Machine Learning · head to head
ClearML vs Ollama

ClearML
Machine Learning
Open-source MLOps platform for experiment tracking and orchestration
- From
- Free
- Rated
- -

Ollama
Machine Learning
Open-source tool for running LLMs locally on desktop and servers
- From
- Free
- Rated
- -
The short version
- Each has a real cost: ClearML broad scope means more to learn and more to run than a focused tracking tool; Ollama requires user to provide computational hardware; no free cloud compute; models may not fit in available RAM on typical machines
- Prices and features above were last checked on 29 August 2026.
Where they differ
Only the attributes on which ClearML and Ollama actually diverge.
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in ClearML
- Experiment tracking
- Remote execution
- Data versioning
- Pipelines
Only in Ollama
Nothing recorded that ClearML does not also cover.
What people use each for
The jobs each tool is most often brought in to do.
ClearML
- Tracking experiments across a team so results are reproduciblenot Ollama
- Moving training from laptops to shared GPU hardware without repackagingnot Ollama
- Versioning datasets alongside the experiments that consumed themnot Ollama
Ollama
- Local development and testing without API costs or rate limitsnot ClearML
- Privacy-sensitive applications requiring data to remain on-devicenot ClearML
- Cost-sensitive deployments where computational resources are already availablenot ClearML
- Fully offline environments or air-gapped networksnot ClearML
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
ClearML
- Broad scope means more to learn and more to run than a focused tracking tool
- Self-hosting the server is real infrastructure — database, file storage and web server
- Documentation quality is uneven across the newer parts of the platform
- Smaller community than the most popular tracking tools, so fewer worked examples exist
Ollama
- Requires user to provide computational hardware; no free cloud compute; models may not fit in available RAM on typical machines
- No hosted service option for inference; all computational burden falls to user
- Limited to open-weight models; cannot run proprietary models like GPT-4 or Claude locally
- Performance depends entirely on user's hardware; no SLAs or guarantees on speed
Pricing, plan by plan
ClearML
Free- Open sourceFree
- Experiment tracking
- Pipelines
- Self-hosted server
Ollama
Free- FreeFree
- CLI, API, desktop apps
- Unlimited public models
- 40,000+ community integrations
- Pro$20/month
- Access to larger, more powerful cloud models
- Run 3 concurrent cloud models
- 50x more usage than Free
- Max$100/month
- Run 10 concurrent cloud models
- 5x more usage than Pro
- Team$25/month
- Per seat pricing (5-seat minimum = $125/month)
- Shared billing
- Zero data retention
Which should you pick?
Choose ClearML if
- You need experiment tracking.
- You want to start without paying.
- You work on Linux, macOS, Windows, Docker, Kubernetes.
- You also want remote execution.
Choose Ollama if
- You want to start without paying.
- You work on macOS, Windows, Linux, Cloud (AWS, Google Cloud, Azure, self-hosted).
Questions people ask
- Is ClearML or Ollama better?
- Neither clearly leads. ClearML starts at Free and Ollama at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, ClearML or Ollama?
- ClearML starts at Free and Ollama at Free.
- Does ClearML or Ollama run on more platforms?
- ClearML runs on Linux, macOS, Windows, Docker, Kubernetes. Ollama runs on macOS, Windows, Linux, Cloud (AWS, Google Cloud, Azure, self-hosted).
- Can I use ClearML for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is ClearML best used for?
- ClearML is most often used for tracking experiments across a team so results are reproducible, moving training from laptops to shared gpu hardware without repackaging, versioning datasets alongside the experiments that consumed them. Of those, tracking experiments across a team so results are reproducible and moving training from laptops to shared gpu hardware without repackaging are not what Ollama is typically brought in for.
- What can ClearML do that Ollama cannot?
- ClearML covers Experiment tracking, Remote execution, Data versioning, Pipelines.
Answered from the vendors’ own pages
ClearML: Is ClearML free?
The open-source version is free and self-hostable. Hosted and enterprise tiers are paid.
Ollama: How much does Ollama cost?
Ollama is free to use with unlimited public models. Pro paid plans start at $20/month for 3 concurrent cloud models, or $100/month for Max with 10 concurrent models. Team plans cost $25/seat/month with a 5-seat minimum.
SourceClearML: How much code does tracking require?
Very little — adding a couple of lines to an existing training script captures parameters, metrics and environment automatically.
Ollama: What does the Ollama free tier include?
The free tier includes CLI and API access, unlimited public models, 40,000+ community integrations, and private data retention, though limited to 1 concurrent cloud model.
SourceClearML: Does ClearML replace MLflow?
It covers MLflow’s tracking and adds orchestration, remote execution and data versioning. Whether that breadth is an advantage or extra weight depends on whether you need the rest.
Ollama: How much usage is included with each Ollama plan?
Pro includes 50x more usage than Free, and Max includes 5x more usage than Pro. Session limits reset every 5 hours and weekly limits reset every 7 days across all tiers.
SourceOllama: Does Ollama log or train on user data?
No, Ollama explicitly states that prompt or response data is never logged or trained on, protecting user privacy across all plans.
SourceRelated pages
Other head to heads
- ClearML vs MLflow
- ClearML vs Weights & Biases
- ClearML vs Comet ML
- ClearML vs Neptune.ai
- ClearML vs Dataiku
- ClearML vs Pachyderm
- ClearML vs Azure Machine Learning
- ClearML vs Domino Data Lab
- ClearML vs DVC
- ClearML vs AWS SageMaker
- ClearML vs Google Vertex AI
- ClearML vs DataRobot
- ClearML vs Pinecone
- ClearML vs Python
- ClearML vs PyTorch
- ClearML vs scikit-learn
- ClearML vs Apache Spark MLlib
- ClearML vs Weaviate
- ClearML vs Groq
- ClearML vs Mistral AI
- ClearML vs OpenRouter
- ClearML vs LangChain
- ClearML vs OpenAI API
- ClearML vs Haystack
- ClearML vs Kubeflow
- ClearML vs Langwatch
- ClearML vs LlamaIndex
- ClearML vs Milvus
- ClearML vs Semantic Kernel
- Ollama vs MLflow
- Ollama vs Weights & Biases
- Ollama vs Comet ML
- Ollama vs Neptune.ai
- Ollama vs Dataiku
- Ollama vs Pachyderm
- Ollama vs Azure Machine Learning
- Ollama vs Domino Data Lab
- Ollama vs DVC
- Ollama vs AWS SageMaker
- Ollama vs Google Vertex AI
- Ollama vs DataRobot
- Ollama vs Pinecone
- Ollama vs Python
- Ollama vs PyTorch
- Ollama vs scikit-learn
- Ollama vs Apache Spark MLlib
- Ollama vs Weaviate
- Ollama vs Groq
- Ollama vs Mistral AI
- Ollama vs OpenRouter
- Ollama vs LangChain
- Ollama vs OpenAI API
- Ollama vs Haystack
- Ollama vs Kubeflow
- Ollama vs Langwatch
- Ollama vs LlamaIndex
- Ollama vs Milvus
- Ollama vs Semantic Kernel
