The typical searcher runs machine learning on Databricks and finds that the bill is a function of compute units and cloud charges nobody can forecast, or that a lakehouse is more platform than a modelling team asked for. They move to a tool that does one layer of the job, such as tracking, pipelines or AutoML, or to the machine learning service of the cloud they already pay.
Short answer: The first three alternatives to Databricks in our machine learning platform ranking are Valohai, MLflow and Dataiku. 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. Choose Valohai if training is the heavy part and you want a per-user fee in place of metered compute. All 11 alternatives below are ranked products from Best Machine Learning Platforms in 2026, and each says where it differs from Databricks.
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Pricing: Pay as you go per Databricks Unit, price list published; committed-use discounts; trial offered
Established in: United States
Built for: Teams whose data and pipelines already run on the Databricks lakehouse
Not for: Small teams wanting a flat monthly price for tracking only
01
Why buyers look past Databricks
01
A bill measured in units, not in users
Databricks charges per second in Databricks Units, and its pricing page states that storage, networking and related costs come from your cloud provider on top. A team that experiments in bursts cannot read next month's invoice from the price list. Per-user and per-seat tools move that uncertainty from the invoice to the headcount.
02
A platform for data, when the job is models
The vendor describes one platform for data, analytics and AI, covering data engineering, warehousing, databases and governance. A group that only needs experiment tracking, a registry or training pipelines is adopting a data estate to get them, and the exit cost grows with every pipeline built there.
03
Trial credits that run out into a subscription
The pricing page says a trial ends when its credits are exhausted and that, afterwards, you are automatically subscribed to the plan you used. Teams that wanted a cheap evaluation read this clause as a reason to compare tools that charge flat per user or that run on their own hardware.
No charge for the service itself; compute and linked Azure services billed at published rates
Azure service with no service fee, only underlying compute and services billed
your organisation is standardised on Azure and wants machine learning inside that tenant
#11
03
What each one does differently
1
Valohai
Finland · Per user licence, quoted per organisation; 14-day trial
Charges a licence per user and says the price does not follow the scale of work on the platform, where Databricks bills by unit. It runs on virtual machines, Kubernetes or Slurm, and deploys as managed cloud, on-premises or air-gapped. It has no data warehouse or SQL layer, and the pricing page shows no amounts.
Switch from Databricks if training is the heavy part and you want a per-user fee in place of metered compute.
Not stated by the vendor · Open source under Apache-2.0, self-hosted; managed versions are priced by their hosts
The open source project that Databricks offers as a managed service, so leaving the platform can keep the tracking and registry layer unchanged. The self-hosting docs describe a server, a database and an artifact store you run yourself. It trains nothing and has no vendor to call for support.
Switch from Databricks if you have platform engineers and want tracking and a registry with no vendor attached.
France / United States · 14-day cloud trial; paid plans arranged with sales
Puts visual machine learning, AutoML and code in one project flow for mixed teams of analysts and data scientists, and connects to Snowflake and Databricks as data sources. Its 14-day trial runs in a managed workspace for up to five users, and a self-installed Free Edition exists. Paid prices are not published.
Switch from Databricks if analysts and data scientists must share one governed workspace.
United States · Quoted per organisation; Cloud, Premium and Enterprise tiers
Sells a platform for running many practitioners on shared infrastructure, as a managed single-tenant service or self-managed in your own VPC or on-premises. It is not a data platform and the site offers an on-demand demo, not a trial. Buying is by quote, so no list price is available to compare.
Switch from Databricks if a large regulated team needs governed compute in its own cloud account.
United States · Quoted per organisation; open source H2O-3 core; evaluation agreement for the commercial product
Centres on automated modelling for tabular data through Driverless AI, with AutoDoc documentation and deployment on-premises, air-gapped, in a private cloud or as managed cloud. The current site also presents agentic and generative products. It has no warehouse, and the site routes buyers to a demo request.
Switch from Databricks if automated tabular models with documentation for audit matter more than a data platform.
Switzerland · Open source desktop app; hosted tiers quoted by capacity, no per-seat licensing
Builds workflows visually in a desktop application the site describes as free and open source, with a hosted tier priced by capacity and stated to have no per-seat licensing. It suits analysts more than Spark engineers. Prices for the paid tier are by contact, and it is not a lakehouse.
Switch from Databricks if the people building models are analysts who want a visual tool.
United States · Free, Pro and Enterprise plans published; Pro meant for companies under 50 employees
Does one job, tracking experiments and artifacts, with Free, Pro and Enterprise plans now listed under CoreWeave Forge pricing. Pro starts at $60 per month, allows up to ten model seats and is aimed at teams under 50 employees. It runs no data pipelines and has no warehouse.
Switch from Databricks if you need hosted experiment tracking with a published price for a small team.
