Agentic Index
AgentOps vs Arize AI (2026)
Both vendors last verified
AgentOps is the agent debugger and Arize is the observability platform, which sounds like a distinction without a difference until you look at what each does. That verdict is the Agentic Index coverage score, graded from each vendor's own published materials.
AgentOps focuses on production agent debugging across many frameworks, with an open source SDK and the first 5,000 events free. Arize traces, evaluates and monitors with Phoenix as an open source core, from $50 a month on AX Pro, adding online and offline evals, ISO 27001 and SOC 2 Type II certification, and a choice of provider behind its own agents. Reach for AgentOps when an agent is misbehaving today. Reach for Arize when you need to know it is behaving every day.
On the Agentic Index agent infrastructure ranking, Arize AI clears the bar and AgentOps does not. Arize AI documents all five production contract capabilities in full; AgentOps documents two of the five in full. 33 of the 186 vendors in the lane clear it. See the agent infrastructure ranking
This comparison is published by Agentic Index, an independent agentic AI vendor research platform. AgentOps and Arize AI are each graded against the same 14 capability Agentic Index taxonomy, from the vendor's own public materials under the Agentic Index verification standard, alongside 1,084 researched vendors. No vendor pays for placement and no vendor has reviewed this page. How this evidence is graded
Choose AgentOps if
- Debugging is the immediate job and you want devtools built for agent traces specifically.
- Framework breadth matters because your agents are not all built the same way.
- A free tier that is enough for a small team decides it.
Choose Arize AI if
- Continuous evaluation and monitoring is the requirement, not incident time debugging.
- Evals should run online on traces and offline on datasets, under SOC 2 Type II.
- Phoenix gives you an open source path with a commercial platform above it when you need one.
| Feature | A AgentOps |
A Arize AI |
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| Action & orchestration | ||
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Integrations & Tool Calling Ability to connect agents to real systems through native integrations, OAuth-authenticated actions, custom tools, APIs, webhooks, or MCP-compatible tools. |
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AgentOpsIntegrations & Tool Calling The major agent frameworks and providers are instrumented natively, including AG2, Agno, CrewAI, the OpenAI Agents SDK, LangChain and LlamaIndex, and AgentOps traces the tool calls those agents make. These integrations bring trace data in, and none connects an agent to a real system to take actions. SourceAgentOps, docs.agentops.ai/llms.txt (integrations) and agentops.ai homepageread 2026-09-21 |
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Arize AIIntegrations & Tool Calling More than 40 integrations with models, frameworks and AI tools instrument the customer's application to send traces into Arize, code evaluators run in E2B, Daytona, Vercel or Modal sandboxes, and Data Fabric syncs trace data to warehouses. These move telemetry in and data out, and no connector lets an agent take authenticated actions in outside systems. SourceArize, arize.com homepage and pricing and the Phoenix README on github.com/Arize-airead 2026-09-21 |
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Workflow Orchestration Ability to sequence, branch, retry, route, and combine deterministic workflow nodes with autonomous agent steps. |
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AgentOpsWorkflow Orchestration Multi agent workflows the customer runs are visualized from their traces, with operations tied to named agents. AgentOps does not run workflows of its own, and nothing in it sequences, branches or retries steps or combines deterministic nodes with agent steps. SourceAgentOps, docs.agentops.ai/llms.txt (tracking agents, recording operations) and agentops.ai homepageread 2026-09-21 |
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Arize AIWorkflow Orchestration Agents that Arize observes, evaluates and improves run elsewhere, and Arize has no workflows that sequence, branch or retry an agent's steps, or mix deterministic nodes with agent steps. SourceArize, arize.com pricing and homepageread 2026-09-21 |
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Triggers & Channel Coverage How agents wake up and where they work: schedules, webhooks, message events, CRM events, inbox events, chat, email, voice, and collaboration tools. |
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AgentOpsTriggers & Channel Coverage When the customer's instrumented code runs, traces arrive and appear in the dashboard. AgentOps names no schedule, event trigger, alert or automation that starts work on its own. SourceAgentOps, docs.agentops.ai/llms.txt and agentops.ai homepageread 2026-09-21 |
