# Agenta vs Arize AI Agenta records 45,760 monthly visits with +29.5% month-over-month growth and Freemium pricing. Arize AI records 249,028 monthly visits with +3.2% growth and Paid, Contact for Pricing pricing. | Metric | [Agenta](https://openaitools.app/tool/agenta) | [Arize AI](https://openaitools.app/tool/arize-com) | | :--- | :--- | :--- | | Website URL | https://www.agenta.ai | https://arize.com | | Categories | AI Developer Tools, Prompt Engineering, Large Language Models (LLMs), AI Agent | Large Language Models (LLMs), AI Agent, AI Monitor, AI Developer Tools | | Pricing Model | Freemium | Paid, Contact for Pricing | | Monthly Visits | 45,760 | 249,028 | | MoM Growth Rate | +29.5% | +3.2% | | Primary Market | United States | United States | | Direct Visits | 17,206 | 107,838 | | Review Score | 4.4 | 4.6 | | Saved Count | 0 | 1 | | Supported Platforms | Website | Website | ## Agenta Agenta positions itself as an open-source workspace where teams build AI agents and AI co-workers rather than one-off prompts. You can start from pre-built agent templates, give an agent access to models and tools, and run it from a chat surface, Slack or Telegram so the work happens where your team already communicates. Agents can browse, read and write files, and run scheduled automations that trigger from your connected systems. What sets Agenta apart is its focus on operational control. Agents connect to more than a thousand integrations and MCP servers, secrets live in a vault the agent can use but not read, and permissions and approval steps decide which actions run automatically and which need a human sign-off. Every run is logged with duration, token usage and cost, so you can trace why an agent produced a certain result and iterate on it with feedback. For engineering teams, Agenta ships as MIT-licensed software you can self-host with Docker Compose or Helm, keeping agents and data on your own infrastructure with no vendor lock-in. A managed cloud option is available for teams that prefer not to run the stack themselves, and both share the same workspace concepts of agents, skills, sessions and automations. **Pricing & Plans:** Freemium. The open-source edition is MIT-licensed and free to self-host; cloud access is offered alongside it, with a demo available for larger teams. Exact seat and usage limits are listed on the pricing page. - **Agent and co-worker builder:** Assemble agents from templates or from scratch, give them models, skills and tools, and run them from a chat UI, Slack or Telegram. - **MCP connections and 1,000+ integrations:** Wire agents into Notion, GitHub, HubSpot, Slack, Linear, Drive and more over MCP, and swap tools or skills without rewriting the agent. - **Evaluations and run observability:** Score agent outputs, collect feedback and inspect every run with duration, token usage and cost logging so improvements are measurable. - **Guardrails, approvals and a secret vault:** Decide which actions run unattended and which require approval, while credentials stay in a vault your agent can use but cannot read. - **Self-hosting with MIT license:** Deploy the full workspace on your own infrastructure with Docker Compose or Helm charts and keep agents and data on-premises. **Strengths:** Open-source and MIT-licensed, so it can be self-hosted without vendor lock-in; Connects to over a thousand tools and MCP servers instead of a fixed integration list; Built-in evaluation and full run logging with token and cost tracking; Approval gates and a secret vault keep agents within human control **Limitations:** The workspace concept is broad, so new users need time to map it to their workflow; Self-hosting requires someone comfortable running containers and Helm charts; Some enterprise features are only fully explained after contacting sales [Profile (Markdown)](https://openaitools.app/tool/agenta.md) ## Arize AI Arize is an AI engineering platform built for teams that run generative AI in production. It is split into two products: Arize AX, a managed environment for agent observability, evaluation, and experimentation, and Phoenix, an open-source tracing and evaluation library that teams can self-host when they need full data control. Both rely on OpenInference, the open-source semantic convention for generative AI tracing that the Arize team founded and that is built on OpenTelemetry. The workflow follows three questions. Observe answers what an agent actually did by capturing every span, trace, and session; Evaluate answers whether quality is improving or regressing using span-level, trace-level, and session-level evals; Learn answers whether a proposed change will help by letting teams test prompts and harnesses before rollout. A datastore for GenAI traces (ADB) keeps data in open formats that connect to warehouses such as BigQuery, Databricks, and Snowflake, and an in-app AI engineering agent named Alyx surfaces issues and drafts review-ready fixes. The platform integrates with dozens of models, frameworks, and tools, including OpenAI, Anthropic, Google, Amazon Bedrock, LangChain, LangGraph, LlamaIndex, CrewAI, and DSPy. Arize publishes compliance certifications including SOC 2 Type II, ISO 27001, PCI DSS, HIPAA, and GDPR, and supports cloud, hybrid, and self-hosted deployment. **Pricing & Plans:** Arize uses a paid model with