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. The largest vendors in MLOps platforms
cannot hold places 1 to 3. Places 1 to 3 go to challengers, vendors outside the best-known brands in a category,
wherever the category has them; the established names follow from place 4 in their ranked order. How it works: placement disclosure ·
editorial process.
The 16 tools, reviewed
#1 ZenML
Open source pipeline orchestration for ML and AI · Germany · zenml.io
Open source Pipelines Self-hosted
ZenML describes itself as self-hosted, Apache 2.0 pipeline orchestration for AI and ML on your own infrastructure, and its pricing page shows two entrances: the free open source package, or the hosted Pro Starter at $39 a month.
- Pricing: Open source + paid tiers. The open source package is free. Free plan: open source.
- Best for: Small teams wanting pipelines first
- Strengths: Pipeline orchestration; Artifact versioning and lineage; Pipelines, traces, evals and replay.
Where it falls short
Pro Starter is capped at one AI agent, one ML project and 100 pipeline runs a month, so a team with several projects outgrows it quickly, and the price of the next tier is not on the page we read.
Wrong for
Teams with many ML projects who will hit the Starter caps, and buyers who want one vendor to own training, serving and production monitoring in a single product.
Visit ZenML →
#2 MLflow
Free open source platform for ML and LLM work · Not stated · mlflow.org
Open source Tracking Model registry
MLflow is the default answer when a team asks for experiment tracking and a model registry without a procurement process.
- Pricing: Open source. Free; the project site calls it forever free and shows no tiers or prices. Free plan: open source.
- Best for: Teams with Python skills and a server
- Strengths: Experiment Tracking; Model Registry and Deployment; Observability.
Where it falls short
The site describes no managed offering, support tier or service agreement, so hosting, upgrades, access control and backups are your job.
Wrong for
Teams without anyone to host and maintain a server, and buyers who need a contract with support terms, who should look at a hosted option such as ZenML Pro or ClearML.
Visit MLflow →
#3 ClearML
ML and GenAI work management, hosted or self-hosted · United States · clear.ml
Free tier Experiment tracking Pipelines
ClearML packs experiment management, a model repository, pipelines and agent orchestration into one platform that you can use hosted, self-hosted or managed.
- Pricing: Free + per user / month. Community is $0. Free plan: free plan.
- Best for: Teams wanting a free hosted tracker
- Strengths: Experiment management; Model repository; Pipelines.
Where it falls short
Pro charges usage on top of the seat price, and the usage rates and currency are not named on the page we read.
Wrong for
Teams of more than three who expect to stay on the free tier, and buyers who need predictable monthly cost with no usage component.
Visit ClearML →
#4 Flyte
Open source durable runtime for AI workloads · Not stated · flyte.org
Open source Workflows Recovery
Flyte's pitch is that long jobs should survive trouble. The site says it recovers from out-of-memory errors and preempted nodes, restarts code failures from the failed task instead of the beginning, and supports branching, looping, parallel fan-out and autoscaling.
- Pricing: Open source. Free under Apache 2.0; Union.ai is named on the site as the enterprise platform. Free plan: open source.
- Best for: Platform teams running training at scale
- Strengths: Infrastructure recovery (OOM, preempted nodes); Code-failure recovery from the failed task; Runtime branching and looping.
Where it falls short
Flyte is a runtime, not a tracking and registry product, and the feature list we read has no experiment dashboard.
Wrong for
Analysts and small teams without someone to operate infrastructure, and buyers who mainly want experiment records; they should start with MLflow, ClearML or Comet.
Visit Flyte →
#5 Union.ai
Managed Flyte control plane in your own cloud · United States · union.ai
Managed Flyte Usage-based Own cloud
Union.ai describes itself as built by the creators of Flyte, and it sells a managed control plane that runs durable AI and ML workloads in your own cloud.
- Pricing: Usage-based. Team plan has a $950 monthly minimum credited to usage and the first $950 of usage is free. Free plan: free trial.
- Best for: Mid-sized teams wanting managed Flyte
- Strengths: Zero Trust security and observability; Replay and recover; Remote debugging.
Where it falls short
The Team plan limits you to two active clusters, 10,000 concurrent actions, 200,000 actions per run, 30-day data retention and standard 12x5 support.
Wrong for
Solo practitioners and small teams for whom $950 a month is more than the compute they run, and buyers who need around-the-clock support on the entry plan.
Visit Union.ai →
#6 Metaflow
Open source framework for ML and data science · United States · metaflow.org
Open source Framework Python
Metaflow is a framework for real-life ML, AI and data science, and the site says it was originally developed at Netflix.
- Pricing: Open source. Free open source framework; the site states no price. Free plan: open source.
- Best for: Data scientists writing Python workflows
- Strengths: Modeling; Deployment; Versioning.
Where it falls short
The site gives no pricing page, managed service, support terms or service levels, so you take on the infrastructure and the maintenance.
Wrong for
Companies that need audit trails, SSO and a vendor to call, and teams that want a visual interface for experiments.
