Databricks Free Edition vs Free Trial: Which One Should You Choose?
Learning Databricks, building a portfolio project, or testing it for your company? The two no-cost options are not interchangeable. This guide covers the usage limits, the 14-day credit window, the billing traps on each cloud, the data rules, and a simple way to choose.
Short version: Free Edition is for personal learning and non-commercial experiments. The free trial is for evaluating Databricks for business use, with credits that last 14 days. Pick the wrong one and you can hit a limit you did not expect, put company data under terms that do not suit it, or get a cloud bill you did not plan for.
Databricks Free Edition
A serverless, quota-limited account for students, educators, hobbyists, and anyone learning or experimenting with data and AI.
Best for: coursework, practice, tutorials, portfolio projects, and personal prototypes with non-sensitive data.
Databricks free trial
Free usage credits for organizations and professionals who want to evaluate the full platform for business use.
Best for: proofs of concept, commercial evaluation, and checking a workload before you pay.
1. The short answer
Do not choose on the word “free”. What matters is who the account is for, what it can do, how it bills, and what happens when the free period ends.
Learning goes to Free Edition. A company proof of concept goes to the free trial. Production goes to a paid plan.
Free Edition does not expire after two weeks. It is a continuing offer with daily usage limits. The trial is temporary: its credits are valid for 14 days from the day it starts. Databricks’ comparison page says “up to $400” in credits, so confirm the amount in your own account under Manage trial and do not assume you get the maximum.
2. Side-by-side comparison
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This table follows Databricks’ own comparison page, updated on 27 October 2026, plus the Free Edition limits page. Trial details vary by cloud, signup route, and email type, so treat it as a decision aid and read the terms you see at signup.
| Area | Free Edition | Free trial |
|---|---|---|
| Purpose | Learning, training, and other non-commercial use | Business evaluation, designed for commercial use |
| Duration | “Forever free” with daily limits on usage | Free usage credits valid for 14 days after the trial begins |
| Credits | No credit balance. Fair-use quotas instead. | Up to $400 in credits, according to the AWS comparison page |
| Your data | Use exploratory datasets. Databricks reserves the right to train on your data. | You can securely connect proprietary data for proofs of concept |
| Sign up with a personal email address | A work email address is recommended for the full trial benefits | |
| Workspaces | One serverless workspace and one metastore per account | A production account where you can add more workspaces |
| Compute | Serverless only. No classic compute. Limited size and usage. | Serverless to start. Classic workspaces in your own cloud account depend on the route and on upgrading. |
| SQL warehouse | One warehouse, up to 2X-Small | Personal-email trials: one warehouse scaling to 50 DBUs an hour |
| Jobs and pipelines | Five concurrent job tasks per account. One active pipeline per pipeline type. | Personal-email trials: serverless notebooks, jobs, and pipelines scale to 50 DBUs an hour |
| Languages | Python and SQL. No R or Scala. | Full platform, with the trial limits that apply to your account |
| Admin and security | No account console, account-level APIs, SSO, SCIM, compliance enforcement, or private networking | Upgrade to add enhanced compliance controls |
| Support and SLA | None. No guaranteed reliability. | Databricks lists support and SLAs among the production-ready features that suit the trial route. Check what your plan includes. |
| Paying later | No payment needed | Credit card, marketplace, or a commit contract |
3. What Free Edition includes
Free Edition is a real serverless environment, so you can build a project you are proud of as long as you work within its limits. Databricks describes it as covering data engineering, analytics, machine learning, and AI.
Notebooks and Spark
Load, inspect, clean, and transform sample data with Python and PySpark in notebooks.
SQL and dashboards
Explore data in the SQL editor with the one small SQL warehouse, and build dashboards.
Lakeflow
Practise ingestion, pipelines, and job orchestration inside the concurrency and pipeline limits.
Unity Catalog basics
Work with catalogs, schemas, tables, and volumes in your own single workspace.
AI and Genie
Try supported AI features and prototypes, within the endpoint limits listed below.
Lakebase and Apps
Build a small database or app prototype, inside the per-account caps.
