MLOps platforms track experiments, version data and models, schedule training pipelines and move models into production.
This guide ranks eleven products on setup effort, the bill once users, runs and GPU hours grow, how runs and models can leave, and whether you can adopt the tool without a partner. Small independent vendors rank above larger owners.
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. Every vendor link is marked the same, whatever the arrangement behind it. How that works.
In short
What mlops platforms do
An MLOps platform records machine learning experiments, versions data and models, runs training pipelines on managed compute and deploys models, giving teams a repeatable route from notebook to production.
Five tests decide the order: setup effort, real cost, how your data leaves, whether you can walk away, and who the mlops platform was built for. A separate rule reserves the first three places for challengers and moves the household names to place 4 and below. The reasoning behind the five tests, and behind having no score out of ten, is set out on the how we work page.
Apache-2.0 pipeline framework from Munich with flat, seat-free hosted pricing
Ranked #1 of 11 in Best MLOps Platforms in 2026.
Open sourceSelf-hostablePublished pricingEurope
Starter allows 100 pipeline runs a month and unlimited seats, so a team of fifteen pays what a team of two pays. The open-source edition has no run limit but you operate the server. ZenML says it keeps metadata only, with data, artifacts and compute in your own cloud.
Self-hosted use gets community support; email support starts at Starter, and audit logs, SCIM and air-gapped installs are Enterprise. The company describes itself as a remote team with a Munich office.
What stands out
Apache-2.0
Unlimited seats
Pipelines
Where it costs you
The hosted Starter plan covers a single ML project, so a company with several teams moves to Enterprise quickly.
The pricing page puts AI-agent monitoring first, which is a different buying story from classic model training.
Right for
Small ML teams wanting an open-source pipeline layer on their own cloud
Wrong for
Companies wanting one vendor to host training, registry and serving
GermanyOpen source self-hosted edition at no cost; Starter plan published monthly; Enterprise quoted; 14-day trial
Apache-2.0 experiment, data and orchestration suite with a hosted community plan
Ranked #2 of 11 in Best MLOps Platforms in 2026.
Free tierSelf-hostablePublished pricingNorth America
The community plan includes 100 GB of artifact storage, 1 GB of metric events and 1M API calls a month for up to three users. Pro adds cloud auto-scaling, hyperparameter optimisation and pipeline triggers, with extra usage priced per GB, per MB of events and per 100K API calls.
Scale targets organisations with 8 to 48 GPUs and is VPC only. Enterprise adds Slurm and LDAP integration. The open-source tier has the same experiment tracking and pipelines.
What stands out
Apache-2.0
Self-hostable
GPU scheduling
Where it costs you
Hyper-Datasets and dynamic resource allocation are not in the community or Pro plans.
Pro bills storage, metric events and API calls on top of the per-user fee, so the monthly total moves with activity.
Right for
Teams that want tracking, orchestration and data versioning from one self-hostable tool
Wrong for
Buyers wanting a single flat price with no usage meters
United StatesFree community plan for up to 3 users; Pro priced per user plus usage; Scale and Enterprise quoted
Per-user MLOps platform from Turku, deployable on-premises or air-gapped
Ranked #3 of 11 in Best MLOps Platforms in 2026.
Self-hostablePricing on requestEurope
In this market Valohai stands out for where it can be installed. Managed cloud, private cloud, on-premises and air-gapped setups are all listed, and the vendor is Valohai Oy in Turku, under Finnish law.
Experiments, pipelines and deployments carry no cap, and a 14-day trial opens every feature. The cost is opacity: amounts are shown nowhere, so a shortlist cannot rank it against platforms with printed monthly rates until someone has asked for a quote.
What stands out
Per-user licence
Air-gapped option
Git-based runs
Where it costs you
Reading the pricing page gives no figure, so comparing it with ZenML or ClearML on cost needs a quote first.
Seat-based licensing means a wide group of occasional experimenters is billed like daily users.
Right for
Finnish-run or air-gapped environments that need an MLOps platform on their own hardware
Wrong for
Buyers who want to read a price and sign up the same afternoon
FinlandPer-user licence, quoted per organisation; 14-day trial
Experiment tracking and registry from New York, sold apart from its Opik product
Ranked #4 of 11 in Best MLOps Platforms in 2026.
Free tierPublished pricingNorth America
The MLOps Free plan tracks and compares training runs, versions datasets and includes a model registry for a single user. Pro is priced per user per month, up to ten users and 1,500 training hours, with email support.
Enterprise lifts the limits and adds service accounts, view-only users and model production monitoring. Comet states it has offices in Tel Aviv and a remote-first workforce. Opik, the LLM product, has a free open-source version.
What stands out
Experiment tracking
Model registry
Per-user Pro
Where it costs you
Production monitoring of models is listed only under Enterprise.
