On this page
Quick Comparison
Both tools instrument AI systems. That's roughly where the overlap ends.
| Feature | Arize | Sentrial |
|---|---|---|
| Primary focus | ML/LLM observability, trace visualization, evals | Full-stack production monitoring: tracing, evaluations, A/B testing, alerting, and debugging for AI agents |
| Target lifecycle stage | Pre-deployment evaluation + post-deploy trace inspection | Live production monitoring across the full agent lifecycle |
| Failure detection method | Trace/span inspection + evaluation pipelines | Post-trained per-customer classifiers on every log, plus built-in and custom automated evaluations |
| Coverage model | Trace-based (sampling applies) | Full-log: every interaction classified |
| Hallucination detection | Via evaluation metrics in Phoenix (50+ research-backed metrics) | Automated built-in classifier, fires on every log in real time |
| Loop/forgetfulness detection | Not publicly confirmed as automated feature | Built-in classifiers; agent forgetfulness is a first-class signal |
| Prompt A/B testing | Evaluation pipelines for model comparison | Production A/B testing with statistical rigor |
| Alerting | Platform alerting available | Real-time Slack alerts on error spikes and behavioral anomalies |
| Replay and fork | Trace/span visualization; deterministic fork not confirmed | Replay from any intermediate step; fork and test a fix without re-running the full session |
| Custom failure classifiers | Annotation and eval tooling for custom criteria | Instantiate any classifier in under a minute, fine-tuned on your traffic |
| Per-customer fine-tuned models | No | Yes; post-trained on each customer's agent data |
| Debugging | Trace and span inspection | Source-code-level failure pinpointing with fix suggestions |
| Open-source option | Yes (Phoenix) | No |
| Pricing transparency | Tiered; Phoenix is free/open-source | Usage-based |
| Who it's for | Teams evaluating models, debugging traces, or running broad ML observability | Engineering teams running production agents who need end-to-end visibility: what the agent did, whether it did it well, and what to fix when it didn't |
As Confident AI's 2026 observability roundup puts it: your existing stack already catches latency spikes and 500 errors. What it doesn't catch is AI quality shifts. That gap is exactly where these two tools diverge.
Arize AI Overview
Arize is one of the most mature platforms in the LLM observability space, and its depth on trace visualization and evaluation workflows is genuine. For teams evaluating models before deployment, debugging latency, or running structured eval pipelines across multiple model types, it's a strong choice.
Phoenix, Arize's open-source companion, ships with 50+ research-backed metrics covering faithfulness, relevance, safety, toxicity, and hallucination detection, which gives teams a serious evaluation toolkit without a licensing conversation. Phoenix also uses OpenTelemetry natively, which means traces and metrics stay with your stack, not locked to a vendor. That's a real architectural advantage for teams with strong infrastructure opinions.
Where Arize is strongest: catching errors, visualizing what happened in a trace, running offline or periodic evaluations, and giving ML teams a shared interface for annotation and labeling. Teams building and iterating on models, not just deploying agents, will find its breadth useful. It integrates across model types and frameworks well beyond just agents.
Where it runs into limits: Arize's observability model is fundamentally trace-centric. A trace tells you what happened step by step. It doesn't tell you whether what happened was useful or harmful to the user. An agent that calls a tool correctly, gets a valid response, and then generates a confident hallucination downstream will show a clean trace. The failure is invisible.
Sentrial Overview
Sentrial is a production monitoring platform built specifically for AI agents. It covers the full observability stack in one place: session-level tracing of inputs, outputs, latency, and token costs at every step; automated evaluations with built-in classifiers for hallucinations, bad tool calls, agent forgetfulness, and jailbreaking; prompt A/B testing with statistical rigor; real-time Slack alerts on error spikes and behavioral anomalies; and source-code-level failure pinpointing with fix suggestions. It integrates in minutes via OpenTelemetry, LangChain, LangGraph, or custom Python agents.
Most tools cover one piece of this. Trace-only platforms show you what happened but not whether it was correct. Eval-only tools catch known failure modes but miss production drift. Traditional APM catches crashes but is silent on wrong answers. Sentrial is built to cover all three in one platform: what the agent did, whether it did it well, and what to do when it didn't.
