# Arize AI vs ClearML

Arize AI records 249,028 monthly visits with +3.2% month-over-month growth and Paid, Contact for Pricing pricing. ClearML records 20,123 monthly visits with -60.9% growth and Contact for Pricing pricing.

| Metric | [Arize AI](https://openaitools.app/tool/arize-com) | [ClearML](https://openaitools.app/tool/clear-ml) |
| :--- | :--- | :--- |
| Website URL | https://arize.com | https://clear.ml |
| Categories | Large Language Models (LLMs), AI Agent, AI Monitor, AI Developer Tools | AI Developer Tools, Large Language Models (LLMs), AI Models, AI Workflow |
| Pricing Model | Paid, Contact for Pricing | Contact for Pricing |
| Monthly Visits | 249,028 | 20,123 |
| MoM Growth Rate | +3.2% | -60.9% |
| Primary Market | United States | Germany |
| Direct Visits | 107,838 | 6,667 |
| Review Score | 4.6 | 4.3 |
| Saved Count | 1 | 1 |
| Supported Platforms | Website | Website |

## 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)

## ClearML

ClearML packages the operational side of AI into one platform. The Infrastructure Control Plane connects and manages GPU resources across on-premises, cloud, and hybrid environments, adding multi-tenancy, role-based access control, quota management, fractional GPUs, priority-based scheduling, and usage-based billing for chargebacks. The goal is straightforward: raise utilization of the hardware a company already owns and make GPU-as-a-Service practical for internal teams.

The AI Development Center is the builder-facing half. It provides a workbench with an integrated development environment for coding, testing, and deploying models, plus data integration, experiment tracking, automation, dashboards, pipelines, a model repository, and CI/CD hooks. Because it is cloud-agnostic and silicon-agnostic, teams can keep their existing tooling while gaining a consistent workflow from research through production.

The GenAI App Engine handles deployment of large language models onto those same clusters. ClearML manages networking, authentication, access control, and monitoring so a team can launch a secure LLM API with minimal setup, fine-tune an off-the-shelf model, or wire up data ingestion and vector databases. The platform is sold on an enterprise model — demos and quotes rather than self-serve checkout — and it publishes an annual State of AI Infrastructure report.

**Pricing & Plans:** ClearML is sold as an enterprise platform with demo-led and quote-based pricing; a free starting tier is advertised alongside Request Demo calls to action. Specific seat, GPU, or usage rates are not published, so buyers should contact sales for a quote.

- **Infrastructure Control Plane:** Connect and govern GPU clusters across on-prem, cloud, and hybrid setups with multi-tenancy, RBAC, quota management, fractional GPUs, and priority-based scheduling.
- **AI Development Center:** A builder workbench with an integrated development environment plus experiment tracking, data integration, dashboards, pipelines, model repository, and CI/CD integration.
- **GenAI App Engine:** Deploy LLMs onto existing clusters with handled networking, authentication, and security, so teams can launch secure model APIs with minimal manual configuration.
- **Usage-based billing and chargebacks:** Granular cost tracking by compute hours, storage, and API calls lets IT teams allocate spend back to the projects and departments that consume it.
- **Vendor- and cloud-agnostic design:** Works across different silicon, clouds, and environments, avoiding lock-in while keeping one consistent workflow from experimentation to production.

**Strengths:** One platform covers cluster management, MLOps, and LLM deployment rather than stitching three vendors together; Strong GPU utilization, scheduling, and chargeback features aimed squarely at cost control; Cloud-, silicon-, and vendor-agnostic, which limits lock-in for mixed infrastructure; Meaningful adoption footprint with enterprise references, documentation, and training guides

**Limitations:** Enterprise, quote-based sales means no transparent public pricing and a longer procurement cycle; The breadth of the platform can be heavy to adopt for small teams that only need experiment tracking; Self-hosting and cluster integration work assumes real infrastructure and platform engineering capacity

[Profile (Markdown)](https://openaitools.app/tool/clear-ml.md)

## Frequently Asked Questions

### How do Arize AI and ClearML compare in monthly traffic?

Arize AI has 249,028 monthly visits, while ClearML has 20,123. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Arize AI has a month-over-month growth rate of +3.2%; ClearML has -60.9%.

### How does pricing compare?

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. ClearML: Contact for Pricing. ClearML is sold as an enterprise platform with demo-led and quote-based pricing; a free starting tier is advertised alongside Request Demo calls to action. Specific seat, GPU, or usage rates are not published, so buyers should contact sales for a quote.

## 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 | [ModelScope](https://openaitools.app/tool/modelscope-community) | 3,085,423 | +5.1% | Free | Open-source model and dataset hub with one-click online demos | [.md](https://openaitools.app/tool/modelscope-community.md) |
| 3 | [SuperAnnotate](https://openaitools.app/tool/super-annotate) | 386,034 | +37.4% | Free Trial, Contact for Pricing | AI data infrastructure for building production-grade models | [.md](https://openaitools.app/tool/super-annotate.md) |
| 4 | [Weights & Biases (CoreWeave Forge)](https://openaitools.app/tool/prompts) | 315,624 | -80.2% | Freemium, Contact for Pricing, Free | Track, evaluate, and ship AI models and agents in one connected environment. | [.md](https://openaitools.app/tool/prompts.md) |
| 5 | [Label Studio](https://openaitools.app/tool/label-studio) | 137,199 | -16.1% | Contact for Pricing, Free | Label any data. Evaluate any AI. | [.md](https://openaitools.app/tool/label-studio.md) |
| 6 | [Future AGI](https://openaitools.app/tool/future-agi) | 121,741 | -7.8% | Freemium, Usage-based | Build, simulate, evaluate and monitor self-improving AI agents | [.md](https://openaitools.app/tool/future-agi.md) |
| 7 | [Maxim AI](https://openaitools.app/tool/maxim-ai) | 121,351 | +10% | Contact for Pricing, Free Open Source | Evaluate, monitor, and govern your AI applications in production | [.md](https://openaitools.app/tool/maxim-ai.md) |
| 8 | [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) |
| 9 | [Confident AI](https://openaitools.app/tool/confident-ai) | 83,400 | -3.3% | Freemium, Paid, Contact for Pricing | Offers comprehensive LLM evaluation and unit testing via DeepEval, but lacks Arize's deep production monitoring infrastructure. | [.md](https://openaitools.app/tool/confident-ai.md) |
| 10 | [LangWatch](https://openaitools.app/tool/langwatch) | 52,098 | +73% | Contact for Pricing | Agent observability, evals and governance for AI platform teams | [.md](https://openaitools.app/tool/langwatch.md) |

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Source: https://openaitools.app/compare/arize-com-vs-clear-ml

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