The pricing page leads with Opik, so MLOps buyers must read past the LLM plans to find their own.
Right for
Research-style teams tracking many training runs who want a hosted comparison UI
Wrong for
Teams needing production model monitoring without an Enterprise contract
United StatesFree plan for one platform user; Pro per user monthly, published; Enterprise quoted
Flyte-based orchestration billed by actions, vCPU, memory and GPU hours
Ranked #5 of 11 in Best MLOps Platforms in 2026.
Self-hostablePublished pricingNorth America
Team asks for a monthly minimum, credited against usage and waived in the first month, which also starts with a usage credit and no card. Limits include 10,000 concurrent actions and 200,000 actions per run.
GPU hours fall into three price classes that are the same on every cloud. Optional Union-run cluster management is priced per cluster with a cap. Flyte, the open-source engine underneath, is Apache-2.0 and can be run without Union.
What stands out
Metered usage
Flyte-based
Self-hosted option
Where it costs you
Every task, cache hit and artifact version counts as an action, which makes cost hard to predict from job counts.
Team is limited to two active clusters and 30-day data retention.
Right for
Engineering-led teams running many parallel workflows who prefer pay-for-use billing
Wrong for
Finance teams wanting a fixed annual number without usage meters
United StatesMetered rate card published; Team plan has a monthly minimum credited to usage; Enterprise quoted
Apache-2.0 tracking, registry and evaluation layer backed by the Linux Foundation
Ranked #6 of 11 in Best MLOps Platforms in 2026.
Open sourcePricing on requestElsewhere
MLflow sits at the zero-licence end of this list, next to Kubeflow and Metaflow. Apache-2.0 code on GitHub, a Linux Foundation badge and a feature set covering tracking, evaluation, registry, deployment, tracing and an AI Gateway make it a common base layer.
Its weakness for a European buyer is the missing legal entity. No vendor, no stated establishment and no plan means procurement, support and data-residency questions have to be answered by your own team.
What stands out
Apache-2.0
Linux Foundation
Tracking and registry
Where it costs you
The site names no company, address or country, so there is nobody to hold to a contract or a data location.
Without a pricing page, the true cost is the engineering time spent on hosting and upgrades.
Right for
Buyers who would rather assemble their own stack around a neutral, foundation-backed project
Wrong for
Organisations that need a named European supplier to sign with
Not stated by the vendorOpen source under the Apache-2.0 licence on GitHub
Netflix-born Python framework, deployed on your own cloud or Kubernetes
Ranked #7 of 11 in Best MLOps Platforms in 2026.
Open sourcePricing on requestNorth America
Flows run locally and then deploy to production with one command, with results stored and tracked automatically. Compute can use GPUs, many cores and parallel instances on AWS Batch, Step Functions, EKS, AKS or GKE.
Metaflow Sandbox lets you try it in a browser. Anaconda Inc. of Austin, Texas announced the Outerbounds acquisition on 29 April 2026. The framework stays Apache-2.0, so the code remains yours whatever happens to the company.
What stands out
Apache-2.0
Python flows
Bring your cloud
Where it costs you
The commercial platform changed owner in 2026, so roadmap and pricing may change.
A model registry and serving layer are not described on the framework's site.
Right for
Python-first data science teams wanting laptop-to-cloud workflows on existing infrastructure
Wrong for
Teams wanting a hosted web console with registry and monitoring included
United StatesOpen source framework under Apache-2.0; the Outerbounds platform is shown with demos
Kubernetes-native CNCF project assembled from separate training, pipeline and registry parts
Ranked #8 of 11 in Best MLOps Platforms in 2026.
Open sourcePricing on requestElsewhere
The website lists Spark Operator, Notebooks, Trainer for distributed LLM fine-tuning, Katib for AutoML, Hub, Pipelines and a central dashboard as separate subprojects with their own repositories.
Documentation exists for many releases, which suggests upgrades are a recurring chore. The code is Apache-2.0 and the project says it runs on a laptop, on-premises or any cloud. No pricing exists because nothing is sold.
What stands out
CNCF project
Kubernetes-native
Modular
Where it costs you
Every component presumes a Kubernetes cluster and someone who can run it.
The site shows no commercial support option for the full stack.
Right for
Platform teams already running Kubernetes and wanting parts they assemble themselves
Wrong for
Small data teams without cluster operators or time for setup
Not stated by the vendorOpen source, Apache-2.0 repository; runs on your own Kubernetes
Apache-2.0 Git extension for data versioning, hosted beside the lakeFS product
Ranked #9 of 11 in Best MLOps Platforms in 2026.
Open sourcePublished pricingNorth America
The homepage splits the audience in two: individual data scientists get DVC as a free, open-source Git extension with a VS Code extension, while enterprise teams get lakeFS.