Watch: a learning roadmap from Databricks
This short video, published on Databricks’ own channel, lays out a path through Academy courses, Free Edition, documentation, and community help. It is good for orientation. For exact limits and billing rules, use the documentation linked in this guide.
Learn Databricks: 4-Step Roadmap and Free Resources
Useful for deciding where Free Edition fits among the free learning resources.
Databricks Academy offers a free course on Free Edition that covers a workspace tour, notebooks, Unity Catalog objects, file uploads, the SQL editor, dashboards, and ingestion. Open the workshop.
The video teaches how to start learning. It does not compare Free Edition with a commercial trial or explain billing. For those, use the signup and cloud pages in the sources section.
Free Edition replaced the older Community Edition. If a tutorial tells you to create a Community Edition account, it is out of date, so use the Free Edition signup instead.
4. Free Edition limits
The easiest way to understand a limit is to picture how it shows up. A notebook stops because you used up your quota. A tutorial asks for a custom cluster, a Scala notebook, or a second SQL warehouse, and you cannot do it. These limits come from Databricks’ limitations page, updated on 18 October 2026.
Compute, SQL, jobs, and pipelines
| Resource | Limit | What it means in practice |
|---|---|---|
| Compute type | Serverless only. Custom compute configurations are not supported. | You cannot copy a classic cluster setup from an enterprise tutorial. |
| Serverless for notebooks | Limited compute size and usage | Keep exercises small. Avoid long runs and repeated triggers. |
| SQL warehouse | One warehouse, up to 2X-Small | No parallel warehouses and no large concurrency tests. |
| Jobs | At most 5 concurrent job tasks per account | Build a small demo workflow, not a scheduler stress test. |
| Lakeflow pipelines | One active pipeline per pipeline type | Run one pipeline lab at a time. |
| Databricks Apps | Up to 3 apps per account. An app runs up to 24 hours after it is started, updated, or redeployed, then stops. You can restart it. | Not an always-on service. |
| AI Search | One endpoint with one search unit. Direct Vector Access is not supported. | Fine for a small experiment, useless for a throughput test. |
| Model serving | Limited active endpoints. No GPU serving endpoints, provisioned throughput, custom models on GPU, or batch inference. Some models are unavailable. | Some model-serving labs need another environment. |
| Lakebase | One project per account, with scale-to-zero compute | Good for one small learning project. |
Not supported
- R and Scala
- Custom workspace storage locations
- Online tables and clean rooms
- All legacy Databricks features
- Knowledge Assistant
Account and admin limits
- One workspace and one metastore per account.
- No account console and no account-level APIs.
- No compliance enforcement, security customisation, or private networking.
- Sign-in is limited to email one-time passcode, Google, or Microsoft. No SSO and no SCIM.
- Outbound internet access is restricted to a limited set of trusted domains. If a notebook cannot download a public file, download it yourself and upload it through the workspace.
- Free Edition accounts cannot become Databricks Marketplace providers.
- Databricks may delete accounts that stay inactive for a long time. Keep your own copies of anything you care about.
LinkedIn verification
Some accounts show a Verify identity button in the top bar. If you verify through LinkedIn, Databricks can raise some limits and give limited access to AI Runtime. Databricks says it does not post for you or read your connections. Verification does not remove all limits, and it may not be offered in your region or for your account type, so do not plan a project around it.
Commercial use and your data
5. How the free trial works
The trial is built for business evaluation. Its credits are valid for 14 days from the start. The “up to $400” figure appears on the AWS comparison page, while the Google Cloud and Azure trial pages say 14 days of free Databricks usage without repeating that amount. Check Manage trial in your workspace to see your real credit, remaining balance, and end date.
Express setup on AWS
Express setup lets you start with only an email address and no cloud account. You get a serverless workspace with always-on serverless compute and default storage.
Open the signup page
Sign up with your email and choose Start trial with express setup. Use a work email for a company evaluation, because personal-email trials have extra limits.
Name the account and set access
Give the account a name and decide whether people with the same email domain can join automatically. That setting gives anyone with your professional domain access, so turn it on only if you mean to.