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Arize AITriggers & Channel Coverage Online evals run on traces and spans as they arrive, custom monitors fire on production conditions, and Signal's agents surface failure patterns from production telemetry, trace root causes and generate proposed fixes, so Arize's work starts from incoming telemetry with no person initiating each run. SourceArize, arize.com homepage and pricingread 2026-09-21 |
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| Knowledge & context | ||
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Knowledge Grounding & RAG Ability to ground agent behavior in company data through document ingestion, retrieval, external knowledge APIs, semantic search, or RAG layers. |
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AgentOpsKnowledge Grounding & RAG Calls a customer's agents make are traced, including retrieval steps where the customer instruments them. AgentOps names no document ingestion, index, retrieval layer or knowledge API that grounds an agent in company data. SourceAgentOps, docs.agentops.ai/llms.txtread 2026-09-21 |
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Arize AIKnowledge Grounding & RAG RAG pipelines built elsewhere are traced and evaluated in Arize, but Arize offers no retrieval structure over the customer's own documents that grounds an agent's answers. SourceArize, arize.com homepage and pricingread 2026-09-21 |
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Memory & State Persistence Ability to persist context across a run, conversation, workflow, user, team, or longer-term memory layer. |
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AgentOpsMemory & State Persistence Traces, sessions and completions are stored as records of the customer's agent runs. AgentOps names no session, workflow or long term memory that an agent reads and writes. SourceAgentOps, docs.agentops.ai/llms.txt (traces, core concepts)read 2026-09-21 |
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Arize AIMemory & State Persistence Arize stores agent trajectories and sessions for tracing and evaluation, and there is no session, conversation or long-term memory that an agent reads back as context. SourceArize, arize.com homepage and pricingread 2026-09-21 |
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| Control & trust | ||
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Human Oversight & Guardrails Approval steps, consent checkpoints, escalation rules, structured guardrails, policy constraints, and pause/resume controls. |
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AgentOpsHuman Oversight & Guardrails Logs, errors and prompt injection attacks leave a data trail, and runs can be replayed for review. AgentOps names no approval step, consent checkpoint, runtime guardrail or pause and resume control over an agent's actions. SourceAgentOps, agentops.ai homepage and docs.agentops.ai/llms.txtread 2026-09-21 |
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Arize AIHuman Oversight & Guardrails Signal generates review-ready fixes for a person to review instead of applying them, so changes Arize's agents propose pass through human review. There is no approval step, escalation rule or pause before an agent acts at run time, and no autonomy modes that treat low risk and high risk actions differently. SourceArize, arize.com homepage and pricingread 2026-09-21 |
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Security, Identity & Governance RBAC, SSO, auditability, encryption, least-privilege tool access, compliance posture, and data handling policy. |
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AgentOpsSecurity, Identity & Governance The Pro plan adds role based permissioning, and the Enterprise plan adds custom SSO, a custom data retention policy and on premise or self hosted deployment. Self hosting lets a customer apply its own security policies. The plans mention compliance standards, with no published report or auditor behind them. SourceAgentOps, agentops.ai homepage pricing section and docs.agentops.ai/v2/self-hosting/overviewread 2026-09-21 |
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Arize AISecurity, Identity & Governance Arize reports SOC 2 Type II, ISO 27001, PCI DSS, HIPAA and GDPR certification, with a Trust Center, and its plans include organization- and space-level RBAC, service accounts, Enterprise SSO and audit logs. SourceArize, arize.com homepage and pricingread 2026-09-21 |
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Observability & Auditability Traces, logs, execution histories, metrics, audit events, and debugging detail for production agent behavior. |
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AgentOpsObservability & Auditability Each agent run is recorded as a trace of spans for LLM calls, tool calls, operations and named agents, with the host environment captured automatically. The dashboard replays runs step by step with point in time precision, visualizes multi agent interactions and tracks tokens and cost. The Pro plan adds session and event export and unlimited log retention. A read only public API and an MCP server expose trace and span data. SourceAgentOps, docs.agentops.ai/llms.txt (traces, spans, host environment, public API, MCP server) and agentops.ai homepageread 2026-09-21 |