Next to it, lakeFS Team has a monthly price with 5 users, 500 GB of data and a 30-day trial, self-managed. The DVC repository is Apache-2.0. Expect to combine it with a tracker and an orchestrator.
What stands out
Data versioning
Apache-2.0
Git extension
Where it costs you
It covers data and model versioning only, with no scheduler, registry UI or serving.
Larger estates are pointed to lakeFS, a different product with its own plans.
Right for
Individual data scientists adding data versioning to an existing Git workflow
Wrong for
Teams needing orchestration, serving or monitoring from the same tool
United StatesOpen source under Apache-2.0 for DVC; lakeFS plans beside it are published (Team) or quoted (Enterprise)
Kubernetes model serving from London, now part of TrueFoundry
Ranked #10 of 11 in Best MLOps Platforms in 2026.
Pricing on requestEurope
Products listed are Core 2, MLServer, an LLM Module, an Enterprise Platform, Alibi Detect and Alibi Explain. MLServer is described as a lightweight inference server compatible with the Open Inference Protocol.
The homepage banner says Seldon is now TrueFoundry, and existing enterprise customers are offered a path to agentic AI. The company says it was founded in London in 2014. Check which brand you will actually contract with.
What stands out
Model serving
Business Source Licence
Drift detection
Where it costs you
Licensing is source available rather than permissive, which limits how freely you can resell or fork it.
The site shows no prices and no trial, only briefings and demos.
Right for
Platform teams standardising real-time model serving on Kubernetes
Wrong for
Teams that need experiment tracking or a pipeline scheduler
United KingdomSource-available Core 2 under the Business Source Licence; modules and platform quoted after a briefing
Experiment tracking with published seat plans, now under CoreWeave Forge
Ranked #11 of 11 in Best MLOps Platforms in 2026.
Free tierPublished pricingNorth America
Weights & Biases is the one tool here that publishes a full ladder of prices for small teams: Free with five model seats and 5 GB, then Pro from $60 a month after a 30-day trial. The catch sits higher up.
Pricing now appears under CoreWeave Forge, the contract names a Delaware company in San Francisco, and Weave ingestion is metered at $0.10 per MB beyond the included amount, so the visible monthly figure can understate the bill.
What stands out
Experiment tracking
Seat limits
Artifacts
Where it costs you
Pro stops at ten seats and companies under 50 employees, so growth forces a move to a quoted contract.
HIPAA, audit logs and customer-managed keys are held back for Enterprise, which matters to regulated buyers.
Right for
Small teams that want a printed monthly price and a free tier for evaluation
Wrong for
Regulated European buyers needing audit logs or their own keys before Enterprise
United StatesFree plan; Pro published monthly with a 30-day trial; Enterprise quoted
An MLOps platform records machine learning experiments, versions data and models, runs training pipelines on managed compute and deploys models, giving teams a repeatable route from notebook to production. Feature lists look alike, so we would settle these points one at a time instead.
01
Check who publishes a price
Prices are public at 6 products (ZenML, ClearML, Comet, Union.ai, DVC, Weights & Biases) and on request at the remaining 5, so budget time for quotes if your favourite is in that second group.
02
Look at where the vendor sits
Where a company is established decides which courts and regulators can reach it. Here that means 5 countries: North America 6, Europe 3, Elsewhere 2.
03
Check the open source options
Source code is available for ZenML, MLflow, Metaflow, Kubeflow, DVC. Running it yourself swaps the licence fee for your own upkeep.
What an MLOps platform invoice is really measured in
The unit on the bill differs more than the features do. ZenML sells a flat Starter plan with unlimited seats, ClearML and Comet charge Pro per user, Weights & Biases limits model seats at each tier, and Union.ai meters actions, vCPU, memory and GPU hours. MLflow, Kubeflow and Metaflow carry no licence fee at all, because the cost moves into the engineers who run them.
A team of eight that trains once a week is cheapest on a per-user plan, while a platform team firing thousands of parallel tasks is cheapest where seats are free. Ask each vendor for your last three months of job counts and GPU hours, then price both models. Note which caps matter: ZenML Starter allows 100 pipeline runs a month, and Comet Pro stops at ten users.
Count users, monthly runs and GPU hours before asking for prices
Find the cap on users, projects or runs in each plan
Ask what happens to the price in a month with ten times the usual jobs
Hosted tracking, self-run frameworks and the work in between
Self-run is the default in this market: Kubeflow, MLflow, Metaflow and DVC are open-source code you deploy yourself, and ClearML, ZenML and Union.ai offer both a hosted service and a path to run it on your own cloud. The saving is real but so is the work.
Kubeflow assumes a Kubernetes cluster and an operator, and its site lists separate subprojects for notebooks, training, tuning, pipelines and registry that each need upgrading. A hosted plan from Comet or Weights & Biases removes that work but caps users or seats. Estimate the ninety-day setup in engineer-weeks, then multiply by the loaded salary. If that product exceeds a year of the hosted price, the free licence was not free.
