Software advice
Best MLOps Platforms in 2026
An MLOps platform is software that tracks machine learning experiments, versions data and models, orchestrates training jobs and registers models for deployment and monitoring. It differs from a notebook, which only runs code, and from a model API, which serves someone else's model. Some products here are open-source trackers, some are enterprise suites, and some are cloud-provider services.
Quick answer
ClearML ranks first for published prices and an open-source route. Valohai fits teams that want per-user licensing across clouds. Domino Data Lab fits regulated enterprises that need governance, while SageMaker AI and Vertex AI fit teams already on AWS or Google Cloud.
Choosing an MLOps platform comes down to who runs the infrastructure and who pays for each run. An open-source tracker is free and flexible but needs an owner. A suite brings governance and support, with a quoted price. A cloud service bills per instance hour inside an account you already hold. This ranking weighs how clearly each vendor states its prices and limits, what it says about hosting and compliance, and how easily a team could leave. We read vendor pages in October 2026 and ran no accuracy or speed tests. Where a page was blocked or showed no figures, we leave the number out.
The 13 best MLOps platforms in 2026, ranked
There is no score. Vendors can pay for more visibility on this website, for example a higher place in a ranking or more room on a page. Commercial relationships can also affect which products we feature and how prominently. How we rank.
| # | Tool | Best for | Free plan | Entry price |
|---|---|---|---|---|
| 1 | ClearML | Small ML teams that want an open-source tracker with a free hosted tier and a path to on-prem | Yes | $15 per user per month plus usage (Pro); Community is $0 for up to 3 users |
| 2 | Valohai | Teams running training on several clouds or their own GPUs that want one orchestration layer | Trial | Quoted |
| 3 | Comet | Teams that want classic experiment tracking and LLM tracing from one vendor, with an open-source option | Yes | $19 per month for Opik Pro Cloud; Opik Free Cloud is $0 |
| 4 | MLflow | Engineering teams that want a vendor-neutral tracking and registry layer they host themselves | Yes | Quoted |
| 5 | Weights & Biases | Research-heavy teams that want polished experiment dashboards and already use CoreWeave GPUs | Yes | $60 per month, billed monthly (Pro) |
| 6 | Domino Data Lab | Regulated enterprises that need reproducible model work with central governance and auditability | No | Quoted |
| 7 | Dataiku | Mixed teams of analysts and data scientists who share one governed workspace for data, models and agents | Trial | Quoted |
| 8 | DataRobot | Enterprises that want agents and predictive models built, monitored and governed on one vendor platform | No | Quoted |
| 9 | Amazon SageMaker AI | Teams already on AWS that want training, registry, feature store and hosting under one bill and IAM | Trial | Quoted |
| 10 | Vertex AI (Gemini Enterprise Agent Platform) | Google Cloud teams that want managed training, experiments, registry, features and monitoring in one console | Trial | Quoted |
| 11 | Databricks | Data teams that keep their data in a lakehouse and want tracking, registry and serving next to it. | Trial | Quoted |
| 12 | Azure Machine Learning | Teams on Azure that want managed training, pipelines and endpoints under existing access controls. | Trial | Quoted |
| 13 | Kubeflow | Platform teams that already run Kubernetes and want modular pipelines, tuning and notebooks they control. | No | Quoted |
- 1
ClearML
ClearML takes first place because its price page is the most complete in this field: Community at $0, Pro at $15 per user per month with itemized overage rates, and an open-source self-hosted route on GitHub. It covers experiment tracking, datasets, pipelines and agent-based job scheduling in one product. It ranks above larger suites on openness, not on breadth of governance.
Strong on
- Itemized overage prices for storage, metric events and API calls, printed on the pricing page
- Same core tracking, pipelines and orchestration available as self-hosted open source on GitHub
- Deploys hosted, in a VPC, on-prem, air-gapped or hybrid
Weak on
- Community is capped at 3 users and 1GB of metric events per month
- Fractional GPUs and quota management sit in the quote-only Scale and Enterprise tiers
Skip it if: Teams that want a single vendor to also supply notebooks, warehouses and business dashboards around the models. Pro covers up to 10 users and bills storage, metric events and API calls beyond the included amounts.