Visit Metaflow →
#7 Kubeflow
Kubernetes-native open source ML toolkit · Not stated · kubeflow.org
Open source Kubernetes CNCF
Kubeflow is a modular collection rather than a single product: Notebooks, Trainer, Katib for hyperparameter tuning, Pipelines, a Hub for models, a Spark Operator and a Dashboard.
- Pricing: Open source. Free open source; the site shows no prices or hosted plans. Free plan: open source.
- Best for: Teams already running Kubernetes
- Strengths: Kubeflow Pipelines; Kubeflow Notebooks; Kubeflow Trainer.
Where it falls short
There is no vendor behind the site, no price, no support offer and no description of a hosted version, so you assemble, secure and upgrade the pieces yourself.
Wrong for
Teams without Kubernetes expertise, and anyone who wants a working tracker by Friday.
Visit Kubeflow →
#8 Comet
Hosted experiment tracking with LLM tracing add-on · United States · comet.com
Free tier Experiment tracking Hosted
Comet's MLOps product records experiments and keeps a model registry, hosted for you. The free plan is real but narrow: one user, 1,500 training hours and 100GB of data. Pro raises that to up to 10 users and 500GB.
- Pricing: Free + per user / month. MLOps Free is $0. Free plan: free plan.
- Best for: Individuals and small teams tracking runs
- Strengths: Experiment tracking; Model registry; AI application tracing (Opik).
Where it falls short
Free is limited to a single user, so any collaboration means paying.
Wrong for
Teams with more than ten users and large artifact volumes, and buyers who want pipeline orchestration.
Visit Comet →
#9 Valohai
Managed ML infrastructure and pipeline platform · Finland · valohai.com
Quote only Pipelines Auto-scaling
Valohai says it abstracts away the complexity of managing ML infrastructure, and its pricing page is blunt about the commercial model: a fixed monthly license per user, with projects, experiments, pipelines and deployments all described as unlimited.
- Pricing: Quote only. Per-user fixed monthly license by custom quote; a 14-day free trial is offered. Free plan: free trial.
- Best for: Teams that want infrastructure handled
- Strengths: Unlimited projects; Unlimited experiments; Unlimited pipelines.
Where it falls short
There is no published price, so you cannot compare it before talking to sales.
Wrong for
Buyers who must compare prices before a sales call, and teams that want an open source fallback; MLflow or ZenML gives them that.
Visit Valohai →
#10 Weights & Biases
AI developer platform for tracking, evals and agents · Not stated · wandb.ai
Free tier Tracking Evaluations
Weights & Biases offers experiments, traces, evaluations, a playground, online monitors, guardrails and serverless inference.
- Pricing: Free + tiered plans. Forge Free is $0 a month for up to 5 model seats and 5GB of storage a month. Free plan: free plan.
- Best for: Teams already on the CoreWeave stack
- Strengths: Experiments; Traces; Evaluations.
Where it falls short
The seat and storage limits are low: five seats and 5GB on Free, ten seats and 100GB on Pro, and Pro is meant for teams under 50 employees.
Wrong for
Teams over 50 employees on the Pro plan, buyers who want a vendor with no cloud-provider parent, and anyone needing open source.
Visit Weights & Biases →
#11 Polyaxon
ML experimentation and scheduling platform · Not stated · polyaxon.com
Scheduling Own clusters Flat monthly
Polyaxon positions itself around experimentation, automation and scheduling, with a managed control plane while data and compute stay in your own clusters. Its pricing page lists a flat monthly Platform plan at $555, a free trial, and free access for academics.
- Pricing: Flat monthly. Platform plan is $555 a month with a free trial, free for academics. Free plan: free trial.
- Best for: Teams keeping data in their own clusters
- Strengths: Workflow scheduling; Audit trail; Run comparison.
Where it falls short
The Platform plan allows three developer seats, one compute cluster, 50 concurrent runs and five active schedules, so $555 a month buys a small footprint.
Wrong for
Individuals and teams with tight budgets, and buyers with several clusters on the entry tier; MLflow or ClearML cost less to start.
Visit Polyaxon →
#12 DVC
Git-like data versioning for ML teams · Not stated · dvc.org
Open source Data versioning Git
DVC does one thing: it lets a data science team version datasets next to code, so that a model can be traced to the data that trained it.
- Pricing: Open source + paid tiers. DVC for VS Code is free and open source. Free plan: open source.
- Best for: Small projects needing data versions
- Strengths: Data Version Control Git extension; DVC for VS Code; lakeFS Team.
Where it falls short
DVC does not schedule pipelines, serve models or monitor them, so it is a companion rather than a platform.
Wrong for
Teams looking for a complete platform in one purchase, and large data estates that will outgrow the Git extension.
Visit DVC →
#13 Anyscale
Managed Ray platform for AI workloads · Not stated · anyscale.com
Ray Usage-based Bring your own cloud
Anyscale sells the Ray distributed computing framework as a managed service, either hosted by Anyscale or run in your own cloud.
- Pricing: Usage-based. Pay-as-you-go from $0.0135 per hour for CPU-only hosted compute, with $100 in starting credits. Free plan: free trial.
