On this page
Both Arize and Guardy sit in the AI observability category, but they're solving different problems. Arize Phoenix is built for teams that want an open-source, OTel-native trace collection layer they can self-host and plug into an existing ML platform. Guardy is built for engineering teams that need detection, alerting, and code-level debugging in one place rather than three tools stitched together. That difference is invisible until your agent is wrong without crashing, and then it's the only thing that matters.
The Quick Breakdown Before You Go Further
| Dimension | Arize Phoenix (OSS) | Arize AX (Enterprise) | Guardy |
|---|---|---|---|
| Deployment model | Self-hosted, open-source | Managed (contact sales) | Managed, usage-based |
| OTel/OpenInference ecosystem | Native | Native | Supported |
| Multi-agent trace depth | Yes | Yes | Yes |
| Tool-call timeline and per-step cost | Partial | Yes | Yes |
| Full-log coverage (vs sampled) | Sampled by default | Configurable | Full coverage by default |
| Built-in failure classifiers | No | Online evals (configured) | Yes (hallucinations, bad tool calls, forgetfulness, jailbreaking) |
| Custom classifier deployment | No | Manual eval setup | Yes, under 1 minute |
| Replay and fork from intermediate step | No | No | Yes |
| Real-time Slack alerting | No (requires setup) | Partial | Yes, built-in |
| Source-code-level fix suggestions | No | No | Yes |
| Integration setup time | Minutes to hours | Hours | ~5 lines of code |
| Best for | OTel infrastructure, ML platform teams | Structured eval pipelines | Production agent reliability |
Two things worth flagging before going further. First, "Arize" is actually two different products: Phoenix, which is open-source and self-hostable, and AX, their enterprise platform with guardrails and online evals. If you're evaluating Arize, you need to know which one you're actually comparing, because the gap between them is significant. Second, we classify every production interaction by default rather than sampling. That single architectural choice drives most of the practical differences in this comparison.
What Arize Actually Is (and Isn't)
Arize runs as a two-product family. Phoenix is open-source, self-hostable, and built natively on OpenTelemetry and OpenInference. AX is the enterprise layer on top, adding managed infrastructure, guardrails, and an online eval framework.
Phoenix's real strengths are worth taking seriously. The OTel and OpenInference ecosystem compatibility is among the broadest in the market. Framework integrations cover LangChain, LlamaIndex, OpenAI, and others. The open-source community is active, the documentation is thorough, and the eval workflow connecting traces to both offline and online evaluations is well-structured. If your team is already invested in OTel infrastructure and wants a self-hosted observability backbone, Phoenix is a serious option.
AX extends this with managed infrastructure, online evaluations, and guardrails. It's priced accordingly and requires a sales conversation. A pattern we keep seeing: teams start with Phoenix's documentation and end up needing AX's feature set. Getting clear on that distinction before you start any evaluation saves a lot of time.
Where Arize asks more from your team: evaluation and alerting are separate workflow steps rather than a closed loop. Trace collection defaults to sampling rather than full-log coverage. And the debugging workflow stops at trace inspection. There's no step-level fork-and-replay, and no direct path from an alert to a specific code location with a suggested fix. These gaps don't matter for every team, but for engineering teams running production agents at scale, they add up fast.
What We Built Guardy to Do
Guardy is a production monitoring platform for AI agents, covering the full observability stack in one place: session-level tracing, automated evaluations, real-time alerting, and source-code-level debugging. The problem we're solving is straightforward. Engineering teams need to know what their agent did (tracing), whether it did it well (evaluations), and exactly what to change when it didn't (alerts plus code-level debugging). Most tools cover one of these layers well. We cover all three.
Our architecture treats sessions as the canonical unit. Every user interaction becomes a structured execution graph rather than a stream of disconnected logs. Sessions contain traces, which contain spans modeling individual operations: LLM calls, tool invocations, retrieval steps, memory accesses, retries, and workflow transitions. This hierarchical model lets engineers reconstruct complex multi-step agent behavior and replay the exact execution ordering across prompts, reasoning loops, tool selections, API responses, and final outputs.