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Arize AIObservability & Auditability Agents are traced in Arize AX on OpenTelemetry with path and graph visualizations of each agent trajectory, token, latency and cost tracking, session and multi-modal tracing, custom dashboards, metrics, monitors and views on traces, audit logs on Enterprise, retention by plan (15 days Free, 30 days Pro, custom Enterprise), and Data Fabric sync of trace data to BigQuery, Databricks or Snowflake. SourceArize, arize.com pricing and homepageread 2026-09-21 |
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Deployment & Data Residency Deployment modes and options, including SaaS, dedicated cloud, VPC, on-prem, hybrid, local runtime, and self-hosting. |
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AgentOpsDeployment & Data Residency Customers can use the hosted service or self host the whole platform (API server, dashboard, Supabase and ClickHouse data layers and an OpenTelemetry collector) on their own servers or cloud through Docker Compose, Kubernetes or deployment to AWS, GCP and Azure, so data stays on their own infrastructure. The Enterprise plan adds on premise deployment, self hosting on AWS, GCP and Azure, and a custom data retention policy. SourceAgentOps, docs.agentops.ai/v2/self-hosting/overview and agentops.ai homepage; docs.agentops.ai/v2/self-hosting/overview.mdread 2026-09-21 |
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Arize AIDeployment & Data Residency Every Arize AX plan chooses a US, EU or CA data region, Enterprise adds self-hosted deployments, and open-source Phoenix runs locally, in containers with Docker and Helm, or in the customer's cloud. SourceArize, arize.com pricing and the Phoenix README on github.com/Arize-airead 2026-09-21 |
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| Solution readiness | ||
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Prebuilt Agents, Templates & Packs Ready-made workflows, packaged employees, templates, blueprints, industry solutions, and role-specific agents that reduce time-to-value. |
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AgentOpsPrebuilt Agents, Templates & Packs Examples show how to instrument agents built with various frameworks, and AgentOps offers expert help building agents as a service. It names no ready made agents, templates or packaged workflows for customers to adopt. SourceAgentOps, agentops.ai homepage and docs.agentops.ai/llms.txt (examples)read 2026-09-21 |
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Arize AIPrebuilt Agents, Templates & Packs Ready-made agents a customer switches on include Alyx, an AI engineering agent that runs evals, debugs issues and improves the customer's agents, and Signal, whose agents find production issues and propose fixes, each working without the other, alongside managed agents on Enterprise and packaged skills that teach coding agents to trace, evaluate and debug with Phoenix. Those are role-specific agents and ready-made workflows. SourceArize, arize.com homepage and pricing and the Phoenix README on github.com/Arize-airead 2026-09-21 |
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| Platform extensibility | ||
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Model Flexibility & Routing Ability to work across multiple foundation models, route tasks to different models, or let buyers bring their own providers and keys. |
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AgentOpsModel Flexibility & Routing More than 400 LLMs and frameworks that a customer's agents call can be traced and costed, and tracing does not constrain which model the agent uses. Customers do not choose, route or key any model the product itself runs, and AgentOps names no model policy. SourceAgentOps, agentops.ai homepage and docs.agentops.ai/llms.txt (tracking LLM calls)read 2026-09-21 |
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Arize AIModel Flexibility & Routing The AI provider behind Arize's own agents and evaluators can be customized, with the customer's own key alongside Arize-managed models on Enterprise, and the Phoenix playground compares models and replays traced LLM calls against them. Customers control the models Arize's own AI features run on, though bringing their own key requires the Enterprise tier. SourceArize, arize.com pricing and the Phoenix README on github.com/Arize-airead 2026-09-21 |
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APIs, SDKs & MCP Extensibility Composability layer: stable APIs, SDKs, MCP tool consumption/serving, custom tools, and integration into internal systems. |
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AgentOpsAPIs, SDKs & MCP Extensibility AgentOps publishes open source Python and TypeScript SDKs with decorators, context managers and manual trace control, a read only HTTP public API for trace and span data, and an MCP server that exposes trace and span data to MCP clients, alongside an MCP docs server. SourceAgentOps, docs.agentops.ai/llms.txt (SDK reference, TypeScript SDK, public API, MCP server)read 2026-09-21 |