United States · Free Hub plus Pro, Team and Enterprise per-user plans published; compute billed hourly
A hub where models, datasets and Spaces live as repositories, with Pro at $9 a month and Team at $20 per user a month on its pricing page. It has no training pipelines or data engineering, so it complements a stack more than it replaces Databricks.
Switch from Databricks if your main need is sharing, versioning and serving open models.
United States · Quoted per organisation; 30-day trial
Sold as an agent and AutoML platform, with a 30-day trial described as full platform access and deployment on-premises, in a virtual private cloud or as software as a service. The pricing page lists no plans or amounts, and it does not offer a warehouse or the open source engines Databricks is built on.
Switch from Databricks if you want automated modelling and agent building from one vendor with a long trial.
United States · Pay as you go per resource with Savings Plans; two-month free tier; rates published
Bills per resource on AWS with no minimum fees, and Savings Plans that the pricing page says cut costs by up to 64%. The free tier covers the first two months only. It binds you to AWS, while Databricks runs on several clouds, and the bill is spread over notebooks, training and inference.
Switch from Databricks if your data and security controls already sit in AWS and you want one cloud invoice.
United States · No charge for the service itself; compute and linked Azure services billed at published rates
Has no fee for the service itself, only for the compute plus Azure Blob Storage, Key Vault, Container Registry and Application Insights it uses. Databricks also runs on Azure, so this is a choice of native service against a partner platform. It needs an Azure subscription.
Switch from Databricks if your organisation is standardised on Azure and wants machine learning inside that tenant.
Databricks remains the right choice when your data and pipelines already run on it. Managed MLflow and model serving then sit next to the data they use, there is one governance layer, and the pay-as-you-go model with committed-use contracts across clouds suits a steady, large workload. Replacing it with a tracker, a pipeline tool and a separate serving stack recreates a set of joins the platform already gives you, and a group that lives in notebooks and SQL gains little from moving.
05
What moving off Databricks involves
01
Separating the model layer from the data layer
Databricks holds tables, jobs and models together. Decide which part you are leaving: only experiment tracking and the registry, or the pipelines and data as well. MLflow is an open source project that Databricks hosts, so the tracking part can often be moved without rewriting, while Spark jobs and notebooks need a new home.
02
Pricing the replacement the same way
A unit bill and a per-user bill are not comparable by list price. Take three months of Databricks usage, split it by workload, and price the same compute on the new tool or the cloud service. Remember that your cloud provider's storage and network charges applied on Databricks too and do not disappear.
06
Questions about replacing Databricks
5 answers
What is the closest alternative to Databricks for machine learning?
For the modelling layer alone, Dataiku and Domino Data Lab are the nearest in scope, since both cover collaborative work and deployment on infrastructure you choose. Neither is a lakehouse, so the data engineering side needs another home.
Can I keep MLflow if I leave Databricks?
MLflow is open source and its documentation describes self-hosting with a tracking server, a backend database and an artifact store. Databricks, AWS SageMaker, Azure Machine Learning and others also offer it managed, so the layer is portable, unlike the surrounding platform.
Which Databricks alternative has a published price?
Hugging Face lists Pro at $9 a month and Team at $20 per user per month, and CoreWeave lists Weights & Biases Pro from $60 a month.
Azure Machine Learning has no service fee but bills compute and linked services. Valohai, Domino, H2O.ai and DataRobot publish no amounts.
Does Databricks have a free option?
Yes. Its site offers a free trial that ends when credits are exhausted and a Free Edition for learning. After a trial you are automatically subscribed to the plan you used, so check the payment settings before relying on it.
Is Databricks cheaper than SageMaker or Azure Machine Learning?
The pages do not allow a general answer. Databricks bills per second in units plus cloud charges, SageMaker bills per resource, and Azure bills compute plus linked services. Price your own workload on each calculator.
—
Sources
Checked 27 October 2026
Databricks pricing: per second pay as you go, committed-use contracts, trial ends when credits are exhausted, automatic subscription, storage and networking billed by the cloud provider, Azure priced by Microsoft.
About Databricks: founded 2013 by the creators of Spark, Delta Lake, MLflow and Unity Catalog; San Francisco headquarters; unified data and AI platform.
Valohai pricing: per-user licence, unlimited projects and experiments, deployment options, VM, Kubernetes and Slurm, no amounts shown.
MLflow self-hosting: fully open source, tracking server, backend and artifact store, managed offers from Databricks, SageMaker, Azure Machine Learning.
Dataiku get started: 14-day managed trial, up to five users, no credit card, self-installed Free Edition.
Dataiku platform: visual ML, AutoML, code, governance, connectors to Snowflake and Databricks.
KNIME pricing: capacity-based quote, no per-seat licensing.
Founder of The Knowledge Engineers and of 2 Steps Ahead, an AI search visibility agency. Editorially responsible for every page on this site; research and drafting are AI-assisted.
Written, edited and fact-checked by Sebastiaan Smits. Last reviewed
27 October 2026. How we review.