- 2
Valohai
Valohai ranks second because its pricing model is the easiest to forecast: a per-user license with unlimited projects, experiments, pipelines and deployments, rather than a meter on usage. It focuses on orchestration across virtual machines, Kubernetes and Slurm, with automatic versioning of code, data and runs. It trails ClearML only because no price is published and the free route is unspecified.
Strong on
- License is per user, so more experiments and pipelines do not raise the bill
- Runs workloads on VMs, Kubernetes or Slurm, on any cloud or on-prem
- Audit log and automatic lineage are listed as part of every subscription
Weak on
- No price is shown, so every comparison starts with a sales conversation
- Pricing page does not say what the free start includes or how long it lasts
Skip it if: Solo researchers and students who need a free tracker today and will not talk to sales. Priced per user license, not per workload, quoted on request. A Start free button exists, terms unpublished.
- 3
Comet
Comet sits third because it spans both eras of the work: experiment management, artifacts and a model registry on one side, and Opik for LLM tracing and evaluation on the other. Opik is open source and has a $0 cloud tier with 60-day retention. The pricing page foregrounds Opik, so a buyer of the classic tracker should confirm current terms with sales.
Strong on
- Opik is open source, with a free cloud tier of 10 team members and 25k spans per month
- Enterprise tier lists SOC 2, ISO 27001, ISO 9001, HIPAA and GDPR compliance
- Opik accepts OpenTelemetry traces and lists 40+ framework integrations
Weak on
- Free cloud keeps data for only 60 days
- Pro Cloud allows 100k spans a month before limits need a custom setting
Skip it if: Buyers who only want a model registry and deployment pipeline, since Comet's current pricing emphasis is LLM observability. Visible prices are for the Opik LLM product. Experiment-management plans were not shown on the page we read.
- 4
MLflow
MLflow ranks fourth because it is the default neutral layer rather than a finished product: tracking, evaluation, prompt versioning, tracing and a gateway under an Apache 2.0 license, backed by the Linux Foundation. Nothing to buy means nothing to lock you in. The price is that you run it, secure it and add the user management that commercial rivals bundle.
Strong on
- Apache 2.0 license with no paid tier needed to use every feature
- Supports Python, TypeScript and JavaScript, Java and R, and uses OpenTelemetry for traces
- Integrates with 100+ tools across LLM frameworks and classic ML libraries
Weak on
- No hosted free plan from the project itself; you operate the server
- Access control, SSO and support depend on the platform you run it on
Skip it if: Small teams without anyone to run a tracking server and database. Free under the Apache 2.0 license. Hosting, storage and operations are your own cost or a managed provider's.
- 5
Weights & Biases
Weights & Biases lands fifth because the product is strong but the commercial frame has changed: it now appears as Weights & Biases Models inside CoreWeave Forge, with a $0 Free plan and Pro from $60 per month. Pro is restricted to teams under 50 employees, so mid-sized companies land on quoted Enterprise pricing sooner than the headline suggests.
Strong on
- Free plan includes experiment tracking, tracing, scorers and an assets registry with lineage
- Enterprise adds HIPAA option, customer-managed encryption keys, SSO and audit logs
- Academic research use is free, with unlimited projects and 200GB storage
Weak on
- Pro is only for companies with fewer than 50 employees, per the pricing page
- Extra Weave data ingestion is billed per megabyte beyond the plan allowance
Skip it if: Companies above 50 employees that want published, self-serve pricing, or teams wary of tying tracking to a GPU cloud vendor. Pro includes up to 10 model seats and 100 GB storage, and is limited to early-stage teams under 50 employees.
- 6
Domino Data Lab
Domino Data Lab ranks sixth because it is built around governance rather than a lightweight start. Its three parts are a model factory for building, an app hub for serving AI applications, and a governance center for policies and cost visibility. Reproducibility is captured by default. It suits large regulated teams, and the absence of any published price puts it behind the self-serve options.
Strong on
- Governance center defines policies once and enforces them across the model lifecycle
- Every experiment, model and decision is captured automatically for reproduction
- Supports any language, framework and coding assistant in its build environment
Weak on
- No public pricing, free plan or trial on the pages we read
- The platform framing is enterprise-wide, which is heavy for a three-person team
Skip it if: Startups and academic groups that want to start tracking runs this afternoon without a demo. No prices or tiers are published; the site offers a demo only.
