ML + LLM observability with rich drift analytics | SaaS · self-host | Workspace RBAC | Enterprise | Visit |
Datadog LLM Observability | Datadog shops consolidating AI signals with APM | SaaS | Datadog RBAC · audit | Datadog tiers | Visit |
New Relic AI Monitoring | New Relic estates extending APM to LLMs | SaaS | New Relic RBAC | Usage tiers | Visit |
One paragraph per vendor.
stackcontrolai
End-to-end traces, evals, drift, and SLOs for every model call — joined with the routing, policy, and cost ledger so on-call sees the whole story in one timeline.
- · One trace across modules
- · Replay against any model
- · OTLP and Datadog exports
Langfuse
Open-source observability with traces, prompts, datasets, and evals. A strong default when self-hosting matters.
- · OSS and self-hostable
- · Good eval and dataset story
- · Clean UX
- · Observability-only
- · No router or policy plane
Helicone
Lightweight LLM observability with traces, prompts, and cost analytics. Popular as a first observability step.
- · Easy integration
- · Good cost analytics
- · Self-hostable OSS
- · Observability-only
- · Lighter on enterprise governance
Arize AI
Mature ML observability platform extended to LLMs (Phoenix). Strong for teams that already own classical ML monitoring.
- · Deep drift analytics
- · ML + LLM in one place
- · OSS Phoenix option
- · Heavier to operate
- · Less focused on routing/policy
Datadog LLM Observability
LLM-aware tracing and monitoring inside Datadog. The pragmatic choice when Datadog is already your APM.
- · Native Datadog UX
- · Joins with infra/APM data
- · Mature alerting
- · LLM-specific features still catching up
- · Eval workflow is thinner
New Relic AI Monitoring
AI-aware monitoring inside New Relic, designed to sit next to existing APM and logs. Natural fit if New Relic is the system of record.
- · Single APM pane
- · Familiar alerting
- · Quick to enable
- · LLM-native depth varies
- · Routing and policy live elsewhere
What's different about AI observability?expand
AI observability extends APM with AI-specific signals: prompt and version provenance, tool calls, eval scores, drift on inputs/outputs/embeddings, and one-click replay. Latency and error rate alone don't catch hallucinations, regressions, or silent quality drops.
Do we need a dedicated tool, or can our APM do it?expand
Datadog and New Relic are adding LLM features and are practical if your APM is non-negotiable. Dedicated tools (Langfuse, Helicone, Arize) lead on eval workflow and prompt management. A control plane like stackcontrolai unifies observability with routing and policy on one trace.
How do you actually score quality on free-form outputs?expand
Configurable evals: rule-based, classifier, model-graded, and human-graded. Scores attach to traces, drive SLOs, and surface drift before users do.
Can we export traces to our existing backend?expand
Yes. stackcontrolai exports OTLP to Datadog, Honeycomb, Grafana Tempo, and any OTel-compatible backend, plus logs to your SIEM and metrics to Prometheus.
Skip the demo loop. Run it on your stack.
The live console mirrors what stackcontrolai does in production — governance, routing, traces, and cost on one plane.