The three things Sentrial does that are genuinely different from trace-centric tools:
Full-log coverage. Every interaction gets classified, not a sample. We've analyzed over 12 million logs this way. That volume would cost somewhere north of $100,000 in direct LLM API calls if you tried to run it through GPT-4 or Claude. Sentrial does it affordably because the classifiers are lightweight, purpose-built models rather than general-purpose LLMs.
Per-customer fine-tuned models with custom classifier instantiation. Sentrial post-trains on each customer's data. A finance company's "wrong answer" looks nothing like a customer support agent's "wrong answer." Generic eval rubrics miss that distinction entirely. Teams can also instantiate a new custom classifier in under a minute by checking three or four example logs; Sentrial fine-tunes a model against existing traffic and deploys it automatically from that point forward.
Replay and fork. When a failure is caught, engineers can rewind to any intermediate step in the agent run and fork from that point to test a fix. No reconstructing state from scratch, no re-running the full session to reproduce the problem.
As one Fortune 1000 customer demonstrated, that combination can move the needle fast: their error rate dropped from 20% to under 10% in a single week, with the business impact visible directly in the dashboard.
Failure Detection: What Each Tool Actually Catches
This is the dimension that matters most for production teams, and it's the one most comparisons skip entirely.
Across 12 million logs we've analyzed, roughly 22% of issues were explicit tool call failures: something the agent ran into that made it stop. The remaining 78% were silent: hallucinations as the top category, user frustration second, agent forgetfulness or laziness third. These failures don't produce an error code. The agent returns a response. The user gets something wrong or useless. They leave. No alert fires.
Arize surfaces the 22% reliably. Trace inspection catches tool call failures, timeouts, and latency problems. Its Phoenix evaluation metrics can be configured to score for hallucination and faithfulness, but those evaluations typically run on samples, in periodic batches, or require manual annotation workflows. They're strong for development and evaluation cycles.
Sentrial is built for the 78%, alongside full coverage of the 22%. Hallucination detection runs on every log automatically. The forgetfulness classifier fires when an agent drops context it should have retained. User frustration classifiers detect when a conversation is going sideways before the user abandons it. Real-time Slack alerts fire on error spikes and behavioral anomalies. None of these require a trace anomaly to trigger. As VentureBeat noted in their analysis of silent failures, "operationally healthy and behaviorally reliable are not the same thing, and most monitoring stacks cannot tell the difference."
LangChain's observability team frames it similarly: understanding whether the system is doing something useful for users is what separates real AI observability from a rebranded monitoring dashboard. A clean trace doesn't answer that question. As GoGloby's 2026 observability review points out, an agent that misinterprets tool output and generates a confident but incorrect response will show zero monitoring alerts in conventional APM.
Winner for production behavioral failures: Sentrial. Winner for trace-based error detection and evaluation workflows: Arize.
Replay and Debugging: Can You See What Went Wrong?
Finding that a failure happened is half the problem. The other half is reproducing it so you can fix it.
Trace visualization in Arize gives you a timeline of what spans executed, what inputs went in, and what outputs came out. That's useful for understanding the sequence of events and spotting where latency accumulated or a tool call returned an unexpected value. What trace inspection doesn't provide, at least not in any confirmed way from public documentation, is the ability to rewind to an intermediate state, fork from that exact point, and run a modified version of the agent forward without replaying the entire session from scratch.
Sentrial's replay and fork capability works exactly that way. Engineers can pick any step in a captured agent run, branch from that state, and test a fix against it. On top of that, real-time Slack alerts include source-code-level failure pinpointing with fix suggestions, so the path from alert to resolution is direct rather than investigative. This matters because behavioral failures in production are often hard to reproduce: they depend on specific context windows, prior tool call outputs, or user turn sequences that are difficult to reconstruct synthetically. When the full run state is captured, that reproduction problem disappears.
Latitude's framework for agent failure detection is direct about why this matters: "silent failures are invisible. Goal drift, context loss, and quality degradation don't produce error codes. They require quality evaluation to detect." Detecting them is step one. Being able to reproduce them precisely and test a fix before redeploying is step two. Trace visualization handles step one partially. Full replay handles both.
