# ClearML vs UbiOps

ClearML records 20,123 monthly visits with -60.9% month-over-month growth and Contact for Pricing pricing. UbiOps records 15,928 monthly visits with +2.6% growth and Contact for Pricing pricing.

| Metric | [ClearML](https://openaitools.app/tool/clear-ml) | [UbiOps](https://openaitools.app/tool/ubiops) |
| :--- | :--- | :--- |
| Website URL | https://clear.ml | https://ubiops.com |
| Categories | AI Developer Tools, Large Language Models (LLMs), AI Models, AI Workflow | AI Developer Tools, AI Models, AI Workflow, Large Language Models (LLMs) |
| Pricing Model | Contact for Pricing | Contact for Pricing |
| Monthly Visits | 20,123 | 15,928 |
| MoM Growth Rate | -60.9% | +2.6% |
| Primary Market | Germany | Netherlands |
| Direct Visits | 6,667 | 12,548 |
| Review Score | 4.3 | 4.2 |
| Saved Count | 1 | 0 |
| Supported Platforms | Website | Website |

## 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)

## UbiOps

UbiOps is a production-focused platform for turning machine learning and AI models into reliable, always-available services. Instead of asking data science teams to become infrastructure specialists, it abstracts away the Kubernetes and cloud plumbing so that a model can go from notebook to a versioned, monitored, autoscaling API endpoint in minutes. It supports everything from large language models and generative AI to computer vision pipelines and classic data science workloads.

The platform is designed around central control. IT and MLOps teams get one place to manage deployments, versioning, access rights, resource prioritization, auditing, and monitoring, which helps reduce shadow IT and vendor lock-in. Compute can be pulled from different clusters and environments, including Kubernetes, virtual machines, and bare metal, so organizations can keep workloads where regulation and cost make the most sense.

UbiOps is especially visible in regulated and critical sectors. Its customer stories include healthcare and immunotherapy research, national cyber security bodies, energy grid optimization, and large-scale computer vision in agriculture, which reflects its emphasis on security, compliance, and predictable scaling. Pricing is quoted per organization: there is a hosted UbiOps Cloud option with pay-per-use compute and a UbiOps Private option installed and managed in the customer's own environment, and interested teams typically book a demo or talk to sales for a tailored quote.

**Pricing & Plans:** Contact for pricing. UbiOps offers a hosted UbiOps Cloud plan with on-demand, pay-per-use compute, and a UbiOps Private plan deployed in the customer's own public cloud, private cloud, or on-premise environment with a dedicated customer success manager and custom SLAs. A free trial is available.

- **Single control plane for AI workloads:** Deploy and manage models from one interface whether they run locally, in a hybrid cloud, or spread across several cloud providers.
- **No-Kubernetes model serving:** Turn models into production API endpoints with versioning, scaling algorithms, and monitoring built in, so teams avoid managing Kubernetes and cloud infrastructure themselves.
- **On-demand GPU scaling:** Instantly scale AI and machine learning workloads onto GPU capacity as demand changes, keeping compute costs under control.
- **Built-in MLOps and governance:** API management, version control, access management, resource prioritization, auditing, and monitoring come as standard, helping prevent shadow IT.
- **Flexible compute sourcing:** Draw resources from Kubernetes clusters, virtual machines, or bare metal across clouds, supporting hybrid and multi-cloud strategies without lock-in.

**Strengths:** Abstracts away Kubernetes and cloud infrastructure, so data scientists ship models faster; Supports local, hybrid, and multi-cloud deployment, which limits vendor lock-in; Strong governance features including versioning, auditing, access control, and monitoring; Proven in regulated sectors such as healthcare, public sector, and critical infrastructure

**Limitations:** Pricing is quote-based, so costs are hard to estimate without contacting sales; Enterprise-grade platform may be heavier than needed for simple hobby or small-team projects; Requires some MLOps understanding to exploit advanced orchestration and governance features fully

[Profile (Markdown)](https://openaitools.app/tool/ubiops.md)

## Frequently Asked Questions

### How do ClearML and UbiOps compare in monthly traffic?

ClearML has 20,123 monthly visits, while UbiOps has 15,928. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

ClearML has a month-over-month growth rate of -60.9%; UbiOps has +2.6%.

### How does pricing compare?

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. UbiOps: Contact for Pricing. Contact for pricing. UbiOps offers a hosted UbiOps Cloud plan with on-demand, pay-per-use compute, and a UbiOps Private plan deployed in the customer's own public cloud, private cloud, or on-premise environment with a dedicated customer success manager and custom SLAs. A free trial is available.

## 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 | [RunningHub](https://openaitools.app/tool/runninghub) | 3,105,554 | -28.5% | Freemium | Cloud ComfyUI platform to run workflows and build AI apps | [.md](https://openaitools.app/tool/runninghub.md) |
| 3 | [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) |
| 4 | [Open WebUI](https://openaitools.app/tool/open-webui) | 804,534 | -2.8% | Freemium | Self-hosted AI workspace for local and cloud models | [.md](https://openaitools.app/tool/open-webui.md) |
| 5 | [Labelbox](https://openaitools.app/tool/labelbox) | 762,803 | -22.8% | Contact for Pricing | The environments frontier AI trains on, and the agents that do the recurring work | [.md](https://openaitools.app/tool/labelbox.md) |
| 6 | [Vocareum](https://openaitools.app/tool/vocareum-com) | 531,556 | +5.4% | Paid | Hands-on lab infrastructure for AI, cloud, and data education | [.md](https://openaitools.app/tool/vocareum-com.md) |
| 7 | [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) |
| 8 | [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) |
| 9 | [Arize AI](https://openaitools.app/tool/arize-com) | 249,028 | +3.2% | Paid, Contact for Pricing | Agent observability, evaluation, and continuous improvement for production AI | [.md](https://openaitools.app/tool/arize-com.md) |
| 10 | [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) |

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

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