- 7
Dataiku
Dataiku ranks seventh because it is a broad analytics and AI platform in which MLOps is one part. Visual recipes, AutoML, code notebooks, a model registry and deployment automation sit on one canvas, with an LLM Mesh for routing between model providers. That breadth helps mixed teams, but it is more product than an engineer wanting only a tracker needs.
Strong on
- Visual ML and full-code work share one flow, which lets analysts and engineers collaborate
- Model registry covers approval workflows, lineage and performance monitoring
- LLM Mesh routes between providers with quotas and monitoring
Weak on
- Pricing and plan limits are not published on the pages we read
- The all-in-one design means ML tracking is not a standalone, lightweight tool
Skip it if: Code-first ML engineers who only want experiment tracking and a registry in their own repo. Prices are not shown on the pages we read; the site offers a demo and a trial.
- 8
DataRobot
DataRobot is eighth because its current pitch has shifted from automated machine learning to an agent workforce platform, with build, operate and govern functions. It can run on-prem, hybrid or across clouds. Monitoring and governance are strong selling points, but a buyer who came for classic MLOps must work through agent-centered messaging, and no price is published.
Strong on
- Runs on-prem, hybrid or across several clouds, per its home page
- Separates build, operate and govern roles, with audit documentation and testing frameworks
- Validated with NVIDIA Enterprise AI, per its home page
Weak on
- Home page copy centers on agents, so classic MLOps features are harder to find
- No public price list or plan table was visible on the pages we read
Skip it if: Teams that want open-source tooling or a transparent self-serve price before talking to a vendor. The pricing URL we requested showed the home page; no prices or plans were listed.
- 9
Amazon SageMaker AI
Amazon SageMaker AI is ninth because it is an infrastructure service first and an MLOps product second. Notebooks, training, Feature Store, HyperPod and a managed MLflow are separate meters, priced per instance hour. That gives scale and IAM integration to AWS shops, but a small team has to assemble the workflow and forecast a bill from many line items.
Strong on
- Free tier covers 250 notebook hours, 50 training hours and 150,000 serverless inference seconds for 2 months
- Pricing is pay-as-you-go with optional Savings Plans for committed usage
- Lists MLflow, Feature Store, Data Wrangler and HyperPod as billable components
Weak on
- Cost is split across many per-hour line items, hard to estimate without the calculator
- Free tier lasts only 2 months from the first resource
Skip it if: Teams that run on another cloud or want one flat per-seat price. Pay-as-you-go by instance and usage, with Savings Plans. The free tier runs for the first 2 months.
- 10
Vertex AI (Gemini Enterprise Agent Platform)
Vertex AI is tenth because its MLOps parts are solid but tied to Google Cloud and now sit under a new name, Gemini Enterprise Agent Platform, in Google's docs. Experiments, Model Registry, Feature Store, pipelines and model monitoring are all documented. Pricing is per resource, so it fits teams already committed to Google Cloud rather than those shopping for a neutral tool.
Strong on
- Docs list experiments, model registry, feature store, training pipelines and monitoring as managed services
- Training and prediction are charged in 30-second increments
- Supports TensorFlow, PyTorch, scikit-learn and XGBoost in managed training
Weak on
- Product naming has changed, so docs and older tutorials use different names
- Billing is per resource and deployed endpoints keep charging until undeployed
Skip it if: Teams on AWS or Azure, or anyone who wants a tracker portable across clouds. Billed by usage; image AutoML training is $3.465 per hour. A free-start button is shown, terms unchecked.
- 11
Databricks
Databricks takes eleventh place because its managed MLflow is strong only when the work already lives on the Databricks platform. The docs describe experiment tracking, a model registry in Unity Catalog with lineage, and model serving as REST endpoints, plus tracing for agents. The pricing page lists billing in DBUs per second and shows no per-DBU rate. A standalone tracker is cheaper for teams outside it.
Strong on
- Model registry in Unity Catalog adds governance, lineage and cross-workspace access
- Registered models can be deployed as REST endpoints that capture requests and responses
- Managed MLflow adds customer-managed key encryption that open-source MLflow lacks
Weak on
- DBU billing needs a calculator, and cloud storage and networking costs come on top
- Free trial converts automatically to a paid plan when its credits run out
Skip it if: Small teams with no lakehouse that only want to log experiments and compare runs. Pay-as-you-go in DBUs, billed per second. Per-DBU rates sit in a separate price list, and cloud costs are extra.