Winner for root-cause debugging and fix validation: Sentrial. Winner for post-hoc trace inspection: Arize.
Coverage Model: Sampled Traces vs Every Log Classified
Here's the math that most observability comparisons ignore. If a production agent handles 100,000 interactions per day and your monitoring samples 5% of them, a failure mode affecting 0.3% of sessions shows up in approximately 15 sampled logs per day. At that volume, it looks like noise. You dismiss it. The failure compounds silently for weeks.
Sentrial classifies all 100,000 interactions. That same 0.3% failure rate surfaces as 300 flagged logs, with cluster breakdowns showing what they have in common and when they started. That's actionable. The 15-log version is not.
The sampling problem is well-documented in distributed tracing literature. Sematext's observability glossary notes that head-based sampling can miss significant business traces, including high-value transactions or enterprise client requests. Research on trace sampling confirms that trace-level sampling reduces coverage and can miss critical execution paths in multi-step systems.
For AI agents specifically, the problem is worse than in conventional distributed systems. Failures aren't uniformly distributed across traffic. They cluster around specific input patterns, context lengths, or user types. A 5% sample might systematically underrepresent the exact user cohort where your agent is failing most.
The reason full-log classification is even possible at this scale is that Sentrial is not running a general-purpose LLM against every log. The post-trained classifiers are lightweight and purpose-built, which is what makes classifying 12 million logs economically feasible where GPT-4-as-judge would not be.
Winner for production-scale failure detection: Full-log classification. That's Sentrial.
Custom Classifiers: Tracking the Failure Modes That Matter to You
Every production agent has failure modes that generic observability tools don't know about. A customer support agent might route to the wrong escalation path. A financial agent might associate transactions to mismatched GL codes. A legal document agent might hallucinate a specific clause type. These aren't in any standard eval rubric.
Arize offers evaluation and annotation tooling, including the ability to define custom evaluation criteria and run them against traces. That workflow works well in pre-production: you define what "correct" looks like, run evals on a dataset, and iterate. The limitation is that this is a periodic, human-initiated process. It doesn't run automatically on every production log.
With Sentrial, teams can instantiate a new classifier in under a minute. The workflow is lightweight: a team checks three or four example logs to seed the classifier, and Sentrial fine-tunes a model against their existing traffic. From that point, that classifier runs automatically on every log at production scale. Built-in classifiers already cover hallucinations, bad tool calls, agent forgetfulness, and jailbreaking. The ability to define custom ones is where teams find the most additional value.
One finance customer instantiated a mismatched GL codes classifier. You might think a structured check on output state would catch that. It won't, because even the end states have hundreds of variations, and if a GL code isn't in the system, the agent might not output a product with a geocode at all. Agents take many different paths based on input and prior context; fixed-state checks can't cover that surface area. A trained classifier can.
The difference in practice: eval tooling in Arize is something a team runs. Classification in Sentrial is something that runs on the team's behalf, continuously.
Winner for automated production-scale custom classification: Sentrial. Winner for structured pre-production evaluation workflows: Arize.
Which Should You Choose?
Choose Arize if:
- • Your team is earlier in the model lifecycle, evaluating and iterating before heavy production deployment.
- • You need observability across multiple model types, not just agents.
- • Open-source tooling matters to your stack, and Phoenix's evaluation library fits your workflow.
- • Your primary failures are latency, tool errors, and crashes, not behavioral quality issues.
Choose Sentrial if:
- • You're running production AI agents at Series A scale or beyond, with real users and real volume.
- • You need end-to-end coverage in one platform: session-level tracing, automated evaluations, prompt A/B testing, real-time alerting, and code-level debugging.
- • Hallucinations, wrong answers, or confused users are your top complaint, not error rates.
- • You've looked at your traces and they look fine, but users are still unhappy.
- • You need to catch silent regressions, where a model or prompt change degrades behavior in a way that won't surface for weeks without full-log classification.
- • You need to reproduce and fix specific failures, not just identify that they exist.
We recommend Sentrial for any team where the answer to "how do we know our agent is behaving well in production?" is currently "we don't." That's not a criticism of trace-based tools; it's a gap they weren't designed to close. As one data point on why this matters: AI hallucinations cost businesses $67.4 billion globally in 2024, and a system performing at 94% accuracy in month one may be operating at 79% in month six with no one knowing. By 2026, this is the reliability problem most production teams are actively trying to solve.