Write down who will patch, upgrade and back up a self-run stack
Ask the vendor which features exist only in its hosted edition
Price the setup in engineer-weeks, not just licences
Where in the machine learning lifecycle each product stops
These products solve different stages, and a shortlist that mixes them compares nothing. DVC versions data and models and little else. Comet and Weights & Biases centre on tracking runs and registering results. Metaflow and Union.ai orchestrate workflows, and Seldon serves models and watches them for drift.
ClearML and Kubeflow span several stages, which suits a team that wants one place but means a shallower tool at each stage. Valohai covers data, models and pipelines around Git. List the stage you lack today, whether tracking, scheduling or serving, and shortlist only tools that name it. Check whether production monitoring is in the plan you can afford: Comet keeps it in Enterprise.
Name the single stage your team lacks today
Ask each vendor for the plan that includes production monitoring
Check how the tool exports runs and models if you leave
Ownership changes that affect a long MLOps contract
Several of the names in this market no longer belong to who built them. Anaconda acquired Outerbounds, the company behind Metaflow, in April 2026. Seldon's site says it is now part of TrueFoundry. Weights & Biases pricing sits under CoreWeave Forge, and DVC's site sits with lakeFS. None of these moves breaks a product, but each can change prices, support contacts and roadmaps within a contract term.
Open-source licences differ too: Apache-2.0 covers MLflow, Metaflow, ClearML, ZenML and Kubeflow, while Seldon Core 2 carries the Business Source Licence, which is source available and restricts reuse. Find out which entity signs, who owns the roadmap, and what the licence allows if the vendor is bought again.
Ask who the contracting entity is today and who owns the product
Read the licence file in the repository, not the marketing label
Ask for a clause on price changes after a change of control
What goes wrong most often when buying mlops platforms
Buying a per-user plan for a team of three, then discovering the pipeline workload needs hundreds of runs a month.
Shortlisting a data-versioning tool such as DVC next to a serving tool such as Seldon as if they were alternatives.
Treating Kubeflow or MLflow as free without budgeting the engineer who patches, upgrades and secures them.
Signing a multi-year term without checking whether the product was recently acquired or merged into another company.
07
Frequently asked questions
8 answers
What are the best mlops platforms in 2026?
ZenML leads our ranking of 11. ZenML GmbH, registered in Munich, publishes one flat monthly Starter plan with unlimited seats, capped at one AI agent, one ML project and 100 pipeline runs a month.
Its licence is Apache-2.0, and the self-hosted edition carries no run cap. ZenML stores only metadata while artifacts and compute stay in your cloud. The pricing page now leads with AI-agent tooling, and SSO, SCIM and on-prem sit in Enterprise.
Which mlops platforms publish their pricing?
Published pricing exists for 6 products on this page:
ZenML: Open source self-hosted edition at no cost; Starter plan published monthly; Enterprise quoted; 14-day trial.
ClearML: Free community plan for up to 3 users; Pro priced per user plus usage; Scale and Enterprise quoted.
Comet: Free plan for one platform user; Pro per user monthly, published; Enterprise quoted.
Union.ai: Metered rate card published; Team plan has a monthly minimum credited to usage; Enterprise quoted.
DVC: Open source under Apache-2.0 for DVC; lakeFS plans beside it are published (Team) or quoted (Enterprise).
Weights & Biases: Free plan; Pro published monthly with a 30-day trial; Enterprise quoted.
5 vendors do not print a price.
Is there a free mlops platform?
ClearML, Comet, Weights & Biases can be used for free, either as a free plan or a self-hosted edition.
Where are these mlops platform vendors established?
In 5 countries across 3 regions: North America 6, Europe 3, Elsewhere 2.
ZenML: Germany.
ClearML: United States.
Valohai: Finland.
Comet: United States.
Union.ai: United States.
MLflow: Not stated by the vendor.
Metaflow: United States.
Kubeflow: Not stated by the vendor.
DVC: United States.
Seldon: United Kingdom.
Weights & Biases: United States.
Which mlops platforms are open source?
ZenML, MLflow, Metaflow, Kubeflow, DVC.
Which mlops platforms can you host yourself?
ZenML, ClearML, Valohai, Union.ai. The other 7 are hosted by the vendor only.
What should you use instead of ZenML?
Second is ClearML, which suits teams that want tracking, orchestration and data versioning from one self-hostable tool. Third is Valohai, which suits Finnish-run or air-gapped environments that need an MLOps platform on their own hardware.
Who should not buy ZenML?
Companies wanting one vendor to host training, registry and serving. The hosted Starter plan covers a single ML project, so a company with several teams moves to Enterprise quickly..
Vendors who think their mlops platforms product should be here can tell us about it: what it does, who it is for. The call is ours, and software advice sets out what being listed means.