Choose a location and continue
Pick your location. You land in your first workspace.
Check the balance and write down the end date
Open Manage trial in the top bar, note the credit and the end date, and only then start compute.
Upgrade only if you mean to continue
You can upgrade at any point in the 14 days by adding a payment method or signing a commitment. Remaining trial credits are still available after you upgrade. After you upgrade, you can add workspaces, including classic workspaces with custom compute and storage. Databricks notes some trial limits can continue after an upgrade.
Classic workspaces
A classic workspace puts compute and storage in your own cloud account. You may need one to test networking, cloud integrations, or a design that depends on your own subscription. It also adds a second bill, because trial credits only cover Databricks usage. For a first evaluation, serverless is usually simpler. Add classic infrastructure only when the test needs it and someone owns the cost.
Personal email or work email
Databricks says trial accounts created with a personal email can be limited, and recommends a business email to avoid that. The documented possible limits are:
- One SQL warehouse per workspace, scaling to a maximum of 50 DBUs an hour.
- Serverless compute for notebooks, jobs, and Lakeflow pipelines scaling to a maximum of 50 DBUs an hour.
- CPU only, with no GPUs.
- AI Search limited to one endpoint with one search unit.
- Limited external network access. Databricks suggests downloading a blocked public dataset yourself and uploading it to a Unity Catalog volume.
A work email removes some of these limits, but it does not guarantee every feature. Check what your workspace actually allows.
6. AWS, Azure, and Google Cloud: the trial is not identical
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“The Databricks free trial” covers several account and marketplace routes. Do not apply an AWS instruction to an Azure workspace, and do not assume trial credits pay for your whole cloud bill.
| Route | How you start | Billing point to watch |
|---|---|---|
| AWS express setup | Email signup, serverless workspace, no cloud account needed first | To keep going you add a payment method or sign a commitment. After that you can add classic workspaces. |
| AWS Marketplace | Subscribe through AWS Marketplace. You get a serverless workspace, with a classic workspace as an option. | Charges show up in AWS Billing. When the trial ends or the credits run out, the account switches to pay-as-you-go, billed in your AWS console. |
| Azure Databricks | Create a workspace in the Azure portal and pick the Trial (Premium – 14-Days Free DBUs) pricing tier | The tier gives free Premium DBUs for 14 days. It needs an Azure subscription that is not a Free Trial Subscription. A free account must first move to pay-as-you-go, have its spending limit removed, and get a vCPU quota increase. Watch Azure Cost Management for other resources. |
| Google Cloud Marketplace | Subscribe from the Google Cloud Marketplace with a Google Workspace or Cloud Identity account, a project under an organization, an active billing account, and the right IAM roles | Credits cover Databricks usage only. A classic workspace creates resources such as a GKE cluster, VPC subnets, and Cloud Storage buckets that Google bills you for, during and after the trial. The default serverless workspace creates nothing billable in your project. |
AWS
With an AWS-account route, the trial and the pay-as-you-go charges that follow are managed in AWS billing. If you build a classic workspace, resources deployed in your AWS account are billed by AWS, so delete them as well as cancelling the Databricks plan.
Azure
The free DBUs do not make the rest of the deployment free. Microsoft’s page documents the 14-day free DBU offer and does not describe what happens next, so check the pricing tier on your workspace and your Azure bill after day 14. To create the workspace you need the Contributor or Owner role on the subscription, or a custom role with the permissions Microsoft lists.
Google Cloud
The roles you need are Billing Administrator on the billing account or on the project’s organization, plus the View role on the project tied to that billing account. If your organization policy restricts domain sharing, both Databricks’ customer ID and your own must be on the allowed list. If your company already has a contract subscription, do not follow these steps. Ask your Databricks account team about a Google Marketplace Private Offer.
7. When the trial ends
Read this before you click start. What happens depends on the signup route and on whether you added a payment method.
AWS express, not upgraded
You must add a payment method to keep using Databricks. If you do not upgrade, notebooks, jobs, and dashboards in the workspace are deleted 60 days after the trial ends. Some trial-generated data, such as AI Search indexes, can be deleted as soon as the trial expires.