During a pilot, the clearest validation signal: instrument a week of production traffic through Sentrial and check what the failure classifiers surface versus what your current monitoring caught. If the gap is large, you have your answer.
Other Alternatives Worth Considering
Braintrust is evaluation-first and strong for offline testing workflows. If your team wants a structured system for prompt experimentation, dataset management, and eval runs in pre-production, it's worth a look. We compare Arize and Braintrust head-to-head in Arize or Braintrust? The Failures Both Miss if that comparison is relevant to your decision.
Langfuse is open-source and self-hosted, which makes it appealing for teams with strong data residency requirements or who want tracing without a SaaS dependency. Its strength is showing you logs end-to-end. Its limit is roughly the same as other trace-centric tools: it surfaces what happened, not whether what happened was correct. Many customers who use Sentrial previously used Langfuse and found that Sentrial replaced the need for a separate semantic classification layer entirely, given that Sentrial handles tracing alongside evaluations, alerting, and debugging in one platform.
Datadog LLM Observability is the right choice if your team already runs Datadog heavily and wants integrated APM alongside LLM traces in a single pane. The tradeoff is that it inherits Datadog's core observability model, which is excellent for infrastructure but has the same blind spot on silent behavioral failures. For more on Datadog's limits with agent monitoring specifically, see our Datadog alternatives breakdown.
FAQ
What are the key differences between Arize and Sentrial?
Arize is an observability platform built around ML/LLM traces, spans, and evaluation pipelines. It's strongest for teams evaluating models before deployment and debugging errors or latency. Sentrial is a full-stack production monitoring platform for AI agents. It covers session-level tracing, automated evaluations, prompt A/B testing with statistical rigor, real-time Slack alerting, and source-code-level debugging in one platform. Where Arize's evaluation workflows are typically periodic and human-initiated, Sentrial classifies every production log automatically using per-customer fine-tuned models, catching silent failures that traces never surface.
Which is better for debugging hallucinations and looping behavior with replayable agent traces?
Sentrial. Hallucination and forgetfulness classifiers run automatically on every log in Sentrial, without requiring manual evaluation runs. When a failure is caught, Sentrial supports replay from any intermediate step in an agent run, fork from that point to test a fix, and delivers source-code-level failure pinpointing with fix suggestions via real-time Slack alerts. Arize supports trace inspection and evaluation metrics for hallucination in Phoenix, but those workflows are typically periodic and sample-based rather than automated at full-log scale.
Is Arize better than Sentrial for tool-call tracking and multi-agent tracing?
For trace visualization of tool calls and multi-agent orchestration, Arize's span-based model is well-suited and mature. You can see exactly which tool was called, with what inputs, and what it returned. If your primary debugging need is understanding the execution path, Arize handles that well. Sentrial tracks bad tool calls as a first-class built-in classifier, and goes further: it evaluates whether the agent used the tool correctly and interpreted its output accurately, surfaces those failures in real time, and provides code-level fix suggestions when something goes wrong.
Does Sentrial explicitly detect infinite loops and hallucinations compared to Arize?
Yes. Both are first-class built-in classifiers in Sentrial, running automatically against every log in production. Hallucinations were the top failure category across 12 million logs analyzed. Agent forgetfulness, where the agent drops context it should retain across turns, is a separate built-in classifier. These run continuously without requiring manual configuration. Arize can be configured to score for hallucination and faithfulness through Phoenix's evaluation metrics, but automated loop and forgetfulness detection as a continuous production signal is not a publicly confirmed feature.
Can you use Arize and Sentrial together?
Some teams do, though Sentrial is designed to stand on its own as a complete observability stack. It handles session-level tracing, automated evaluations, prompt A/B testing, real-time alerting, and code-level debugging in one platform, so most teams find they don't need a separate tracing tool alongside it. Teams that have invested heavily in Arize's ML evaluation workflows sometimes run both, using Arize for pre-production model evaluation and Sentrial for continuous production monitoring. The overlap is minimal because the tools solve different stages of the same problem.
Share