- 12
Azure Machine Learning
Azure Machine Learning ranks twelfth because it works best as a part of an Azure estate, not as a neutral tool. Microsoft's pricing page says the service adds no charge of its own, and compute is billed by the second. Documentation lists MLflow integration, pipelines, managed real-time and batch endpoints, and lineage. It also warns that prompt flow retires on April 20, 2027, which affects teams building LLM apps.
Strong on
- Pricing page states there is no additional charge for the service, only for resources used
- Docs list MLflow integration, pipeline scheduling and Git integration for auditable lifecycles
- Docs state it does not store or process data outside the region where you deploy
Weak on
- Prompt flow is announced to retire on April 20, 2027
- Storage, key management and monitoring are billed as separate Azure services
Skip it if: Teams on AWS or Google Cloud, or anyone who wants a tracker that moves between clouds. No charge from the service itself. Compute, storage and related Azure services are billed separately, by the second.
- 13
Kubeflow
Kubeflow ranks thirteenth because it is a toolkit to assemble, not a product to buy. The project site describes modular subprojects, including Kubeflow Pipelines, Katib for tuning, Trainer, Notebooks and a Hub with a model registry, with a banner marking it a CNCF graduated project. Nobody sells support through that page. It suits teams with Kubernetes skills, while smaller groups tend to prefer a hosted tracker.
Strong on
- Subprojects such as Pipelines, Katib and Notebooks can be used on their own
- Site banner marks Kubeflow as a CNCF graduated project with community governance
Weak on
- Kubernetes-native design implies running and upgrading a cluster yourself
- The introduction page names no vendor support or price, so it fits self-reliant teams only
Skip it if: Data scientists without platform engineers, or teams wanting a hosted dashboard working in an afternoon. Open-source and free to download. Your own Kubernetes cluster, staff time and any hosting costs are the real price.
How these MLOps platforms were judged
How do you judge the quality of an MLOps platform?
Quality here means how reliably a platform records and reproduces work, not how accurate its models are. Check what is captured automatically: ClearML lists code, uncommitted changes and package versions, Valohai lists automatic versioning and full lineage, and Domino says every experiment and model is captured by default. MLflow adds tracing and 50+ built-in evaluation metrics. Vendors publish no benchmark comparing these tools, so run one real project end to end, then rerun it from a clean machine and see whether the result matches.
What does an MLOps platform cost per month in 2026?
As of October 2026, published prices run from $0 to a few tens of dollars per user. ClearML Pro is $15 per user per month plus usage, Comet Opik Pro Cloud is $19 per month, and Weights & Biases Pro starts at $60 per month for teams under 50 employees. MLflow is free software. Valohai charges per user license on quote. Domino, Dataiku and DataRobot showed no prices to us. SageMaker AI and Vertex AI bill by resource, so estimate with their calculators before committing.
Where does an MLOps platform store your data and models?
Where artifacts live matters more than where metrics live, because training data can be sensitive. ClearML, Valohai and Domino describe VPC, on-prem or air-gapped deployment, and MLflow runs wherever you host it. Weights & Biases lists SOC 2 Type II and a HIPAA option on Enterprise. Comet lists SOC 2, ISO 27001, ISO 9001, HIPAA and GDPR on Enterprise. For Dataiku, DataRobot, SageMaker AI and Vertex AI, the vendor's policy was not checked for this, so read their security documentation.
How hard is it to move off an MLOps platform?
Lock-in sits in the tracking SDK calls, the registry and the pipeline definitions. MLflow and ClearML are open source, so your history can stay in a server you control. Hosted suites keep run history, lineage and approvals inside their own data model, and none of the pages we read promised a full export. Cloud services add a second layer, since training jobs reference that cloud's storage and identity. Keep experiment code behind a thin logging wrapper so a switch changes one module, not every script.
Which MLOps platform fits a startup, a research lab or a bank?
