Quick answer:
Arize is an AI engineering platform for teams shipping agents. It keeps one record of every call from the first development run through production, evaluates behavior at the span, trace, and session level, and closes the loop with Signal and managed agents that can inspect the repo and propose the next change. The data lives on open standards, the platform deploys in your environment when you need it to, and the pricing meters data volume rather than seats or additional evaluations.
Plenty of tools in this category trace LLM calls and run evals. The differences show up once an AI system carries real traffic: whether the record survives from development into production, whether evaluation can score a whole conversation rather than a single span, whether a finding can become a code change without a human copying context into another tool, whether the trace data stays portable, and whether the architecture holds at enterprise volume.
This page walks through the differentiators that decide those questions, and links out to the head-to-head comparisons where each one gets tested against a specific alternative.
One record from development to production
Most agent evaluation tools split the AI lifecycle in half. Offline experiments live in one workflow, production logging lives in another, and the team carries context between them by hand. Arize treats the trace as the system of record for the whole lifecycle instead.
Every call is traced the same way from the first local run through live traffic, so the same data powers experiments before release and monitoring after it:
The payoff is continuity. The context that usually gets rebuilt by hand between a debugging session and a release review stays attached to the record, so a failure a customer reported in week one is still inspectable and still monitored in week twelve, without anyone excavating a Slack thread to reconstruct what happened.
How Arize compares to the alternatives
For a more detailed look at how Arize stacks up against a specific tool, here are the head-to-head comparisons:
- Arize vs. Braintrust: An eval-first playground versus a continuous record from development through production.
- Arize vs. LangSmith: A framework-native loop versus open-standards observability across any stack.
- Arize vs. Langfuse: Self-managed developer visibility versus a managed production operating loop.
- Signal vs. LangSmith Engine vs. Braintrust Topics: Classification or a vendor-defined fix workflow versus a prebuilt investigation on managed agents you can customize.
Open standards with no framework lock-in
Arize instrumentation is OpenTelemetry and OpenInference native, with auto-instrumentation across 30+ frameworks and providers including OpenAI, Anthropic, Bedrock, LangGraph, LangChain, LlamaIndex, CrewAI, the OpenAI Agents SDK, and DSPy. One instrumentation approach covers the whole stack, and adopting a new framework never means adopting a new observability layer.
That neutrality matters more as architectures evolve. Production systems accumulate a second framework here, a direct provider call there, a custom retrieval service, an agent runtime someone adopted for one workflow. Framework-native tools are deepest where their ecosystem is and thinner everywhere else.
Other providers that claim OpenTelemetry support often run an ETL pipeline under the hood on ingest, converting traces into a proprietary format before the data lands in the product. Braintrust accepts OTel as a fallback and converts spans into its own schema. LangSmith and Langfuse can ingest OTel-shaped telemetry, then map those attributes into their internal run models. Ingestion support is not the same as keeping the data model itself on open standards. Because Arize data lives on OpenTelemetry and OpenInference, no vendor format sits between you and your traces, and moving data out does not require an export negotiation.
Evaluation built for the whole agent trajectory
Scoring the final answer misses how agents actually fail. A response can look fine while the session took the wrong path, repeated work, called the wrong tool, or completed the wrong version of the task. Arize evaluates the trajectory:
- Path evals measure whether the agent took the optimal route.
- Convergence evals catch loops and unnecessary backtracking.
- Session evals track coherence and task completion across multi-turn interactions. This is a first-class agent evaluation target in Arize, not a wrap around span scores: the unit of analysis matches how users experience an agent.
- Graph views show how multi-agent systems hand off, loop, and backtrack, and they work across frameworks.
That session-level view is a real differentiator. Span scores can pass while the conversation drifts, repeats itself, or finishes the wrong job. Because sessions are first-class, scoring a whole conversation never requires restructuring the data first. The same evaluator that scores a session in an offline experiment can run unchanged against live traffic.
The Evaluator Hub adds version history with commit messages over evaluators, so the same judge runs identically in offline experiments, CI, and production monitoring. Arize also supports voice agents natively, inspecting audio sessions alongside transcripts, analyzing interruptions and time-to-first-audio, and running evals directly against audio interactions.
Signal and managed agents turn findings into fixes
Most platforms measure the failures a team already defined, then stop at a dashboard. Production keeps creating failures nobody wrote a check for, and a human still has to copy the trace into a separate coding session to do anything about it. Arize is built to close that loop.