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Arize AIAPIs, SDKs & MCP Extensibility Arize publishes Python and TypeScript client, OpenTelemetry and evals packages, a GraphQL API, a CLI that fetches traces, datasets and experiments, and a remote MCP server at each Phoenix instance's /mcp endpoint that MCP clients use to query traces, datasets and experiments, all on the OpenInference and OpenTelemetry standards it maintains. That is a stable API and SDK for Arize's own platform, with MCP served. SourceArize, the Phoenix README on github.com/Arize-ai and arize.com homepageread 2026-09-21 |
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Testing, Debugging & Optimization Testing, debugging, scoring, retries, fallbacks, quality gates, and optimization loops for improving agent workflows before and after deployment. |
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AgentOpsTesting, Debugging & Optimization Teams debug agents by rewinding and replaying runs with point in time precision, and a data trail of errors and prompt injection attempts is kept. AgentOps also offers fine tuning of specialized models on saved completions as an optimization step after deployment. It names no testing against fixtures or datasets, configurable quality gates or scoring of output quality over time. SourceAgentOps, agentops.ai homepage and docs.agentops.ai/llms.txtread 2026-09-21 |
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Arize AITesting, Debugging & Optimization Testing covers online evals on traces and spans and offline evals on datasets and experiments, with trace evals of agent trajectories, session evals of multi-turn conversations, agent-as-a-judge and custom code evaluators, evaluator alignment, human annotations and labeling queues, agent experimentation in the playground, and Prompt Learning optimization. Phoenix adds open-source evals, datasets and experiments. That tests the customer's agent against datasets before production and scores quality over time. SourceArize, arize.com pricing and the Phoenix README on github.com/Arize-airead 2026-09-21 |
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| Specialist automation | ||
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Browser & Computer Use Browser, desktop, or remote/local computer control for workflows that cannot be handled through stable APIs alone. |
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AgentOpsBrowser & Computer Use The product covers SDK instrumentation, tracing, the dashboard, a public API, an MCP server and self hosting. AgentOps names no browser, desktop or computer control by an agent. SourceAgentOps, docs.agentops.ai/llms.txtread 2026-09-21 |
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Arize AIBrowser & Computer Use Arize observes, evaluates and improves agents, and its agents have no browser, desktop or computer control. SourceArize, arize.com homepageread 2026-09-21 |
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Pricing snapshot
Sourced from the Index pricing dataset · open each vendor's profile for full detail.
| Pricing | A AgentOps |
A Arize AI |
|---|---|---|
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Entry price Lowest public entry point |
Free up to 5,000 events · Pro from $40/mo · Enterprise custom | AX Pro from $50 a month, plus span and ingestion overage. AX Free and Phoenix open source are free, and Enterprise is custom. |
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Pricing confidence How public the numbers are |
Public, exact | Public, exact |
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Billing Primary billing axis |
usage | A flat monthly plan with usage overage on trace spans and on ingestion, billed per GB. |
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Variable cost Workload / overage exposure |
Medium variable cost | Medium variable cost |
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Free tier / trial Try before you buy |
Free tier
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Free tier
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Buying motion Self-serve vs sales call |
Mixed | Mixed |
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Pricing verified When this pricing was last checked |
Product updates
Newest first, each linked to its source.
Arize AI
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· Agent capability
Eval Health is a new managed agent in Arize AX that reviews the evals on a tracing project.
Source -
· Agent capability
Alyx, the agent built into Arize AX, can now create, edit, explain and run Decision Judge evaluators from chat.
Source -
· Observability / auditability
Phoenix v20.18.0 shows evaluation results directly in the trace tree, so eval scores and labels sit beside each step of an agent or LLM run.
Source
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