The differentiators that matter most in this comparison: we classify every production interaction by default, not a sample. We ship built-in classifiers for hallucinations, bad tool calls, agent forgetfulness, and jailbreaking. Teams can deploy a custom classifier for any failure mode in under a minute, describe the failure type, review three or four example logs, and we fine-tune and deploy a lightweight model against your data. We send real-time Slack alerts when error rates spike or behavioral anomalies appear. And we pinpoint failures at the source-code level with fix suggestions, rather than leaving engineers to trace the path from an alert to a code change manually.
Where Arize has a genuine edge: Phoenix's open-source model and self-hosting option are real advantages for teams that need infrastructure control or have compliance requirements that rule out managed platforms. Arize's OTel ecosystem maturity is broader. For teams who want an observability layer that stays cleanly separate from their eval and CI tooling, Phoenix is a well-designed choice. If pre-deployment evaluation quality gates are your primary concern, we'd also point you toward Braintrust vs Guardy: Pre-Release Evals vs Production Silent-Failure Detection.
Both Tools Trace. Here's Where They Diverge.
Both platforms cover the table-stakes tracing requirements: tool-call timelines, nested agent spans, parent-child trace relationships, and per-step latency. The meaningful differences are in what happens after a trace is collected and how granular the agent-specific instrumentation gets.
Arize Phoenix collects traces via OpenTelemetry and OpenInference natively. The framework compatibility is broad, and the trace visualization is well-designed for LLM span exploration. AX extends this with online evaluation hooks. What Phoenix does especially well here is flexibility: because it sits at the OTel layer, teams can wire it to their own eval runners, dashboards, and alerting systems.
We automatically instrument popular frameworks including LangChain, CrewAI, AutoGen, Claude Code, Vercel AI SDK, and Mastra, while also exposing low-level APIs for custom spans, metadata, events, and state transitions directly from application code. Our trace replay gives engineers a visual execution graph showing exact state transitions and causal dependencies across every step, replacing traditional log streams with structurally connected execution trees that expose reasoning pathways and failure propagation patterns.
Arize wins on OTel ecosystem depth and self-hosting flexibility. We win on the trace-to-eval-to-alert pipeline being a single surface rather than requiring separate tools. For a deeper look at what agent tracing actually requires at the step level, our AI Agent Tracing Explained covers the specifics.
Raw trace collection is table stakes though. The more important question is what happens after the trace is collected, which is where these two platforms diverge most sharply.
Sampled Monitoring Misses the Failures That Actually Hurt You
Across 12 million logs we've processed, roughly 22% of issues were explicit tool call failures where an agent stopped mid-run. The remaining 78% were silent failures: hallucinations at the top, followed by user frustration, agent forgetfulness, and similar behavioral problems that don't produce an error code and don't trigger traditional APM alerts. Better models actually make this harder to catch, not easier. Agents now get 90% of the way through a multi-step workflow, sound confident the entire time, and still take the wrong action at the end. That's the failure mode that sampled monitoring is structurally bad at catching.
The math is simple. A 10% sample on 10,000 daily agent runs means you're statistically near-certain to miss failure classes that appear in fewer than 1 in 50 interactions. For rare but high-severity failures like goal abandonment, reasoning loops, or subtle hallucinations in a specific tool-use context, sampling means guaranteed blind spots.
We classify every interaction by default, using post-trained models fine-tuned on company data. This produces meaningfully higher accuracy than LLM-as-judge approaches and costs less at scale. We've concluded that using LLMs as a judge system doesn't scale when agents are running hundreds of tool calls over sessions lasting hours. Classification accuracy degrades exactly where the failure modes are most complex.
Our built-in classifiers cover hallucinations, bad tool calls, agent forgetfulness, and jailbreaking. Beyond that, teams can deploy a custom classifier for any failure mode in under a minute. We haven't seen this capability matched elsewhere, because most observability tools take one of two approaches: provide logs without classification, or ship a fixed set of classifiers without letting teams define new ones.