Managed agents run in an Arize-managed Claude Code sandbox. Connect a GitHub skill and they get the repo context they need to inspect the code, propose a patch, and open a pull request—without forcing someone to paste traces into a separate Claude Code window. They can also read project traces and, with skills, pull in operational context such as Datadog. Code changes arrive as a PR for review; a managed agent does not deploy to production. Custom managed agents and repo access are available on Enterprise.
Signal is the prebuilt investigation on top of that system. It reviews production traces on a recurring schedule, groups related failures into prioritized issues, and writes each one up with evidence, a likely cause, and a proposed change—prompt, code, config, or eval—to test whether it improves the agent. Signal is available on every AX plan (10 issues per month on Free, 25 on Pro, unlimited on Enterprise). On Enterprise, attaching a repo lets Signal carry that investigation into the codebase and open a pull request. Free and Pro get issue detection and the written investigation, including the proposed change; they do not attach a repo or open fix PRs.
The rest of the discovery stack stays in the same loop:
Deployment on your terms
Arize AX deploys to one Kubernetes cluster in your VPC or fully on-prem, control plane included, without outbound calls to third-party servers or a forced update cadence. SOC 2 and GDPR support come with the platform, and Enterprise adds HIPAA, RBAC, and audit controls. That same self-contained model runs in regulated environments, including U.S. Navy / DIU MLOps programs where Arize contributed model-monitoring capabilities.
Phoenix, the open source project, runs entirely in your environment as well, whether locally, in Docker, or in your cloud, with no SaaS control plane in the loop and the full feature set included. Teams start with Phoenix to prove out the workflow and move to Arize AX when they want managed infrastructure, governance, and production scale.
The contrast across the category is sharp here, because some competitors gate self-hosting behind Enterprise contracts entirely while others offer hybrid models where your VPC holds the data while their cloud keeps the control plane and auth. A deployment with a third party in the auth path carries outbound dependencies and version-skew risk that a self-contained cluster doesn’t.
Pricing that doesn’t tax the work
Arize AX prices on span and data volume, with unlimited users and unlimited evaluations on every hosted tier.
There are no per-seat charges, and running additional evaluations does not add a line item. Here’s how the tiers break down (learn more on the pricing page):
- Phoenix: free and self-hosted with no feature gates or usage caps, covering tracing, evals, datasets, experiments, and prompt management.
- AX Free: hosted tier that includes Alyx and the evaluation suite at $0, with 25k spans/month, 1 GB ingestion, 15-day retention, Signal limited to 10 issues/month, and a choice of data region.
- AX Pro: self-serve tier from $50/month with 50k spans/month, 10 GB ingestion, 30-day retention, Signal limited to 25 issues/month, and overage pricing so data keeps flowing past the cap.
- AX Enterprise: custom pricing for production scale, governance, ADB Data Fabric, VPC / on-prem deployment, HIPAA, unlimited Signal, custom managed agents, repo access, and support SLAs.
What matters most on the bill:
- The cost tracks how much the system runs, not how many people look at it. Reviewers and cross-functional stakeholders join without changing the price, which matters because production AI quality work spreads across support, product, operations, and finance rather than staying inside engineering.
- Running additional evaluations does not multiply the bill. On Braintrust, teams pay more when they run more evals—even additional span-level scores add up. Arize meters span and data volume, so adding session-level or trajectory evals, or simply scoring more spans, does not change the cost model.
Scale that’s already proven
Arize runs evaluation and observability at volumes most platforms never see. Online evaluators score live traffic continuously across customers like Uber and Booking.com, and adb, the purpose-built AI datastore, handles trillion-span monthly volumes on open formats with Iceberg and Parquet at the foundation.
That capacity has deep roots, since Arize built production ML monitoring, including drift and embedding analysis, before LLMs existed, and shipped Phoenix as an early open source library for LLM evaluation. The platform grew into agents from a production-infrastructure base rather than growing into production from a dev-tool base, and that origin shows in how the architecture behaves under load.
Who Arize fits best
Arize is the strongest fit for teams shipping agents toward or into production, where the operating questions have real stakes: which sessions failed, whether the pattern is recurring, who owns the investigation, and whether the fix held after release. It fits multi-framework stacks and regulated environments that need deployment control. It also fits teams that want a finding to become a reviewed pull request, not a ticket that waits for someone to re-explain the failure to a coding tool.
Narrower tools can be enough for narrower jobs. A team living entirely inside prompt iteration may be happy in an eval-first playground, and a team building end to end on one framework gets real convenience from that framework’s native tooling. The comparison pages above lay out those cases honestly. The pattern across all of them is the same: the alternatives are strongest before an AI system meets real usage, and Arize is built for everything that happens after, while still covering everything that happens before.