AWS with a card or Marketplace
If you signed up through AWS Marketplace or added a payment method, the account converts to pay-as-you-go once your credits are used up. Watch the bill, and cancel properly if you do not want to continue.
Google Cloud
Classic workspace resources are billed by Google Cloud during and after the trial. To cancel, delete your workspace first, then cancel the Databricks subscription.
Azure trial tier
The 14-day free DBU period ends. Review the workspace pricing tier and every Azure resource in the subscription. Free DBUs do not cover the infrastructure around them.
The 14-day credit period is documented by Databricks. What happens afterwards is specific to each route, so this is a planning sequence and not a promise that every account behaves the same.
On AWS, Databricks says that to cancel during the trial and avoid charges you must first terminate all compute and remove your card information if you added one. Only then should you cancel the subscription. Cancelling without those steps can still convert the account to pay-as-you-go.
8. Which one should you choose?
Student or learner
You want practice with PySpark, SQL, Delta, Lakeflow, dashboards, or AI on public or made-up data. You do not need commercial use, classic compute, enterprise networking, or a support SLA.
Company evaluation
Your company wants to test Databricks with a work email, approved data, a real workload, and clear security and performance goals before it buys.
Production owner
You need a running platform with support, governance, network controls, capacity, cost control, and a contract that fits production.
Quick decision: what are you trying to do?
Pick the closest match to see where to start.
If you are preparing for a certification
Free Edition is a good place to practise, as long as your practice does not depend on Scala, R, custom clusters, private networking, or account administration. Match each task to the current exam guide. Our Data Engineer Associate guide lists the outline and has a practice bank, and the certification roadmap compares all eight exams.
If your company wants a two-week proof of concept
Use the trial with a work email and the cloud route that matches your design. Define one question to answer, pick representative data, name the billing owner, and set a stop date. Do not use Free Edition for company data or a commercial evaluation.
If you are comparing Databricks with Microsoft Fabric
Free Edition teaches notebooks, Spark, Delta, Unity Catalog, and SQL. A fair business comparison that includes networking, governance, performance, and cost needs a trial or paid environment. Start with our Fabric vs Databricks comparison, and do not base a buying decision on a restricted learning account.
9. Safe setup checklists
Before you create a Free Edition account
- Confirm the work is personal, educational, or otherwise non-commercial.
- Use public or synthetic data. No customer records, company files, secrets, credentials, or regulated data.
- Read the current terms and privacy notice, including the line about Databricks training on your data.
- Plan for serverless only, one SQL warehouse, five concurrent job tasks, and one active pipeline per type.
- Follow Python and SQL tutorials. R and Scala do not work.
- Keep your own copy of notebooks and project files, in case the account sits unused.
Before you start a free trial
- Choose AWS, Azure, or Google Cloud on purpose, and do not pick a marketplace billing route by accident.
- Use a work email for a company evaluation.
- Note the start date, the end date, and the credit shown in Manage trial.
- Decide whether serverless is enough. Use a classic workspace only when the test needs your own cloud resources.
- Name a billing owner and set a budget or cost alert in the cloud provider’s console.
- Write down what you will test, which data is approved, who gets access, and what counts as success.
- Book the cleanup review before the last day, not after the credits are gone.
A 14-day proof-of-concept plan
| Days | Work | Evidence to keep |
|---|---|---|
| 1 to 2 | Access, identity, region, data connection, and billing checks | Architecture sketch, access list, credit baseline |
| 3 to 4 | Ingest one representative source into a small Delta table | Load time, schema handling, volume, failure behaviour |
| 5 to 6 | Build the main transformation or Lakeflow pipeline | Run history, quality checks, retries |
| 7 to 8 | Test SQL analytics with a realistic user workload | Query times, concurrency notes, validated results |
| 9 to 10 | Test permissions, lineage, and data access | Role list, access proof, policy gaps |
| 11 to 12 | Measure usage and estimate production cost | DBU use, cloud costs, assumptions, exclusions |
| 13 | Repeat the critical workflow and list the operational gaps | A repeatable run and the open limits |
| 14 | Present the result and decide: stop, extend on a paid plan, or evaluate further | Decision record, confirmed cleanup, sign-off from the billing owner |
This plan is a suggestion, not a Databricks requirement. Adjust it to your cloud, data size, and approved scope.