Arize's online eval framework in AX is mature and well-documented. Arize requires teams to configure eval coverage explicitly and run evaluations against sampled traces. The coverage you get is a function of what you sample and which evals you configure. For teams with well-defined, stable failure modes and a dedicated ML platform team to manage the pipeline, that's workable. For teams where new failure modes emerge as the agent evolves in production, it becomes a reactive rather than proactive stance. For more on why evals alone don't cover production drift, see Evals Explained: Offline, Online, and What Both Miss.
The Debugging Loop Is Where Most Tools Fall Apart
Most comparisons mention replay as a feature and stop there. The operationally relevant question is what the debugging workflow actually looks like end-to-end.
Here's the concrete loop with Guardy. A real-time Slack alert fires when an error rate spikes or a behavioral anomaly is detected. The engineer opens the alert, which links directly to the session trace and the classified failure. We pinpoint which prompt, tool configuration, or agent logic step the failure traces to in the source code, and surface a suggested change. The engineer forks from the specific intermediate step where the failure originated, tests the change without rerunning the full session, and validates with eval assertions before shipping. We also integrate this into the remediation loop through GitHub-aware workflows where production failures, traces, execution context, and diagnostic metadata become inputs for diff generation, patch suggestions, and pull request creation.
That last point is worth being specific about, because "code-level fix suggestions" can sound like marketing. What we mean: we identify whether the failure traces to a prompt template, a tool configuration, a memory access pattern, or specific agent logic, and we surface a suggested change at that location. That's different from showing you the failing trace and leaving root cause as an exercise for the reader.
One enterprise infrastructure company we work with saw their agent error rate drop from 20% to under 10% in a single week using this workflow. Before Guardy, their team spent months manually reviewing sessions, reproducing failures, and attempting to stitch together fragmented telemetry from multiple systems. Their agents were handling high-stakes workflows including vendor evaluation, RFQ generation, and autonomous purchasing decisions, where silent failures compound with real business consequences.
Arize's debugging workflow is strong at the trace inspection level. Engineers can explore spans, filter by failure type, and run evals against flagged traces in AX. Where it requires more from your team is step-level fork-and-replay: Arize doesn't offer the ability to fork execution from an intermediate step and test a change in isolation. The connection from alert to specific code location also requires more manual navigation. For teams with a dedicated ML engineering function who are comfortable building that loop themselves, Arize's components are capable. For teams who need the loop to work out of the box, we'd point you toward Guardy.
For context on how traditional monitoring compares to agent-specific observability, Most AI Agent Failures Never Trigger an Alert. Here's Why. covers the structural gap.
How the Pricing Models Actually Work
Neither we nor Arize publish granular tier breakdowns publicly, so here's the framework that matters rather than made-up numbers.
Arize Phoenix is open-source and self-hostable. No per-seat or per-trace fees for the OSS tier. Your cost is infrastructure. That's a genuine advantage for teams with compliance requirements, existing Kubernetes infrastructure, or a preference for keeping telemetry data inside their own environment. Arize AX is enterprise-priced and requires a sales conversation. Teams evaluating "Arize" need to know upfront which product they're scoping, because the feature and cost gap between Phoenix and AX is significant.
Guardy is usage-based and managed. Pricing is available at tryguardy.com or by contacting our team. The cost driver that matters at scale is full-log classification: we classify every interaction, which means our cost scales with your agent usage volume rather than with a sampling rate. That's a different economic model than tools that charge for sampled traces and run evals on a subset.
Questions worth asking both vendors before committing:
- • Does the plan include full-log classification or only sampled traces? What's the sampling rate at your volume?
- • What's the retention window for trace replay? Can you replay sessions from 30 days ago?
- • Is the eval runner included in the base plan or metered separately?
- • For Arize Phoenix specifically: what's the infrastructure cost estimate at your current agent interaction volume?
- • For Guardy: what's the per-interaction cost at your projected monthly volume?
- • Does the plan include real-time alerting, or is that an add-on?