a free entry point: teams can start free or book a demo, while enterprise-scale usage, deployment options, and support are quoted through a contact-sales process. The open-source Phoenix project is free to self-host. - **End-to-end tracing:** Capture spans, traces, and sessions across agents and LLM calls using OpenInference conventions built on OpenTelemetry, so instrumentation stays portable. - **Scalable evaluation framework:** Run evaluations at span, trace, and session level to grade retrieval quality, tool use, and final answers across large production datasets. - **Alyx AI engineering agent:** An in-product agent that inspects traces, diagnoses likely root causes, and proposes fixes that engineers can review before shipping. - **Phoenix open-source toolkit:** Self-host the leading open-source tracing and eval tool when you need to keep trace data inside your own infrastructure. - **Warehouse-native trace storage:** ADB stores traces in open formats that connect natively to BigQuery, Databricks, or Snowflake for joint analytics with business data. **Strengths:** Covers the full loop of observe, evaluate, and improve rather than stopping at dashboards; Backed by OpenInference and OpenTelemetry standards, limiting vendor lock-in on trace formats; Strong enterprise posture with SOC 2, ISO 27001, HIPAA, and GDPR coverage plus self-hosting options; Integrates with a very wide set of models, frameworks, and agent SDKs **Limitations:** Self-serve pricing is not published, so cost requires a sales conversation; The breadth of features can be overwhelming for small teams shipping their first LLM feature; Full value depends on disciplined instrumentation across the whole application [Profile (Markdown)](https://openaitools.app/tool/arize-com.md) ## Frequently Asked Questions ### How do Agenta and Arize AI compare in monthly traffic? Agenta has 45,760 monthly visits, while Arize AI has 249,028. Traffic estimates describe audience size, not product quality. ### How do their growth rates compare? Agenta has a month-over-month growth rate of +29.5%; Arize AI has +3.2%. ### How does pricing compare? Agenta: Freemium. Freemium. The open-source edition is MIT-licensed and free to self-host; cloud access is offered alongside it, with a demo available for larger teams. Exact seat and usage limits are listed on the pricing page. Arize AI: Paid, Contact for Pricing. Arize uses a paid model with a free entry point: teams can start free or book a demo, while enterprise-scale usage, deployment options, and support are quoted through a contact-sales process. The open-source Phoenix project is free to self-host. ## Alternatives | # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | 1 | [Ollama](https://openaitools.app/tool/ollama) | 9,421,973 | +4.3% | Free, Freemium, Paid | Run and build with open AI models locally or in the cloud | [.md](https://openaitools.app/tool/ollama.md) | | 2 | [Orca](https://openaitools.app/tool/orca) | 1,025,891 | +1% | Free, Open Source, Enterprise on request | Run many coding agents in parallel, each in its own worktree | [.md](https://openaitools.app/tool/orca.md) | | 3 | [Dify](https://openaitools.app/tool/dify-ai) | 819,691 | -15% | Freemium | Build, deploy, and govern production-ready agentic AI workflows | [.md](https://openaitools.app/tool/dify-ai.md) | | 4 | [LobeHub](https://openaitools.app/tool/lobehub) | 654,548 | -25% | Freemium | Put always-on AI agents on your team chat | [.md](https://openaitools.app/tool/lobehub.md) | | 5 | [Weights & Biases (CoreWeave Forge)](https://openaitools.app/tool/prompts) | 315,624 | -80.2% | Freemium, Contact for Pricing, Free | Excels at experiment tracking and model registry workflows, but is less purpose-built for real-time LLM production observability. | [.md](https://openaitools.app/tool/prompts.md) | | 6 | [Sim AI](https://openaitools.app/tool/sim-ai) | 140,953 | +20.6% | Freemium, Paid (per user/month), and Enterprise with custom pricing; self-hosting free under Apache 2.0 | Open-source workspace to build, deploy and govern AI agents | [.md](https://openaitools.app/tool/sim-ai.md) | | 7 | [Agno](https://openaitools.app/tool/agno) | 140,302 | -3.3% | Contact for Pricing, Free | Self-driving agent platform you run in your own cloud | [.md](https://openaitools.app/tool/agno.md) | | 8 | [Future AGI](https://openaitools.app/tool/future-agi) | 121,741 | -7.8% | Freemium, Usage-based | Includes integrated guardrails and simulation tools, but is less mature for large-scale enterprise production telemetry monitoring. | [.md](https://openaitools.app/tool/future-agi.md) | | 9 | [Maxim AI](https://openaitools.app/tool/maxim-ai) | 121,351 | +10% | Contact for Pricing, Free Open Source | Features strong testing and prompt evaluation tools, though its monitoring interface is less oriented towards massive telemetry streams. | [.md](https://openaitools.app/tool/maxim-ai.md) | | 10 | [PromptLayer](https://openaitools.app/tool/promptlayer) | 85,899 | -4.6% | Free Trial, Contact for Pricing | Focuses heavily on prompt management and versioning control, whereas Arize prioritizes end-to-end tracing and ML observability. | [.md](https://openaitools.app/tool/promptlayer.md) | --- Source: https://openaitools.app/compare/agenta-vs-arize-com More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)