10. A learning project for Free Edition
If you are learning on your own, build a small retail analytics project from a public or made-up sales file. The goal is to practise the platform, not to imitate an enterprise deployment that Free Edition cannot support.
- Upload a sales CSV. Outbound internet is restricted, so do not count on downloading a file from inside a notebook. Download it yourself and upload it through the workspace.
- Inspect and clean it in a Python notebook. Check data types, missing values, duplicate order IDs, bad amounts, and date parsing.
- Write a Delta table. Keep it small, then check the schema and the row count.
- Query it with SQL. Work out revenue by date, category, and region, and check the totals against the source rows.
- Build a dashboard. Practise the analytics workflow. Do not try to benchmark concurrency.
- Add one Lakeflow job or pipeline. Keep it small and respect the limits.
- Export your work. Keep the notebook source and the data outside the workspace so you can rebuild the project later.
Databricks’ own DataFrame tutorial tells Free Edition users to use the Python examples and to upload the CSV through the workspace UI, not to download it with a command. See the DataFrame tutorial.
Watch: a longer project, start to finish
After the small lab, this longer walkthrough from the Codebasics channel builds an e-commerce data and AI project. The player is set to start near the Free Edition signup section, and the video’s chapters let you jump around.
Databricks Free Edition tutorial: end-to-end data and AI project
Useful for seeing a complete learning project.
Check the date: this video was made in 2025, and Free Edition limits and screens have changed since. Use it for project structure and concepts, and rely on the limits page for today’s quotas.
11. Avoiding surprise bills
A trial credit is not a promise that everything in your design is free. Split the cost into three layers.
Reconcile Databricks usage with the billing account of the cloud you used.
Use our Databricks Cost Calculator for a planning estimate, and confirm the SKU, cloud, region, pricing tier, DBU rate, compute type, run time, and separately billed resources on the official pricing pages.
The safest pattern is: start with serverless if it can answer your question, keep the workload small, watch Manage trial, set a budget alert with the cloud provider, and remove classic resources as soon as the test is done. Stopping Databricks compute does not delete networking or storage you created for a classic workspace.
12. When you hit a limit
| What you see | Likely cause | What to do |
|---|---|---|
| Compute stops and will not restart today | You passed your Free Edition quota | Wait for the reset. Your data stays. Run smaller jobs next time. |
| A tutorial asks for a cluster or instance type | Free Edition has serverless only | Use serverless compute, or find a serverless version of the tutorial. |
| A Scala or R notebook will not run | Neither language is supported | Use the Python or SQL version. |
| A second SQL warehouse will not create | One warehouse, 2X-Small, is the limit | Reuse the one you have. |
| A second pipeline or app will not start | One active pipeline per type, three apps, and apps that stop after 24 hours | Stop the first one, or restart the app. |
| A notebook cannot download a file | Outbound access is limited to trusted domains | Download it on your computer and upload it to the workspace or a volume. |
| You need Unity Catalog admin features, SSO, or private networking | Free Edition has no account console or enterprise admin | Start a free trial. Databricks says you must create a new account for the full platform. |
| A trial feature is capped at 50 DBUs an hour | A personal-email trial limit | Use a work email on a new trial where your policy allows it, or contact Databricks. |
If you outgrow Free Edition, Databricks says you must create a new account by starting a free trial. You cannot upgrade the Free Edition workspace in place, so keep your notebooks and data exports ready to move.
13. Common mistakes
- Using Free Edition for company work. It is for non-commercial use. Use a trial for a company evaluation.
- Uploading sensitive data to Free Edition. Databricks reserves the right to train on your data. Use public or made-up data.
- Treating “forever free” as unlimited. Free Edition runs on quotas, and exceeding them stops your compute.
- Assuming every trial gets exactly $400. The comparison says up to $400, so check your account.