We've seen enterprises find that the cost of stitching together Phoenix plus a separate eval runner plus alerting infrastructure approaches or exceeds the cost of a managed platform once engineering time is factored in. That's not universal, but it's worth modeling before assuming open-source means lower total cost.
Which One Should You Actually Use
We'd recommend Arize Phoenix if your team is already invested in OTel or OpenInference tooling and wants a self-hosted observability layer you can wire to your own eval pipeline. Phoenix is also the right call for ML platform teams building observability infrastructure for multiple model consumers, teams with compliance requirements that preclude managed platforms, or teams doing structured offline eval pipelines before deployment where pre-production quality gates are the primary concern.
We'd recommend Guardy if you're an engineering team running production AI agents and need detection, alerting, and debugging in one platform without stitching three tools together. Specifically: if you need full-log coverage to catch rare failures that sampled monitoring misses; if you want to go from a Slack alert to a code-level fix without switching tools; if new failure modes emerge in production faster than your team can configure new evals; or if you need custom failure classifiers deployed in under a minute as your agent's behavior evolves.
The honest "it depends": teams doing primarily offline evaluation or pre-deployment quality gates are better served by Arize or Braintrust. Our Arize vs Braintrust: The Silent Failure Gap Both Tools Miss covers that comparison in detail. Teams running live agents in production where silent failures are the primary risk, and where failures have downstream business consequences, are better served by Guardy.
The companies succeeding with production AI agents right now are building replayable execution tracing, regression evaluation pipelines, behavioral signal detection, and operational intelligence around every workflow. The teams that identify why their agents fail before users notice are the ones building durable reliability. Tooling choice is how you get there.
For a broader look at the platform landscape, Best LLM Observability Platforms in 2026 covers more options across the tracing, eval, and alerting dimensions.
FAQ
Is Arize AI free or paid?
Arize has two distinct products. Phoenix is open-source and free to self-host, with infrastructure as your primary cost. Arize AX is their enterprise platform with managed infrastructure, online evals, and guardrails; it's priced on a contact-sales basis. Teams evaluating Arize should clarify upfront which product they're scoping, because the feature set and cost model differ significantly between the two.
Who are the main competitors of Arize AI?
The main competitors depend on what you're using Arize for. For open-source trace collection, Langfuse and Phoenix overlap significantly. For enterprise eval pipelines, Braintrust is a common alternative. For production agent monitoring with built-in failure detection and alerting, Guardy is the most direct comparison. Our Best Arize Alternatives in 2026 covers the major options across these categories.
What is the difference between Langfuse and Arize Phoenix?
Both are open-source LLM observability platforms built on OpenTelemetry. Phoenix has deeper integration with the OpenInference spec and a stronger eval framework natively. Langfuse has a more lightweight setup path and broader self-hosting flexibility. Neither provides full-log classification, built-in behavioral failure detection, or source-code-level debugging out of the box. Our Guardy vs Langfuse covers that comparison in detail if Langfuse is also in your evaluation.
What is the difference between DeepEval and Arize?
DeepEval is primarily an evaluation framework focused on defining and running assertion-based test suites against LLM outputs, typically used in CI pipelines and pre-deployment testing. Arize covers both trace collection and online evals in production. The key difference is that DeepEval excels at structured, pre-defined test coverage before deployment, while Arize's Phoenix layer captures what's happening in production. Neither closes the full loop from production failure detection to code-level debugging the way Guardy does, though they solve different parts of the problem.
When does sampling become a real risk for agent monitoring?
Sampling becomes a reliability risk when your agents run a high volume of daily interactions and your most important failure modes are rare but high-severity. Across the production data we've processed, 78% of agent failures were silent: hallucinations, user frustration, and goal abandonment that don't produce error codes. A 10% sample at meaningful interaction volumes means rare failure classes will consistently evade detection. If your agent is making consequential decisions across multi-step workflows, full-log coverage is worth the cost difference. If you're running low-volume or lower-stakes interactions, sampled monitoring with a well-configured eval pipeline may be sufficient.
Share