Match the platform to the size of the team and the cost of an audit failure. Startups and labs do well with a free tier or open source: ClearML Community, MLflow, or Comet Opik. Teams on shared GPU clusters should look at Valohai or ClearML's orchestration. Banks and pharma that must prove how a model was built should shortlist Domino, Dataiku or DataRobot for governance. Teams fully on one cloud can start with SageMaker AI or Vertex AI and avoid a procurement cycle.
How to choose a MLOps platform in three steps
- Decide who will run the infrastructure. Write down who patches the server, rotates credentials and answers an outage. If nobody on your team wants that job, rule out self-hosted MLflow or open-source ClearML and pick a hosted plan, a suite with managed deployment, or a cloud service. If your data may not leave your network, reverse the logic: keep only products that list VPC, on-prem or air-gapped options, and ask each vendor to confirm in writing which components run inside your account, and which ones stay on its servers.
- Price a real workload, not the plan page. List last quarter's runs: how many users, how many experiments a week, how much artifact storage, how many GPU hours. Then price that list on each shortlisted product. Usage-metered plans such as ClearML Pro and Weights & Biases bill storage or data ingestion beyond the allowance, cloud services bill every instance hour, and per-user licenses such as Valohai's do not move with volume. A low sticker price can still produce a high bill once real volume arrives, so test the numbers.
- Run one project end to end and then leave. Pick one pipeline that matters, from data to registered model, and run it on the top two candidates. Then try to reproduce a three-week-old run on a different machine, and export your run history. A platform that handles both in an afternoon will hold up under growth. One that needs a support ticket to export will be costly to leave, so find that out before any contract starts, while walking away is still free. Write down the result for both candidates.
Frequently asked questions
What is the best MLOps platform in 2026?
ClearML ranks first on this page because it publishes a $0 Community tier, a $15 per user Pro tier and an open-source self-hosted option. The best choice still depends on your setup: Valohai fits multi-cloud orchestration, Domino fits regulated enterprises, and SageMaker AI or Vertex AI fit teams already on AWS or Google Cloud.
Is there a free MLOps platform?
Yes. MLflow is free under the Apache 2.0 license, ClearML Community is $0 for up to 3 users, Weights & Biases has a $0 Free plan, and Comet's Opik has a $0 cloud tier. Each has limits on users, storage or retention, so check them against your project before relying on a free tier for production.
What is the difference between MLflow and ClearML?
MLflow is an open-source project under the Linux Foundation that you host yourself, with tracking, evaluation, tracing and a gateway. ClearML is a company product that is also open source on GitHub, but adds hosted plans, pipelines and agent-based job scheduling. Choose MLflow for neutrality, ClearML for a ready hosted option.
Do I need an MLOps platform if I only train a few models?
Probably not at first. A Git repository, a spreadsheet of runs and a storage bucket can work for one or two models. A platform pays off when several people train models, when runs must be reproduced for audit, or when GPU time is shared. Free tiers from ClearML, Weights & Biases or Comet make a low-cost trial easy.
Which MLOps platforms can run on-prem or air-gapped?
Valohai says it can be deployed on-premises, in a private cloud or in air-gapped environments, and ClearML lists on-prem, air-gapped and hybrid options for Scale and Enterprise. DataRobot lists on-prem and hybrid, and MLflow runs wherever you install it. Confirm the exact components with each vendor, since some features stay hosted.
How much does Weights & Biases cost?
As of October 2026, the Weights & Biases pricing page lists Free at $0 per month, Pro from $60 per month billed monthly with a 30-day trial, and Enterprise on quote. Pro is limited to early-stage teams of fewer than 50 employees, and additional data ingestion and storage are billed beyond the plan allowance.
Are SageMaker and Vertex AI MLOps platforms?
Both include MLOps components. SageMaker AI lists Feature Store, MLflow, Data Wrangler and HyperPod as billable parts. Vertex AI documents experiments, a model registry, a feature store, training pipelines and model monitoring. They are cloud services billed per resource, rather than neutral trackers, so they suit teams committed to one cloud.
Can MLOps platforms track LLM applications as well as classic models?
Several now do. MLflow offers tracing, evaluation metrics, prompt versioning and an AI gateway. Comet's Opik is built for LLM tracing and evaluation, and Weights & Biases lists application tracing and scorers. Dataiku and DataRobot add agent and LLM features. Check whether tracing is billed separately, as Comet bills by spans per month.