- Thinking credits cover cloud resources. Credits cover Databricks usage. Classic workspace resources are billed by the cloud provider.
- Ignoring personal-email limits. The trial may cap compute at 50 DBUs an hour, limit AI Search, and block GPUs.
- Forgetting the end date. Export your work and settle the billing route before the credits run out.
- Cancelling in the wrong order. On AWS, terminate compute and remove your card before you cancel.
- Following old Community Edition tutorials. Use Free Edition instructions instead.
- Treating a trial as production. A trial is for evaluation. Settle support, security, and contract terms before you go live.
14. Frequently asked questions
Is Databricks Free Edition free forever?
It is a continuing no-cost offer with no expiry date, but it has daily usage limits and fair-use rules. It has no guaranteed reliability, no support, and no SLA, and Databricks may delete accounts that stay inactive for a long time.
How long is the Databricks free trial?
The free usage credits are valid for 14 days after the trial starts. Open Manage trial in your workspace to see the start date, the remaining credit, and the end date.
Does the free trial give $400?
The AWS comparison page says “up to $400 in credits”. The Google Cloud and Azure trial pages describe 14 days of free Databricks usage and do not repeat that figure, so check the credit in your own account.
Can I use Free Edition for commercial projects?
No. Free Edition accounts may not be used for commercial purposes. Use a free trial or a paid plan for business work, and follow your organization’s data policy.
Can I use Free Edition to prepare for a Databricks exam?
For many Python, SQL, Delta, ingestion, and job tasks, yes. It does not support every feature, language, or compute mode, so match each practice task to the current exam guide and to the Free Edition limits.
Can I use Scala or R in Free Edition?
No. Both are listed as unsupported. Use Python and SQL.
Can I create more than one SQL warehouse in Free Edition?
No. You get one SQL warehouse, up to 2X-Small.
What happens if I go over my Free Edition quota?
Your compute shuts down for the rest of the day, and in extreme cases the rest of the month. Databricks says your data and settings are not deleted, and you can resume when the limit resets.
Will Databricks charge me after the trial?
It depends on the route. On AWS, an account created through Marketplace or with a payment method converts to pay-as-you-go once the credits are used up. Other routes have their own steps. Terminate compute, remove payment details where Databricks tells you to, cancel, and delete any cloud resources you created.
What happens to my notebooks when an express trial ends?
On AWS express setup, if you do not upgrade, notebooks, jobs, and dashboards are deleted 60 days after the trial ends. Some trial data, such as AI Search indexes, can be deleted as soon as the trial expires. Export what you need before then.
Can I convert Free Edition into a free trial?
No, not in place. Databricks says that if you need the full platform you must create a new account by starting a free trial.
Is Databricks Community Edition still available?
Free Edition replaced it. Treat any tutorial that starts with a Community Edition signup as out of date.
Which option is best for a company proof of concept?
The free trial, with a work email and approved data. Choose serverless or classic based on what the test must prove, and confirm the credit, the trial limits, the data handling, and any cloud charges before you load company data.
15. Official sources
These pages were checked on 9 November 2026. Databricks changes signup offers and Free Edition limits, so read the source pages again before you make a billing or security decision.
- Free trial vs Free Edition: purpose, terms, credit offer, and account differences (updated 27 October 2026).
- Free Edition limitations: compute, jobs, pipelines, admin, and account limits (updated 18 October 2026).
- Sign up for Free Edition
- Free trial on AWS: 14-day credits, Manage trial, cancellation, and trial limits (updated 15 October 2026).
- Express setup: signup steps and what happens after the trial (updated 11 September 2026).
- AWS Marketplace trial
- Azure Databricks free trial (Microsoft Learn): the Trial pricing tier and Azure prerequisites (updated 15 October 2026).
- Free trial on Google Cloud: Marketplace requirements, classic workspace charges, and cancellation (updated 15 October 2026).
- DataFrame tutorial: the Python path and upload advice for Free Edition users.
- Databricks Academy: Get Started with Databricks Free Edition
- Databricks Terms of Use and Privacy Notice