- Best for: Teams building on Ray
- Strengths: Hosted deployment; Bring your own cloud; Committed contracts.
Where it falls short
The listed features are about running compute, not about experiment records or registries, so you will still need a tracker.
Wrong for
Teams not using Ray, and buyers who mostly need experiment tracking or a model registry; MLflow, ClearML or Comet are closer to that job.
Visit Anyscale →
#14 Domino Enterprise AI Platform
Enterprise platform for building and governing AI · United States · domino.ai
Enterprise Governance Quote only
Domino describes an integrated environment for building, operating and governing AI models at scale, with a workbench, MLOps, data management, FinOps and governance in one product.
- Pricing: Quote only. Subscription by deployment type, quoted through sales. Free plan: none.
- Best for: Large regulated organizations
- Strengths: AI infrastructure; Data management; AI workbench.
Where it falls short
There is no published price, no free plan and no trial on the page we read, so evaluation starts with a sales call.
Wrong for
Startups and small teams, and buyers who want to try before they talk to sales.
Visit Domino Enterprise AI Platform →
#15 Iguazio AI Platform
Platform for production gen AI and model monitoring · Not stated · iguazio.com
Gen AI Monitoring Free trial
Iguazio frames its platform around two jobs: operationalizing generative AI applications and managing the risk of running them. The feature list covers LLM customization, GPU management, flexible deployment, pipeline orchestration, model monitoring, guardrails and serverless automation.
- Pricing: Quote only. Not stated on the page; a free trial is offered. Free plan: free trial.
- Best for: Teams operationalizing gen AI applications
- Strengths: LLM customization; GPU management; Flexible deployment.
Where it falls short
The page we read gives no prices, limits, support terms or country, and describes capabilities in marketing language without tier detail.
Wrong for
Buyers who need public pricing and clear limits before a call, and teams that mainly track classic experiments.
Visit Iguazio AI Platform →
#16 JFrog ML (formerly Qwak)
Build, deploy and monitor ML and GenAI systems · Not stated · qwak.com
Model registry Deployment Monitoring
The site banner reads Qwak is Now Officially Part of JFrog, and the product is now called JFrog ML. It covers a model registry, training, deployment at scale, real-time monitoring, prompt management, an LLM model library, LLM tracing and a vector store.
- Pricing: Quote only. Not stated on the page; a Start for Free button is shown with no terms. Free plan: free trial.
- Best for: JFrog customers adding model management
- Strengths: Model management and registry; Model training; Model deployment at scale.
Where it falls short
The page gives no pricing, limits or country, and the footer carries a 2024 copyright line, so we cannot say how current the page is.
Wrong for
Teams with no JFrog relationship who want clear pricing, and buyers needing an open source fallback; MLflow, ZenML or ClearML give them one.
Visit JFrog ML (formerly Qwak) →
Questions and answers
What is the best MLOps platform in 2026?
ZenML ranks first of 16, best for small teams wanting pipelines first. MLflow is second (teams with Python skills and a server) and ClearML third (teams wanting a free hosted tracker). Places 1 to 3 go to challengers, vendors outside the best-known brands, wherever the category has them.
Do I need an MLOps platform if I only have one or two models?
Usually not a full platform. Two models can be managed with experiment tracking, version control for code and a documented release routine. MLflow or a free ClearML or Comet account gives you the records without a bill.
Is open source MLOps software really free?
The license is free, the operation is not. MLflow, Kubeflow, Metaflow and Flyte can be downloaded at no cost, but you pay for the servers, the storage, upgrades and the on-call time when the tracking database fills up.
MLflow, Kubeflow or Flyte: how do they differ?
MLflow centers on experiment tracking, a model registry, evaluations and prompt management, and is the lightest to adopt. Kubeflow is a set of Kubernetes-native components for notebooks, training, tuning and pipelines, which assumes you already run Kubernetes.
Can an MLOps platform run in my own cloud or on premises?
Several can. ZenML lists on-premises and air-gapped deployment, Union.ai runs workloads in your own cloud, Anyscale offers a bring-your-own-cloud mode, and Polyaxon keeps data in your clusters under a managed control plane.
What does an MLOps platform cost for a ten-person team?
It ranges from the cost of your own servers to several thousand dollars a month.
How hard is it to move off an MLOps platform?
It depends on where your artifacts and pipeline definitions live. If pipelines are ordinary Python in your repository and artifacts sit in your own bucket, migration means rewriting the orchestration glue.
Which tools handle LLM and agent work as well as classic ML?
Many now list it.
Is Weights & Biases still independent?
Its own site says Weights & Biases is now part of CoreWeave Forge, and the pricing we read is on CoreWeave's page, where the Free plan is $0 a month with up to five model seats and 5GB of storage a month.
Do I need Kubernetes to use an MLOps platform?
Not always, but it depends on the tool. Kubeflow is built for Kubernetes and needs it.
Can data versioning replace a full MLOps platform?
For small projects it can cover a lot. DVC describes a Git-like extension for data science projects with minimal overhead, and its lakeFS products target petabyte-scale object stores.
