Databricks vs Microsoft Fabric: Which to Use? (2026)

Quick verdict

Short answer

Choose Databricks if data engineering and machine learning are central, you need the same platform on AWS or Google Cloud as well as Azure, or you want fine control over compute types and networking. Choose Microsoft Fabric if your organisation is standardised on Microsoft 365 and Power BI, you want one SaaS capacity that covers ingestion, lakehouse, T-SQL warehouse and BI, and your team prefers low-code tools and T-SQL to managing clusters. They are not mutually exclusive: Microsoft documents mirroring an Azure Databricks Unity Catalog into Fabric so that Power BI and Fabric engines read Databricks tables without copying data.

How we know: This comparison is research-based: architecture, billing models, capacity SKUs, Power BI licensing rules, open format support and the Databricks integration were checked against Microsoft Learn, the Azure pricing page for Fabric, Databricks documentation and the Databricks pricing page in October 2026. We have not run either platform or any benchmark, so nothing here is a measured result.

Databricks is a lakehouse platform that runs on AWS, Microsoft Azure (as Azure Databricks, sold and billed through Microsoft) and Google Cloud. Tables are stored as Delta Lake or Apache Iceberg files in cloud object storage and governed by Unity Catalog; Spark clusters, jobs, Lakeflow pipelines, SQL warehouses and ML services work on them. Compute is billed in Databricks Units (DBUs). See Snowflake vs Databricks for how it compares with a warehouse-first platform.

Microsoft Fabric is delivered as software as a service. Microsoft describes it as an analytics platform with integrated workloads (Data Engineering, Data Factory, Data Science, Real-Time Intelligence, Data Warehouse, Databases, Power BI and Fabric IQ, which is in preview) operating over a shared compute and storage model. All workloads store data in OneLake, a single tenant-wide logical data lake built on Azure Data Lake Storage Gen2, in Delta Lake format. You buy compute as a capacity, and every workload draws on it.

Platform versus SaaS. Databricks gives you a workspace in your chosen cloud and many compute options to configure. Fabric gives you a capacity and a tenant-wide lake with no clusters, resource groups or regions to manage for most tasks; Microsoft notes you do not need an Azure account to use Fabric, although F capacities are bought through Azure.

Side by side

Aspect	Databricks	Microsoft Fabric
Delivery model	Managed platform (PaaS) on AWS, Azure and Google Cloud	SaaS on Microsoft's cloud, inside a Microsoft Entra tenant
Billing unit	DBUs per second by compute type, tier, cloud and region; classic compute VMs billed by the cloud provider	Capacity units (CUs) in F SKUs from F2 to F8192, billed per second (one-minute minimum) or reserved yearly; storage billed separately
Storage	Delta Lake or Iceberg tables in your cloud object storage, governed by Unity Catalog	OneLake (on ADLS Gen2), one per tenant; tables in Delta Lake format; shortcuts to ADLS, Amazon S3, Google Cloud Storage and others
Spark	Core of the platform: all-purpose, job and serverless compute, Lakeflow pipelines	Fabric Data Engineering: starter pools and custom Spark pools sized from the capacity (1 CU = 2 Spark vCores)
SQL warehousing	Databricks SQL: serverless, pro and classic SQL warehouses	Fabric Data Warehouse (T-SQL, multi-table ACID transactions) plus a read-only SQL analytics endpoint on every lakehouse
BI	Built-in dashboards and Genie; Power BI and other BI tools are separate products	Power BI built in, including Direct Lake mode over OneLake tables
Governance	Unity Catalog: privileges, attribute-based policies, row and column filters, lineage	OneLake Catalog plus Microsoft Purview for information protection, DLP and audit
ML and AI	MLflow, Feature Store, Model Serving, AI Gateway, serverless GPU compute	Fabric Data Science with Azure Machine Learning integration; Copilot; Microsoft Foundry integration
Free option	14-day trial; Free Edition for non-commercial use	60-day Fabric trial capacity (F64 equivalent)
Main trade-off	More control and multi-cloud, but more to configure and two bills for classic compute	Simple single capacity and tight Power BI integration, but Microsoft-only and shared capacity can throttle

Key differences

Billing: DBUs versus a shared capacity

Databricks meters each workload in DBUs, billed per second, at a rate that depends on the product and compute type (jobs, all-purpose, SQL classic, SQL pro, SQL serverless and so on), the tier, and the cloud and region. With classic compute, the virtual machines run in your cloud account and are billed by your cloud provider; with serverless, Databricks runs the compute in its own serverless compute plane. Costs therefore scale with what each job uses, and each workload can be sized independently.

Fabric works the other way round. You buy a capacity of a fixed size (F2 has 2 capacity units, F64 has 64, up to F8192), and every Fabric workload in the workspaces assigned to it, from Spark notebooks to warehouse queries to Power BI reports, consumes CU seconds from that pool. Microsoft documents that Azure bills F capacities per second with a one-minute minimum and no commitment, with an optional yearly reservation, and that you can pause and resume a capacity. To absorb spikes, Fabric uses bursting (an operation may temporarily use more than the SKU provides) and smoothing (interactive operations are averaged over at least 5 and up to 64 minutes, background operations over 24 hours). Warehouse operations are classed as background almost entirely.

The trade-off is predictability against isolation. A Fabric capacity gives a fixed bill, but when smoothed usage exceeds the capacity, Fabric throttles in stages: after 10 minutes of future capacity is used, new interactive operations are delayed by 20 seconds; after 60 minutes they are rejected; after 24 hours all new requests are rejected until the usage is paid off. Remedies are to scale up the SKU, pause and resume (which bills the accumulated usage), enable capacity overage billing (documented at three times the normal rate) or split workloads across capacities. In our view, teams moving from Databricks should plan capacity sizing and workload separation early.

Storage: your object storage versus OneLake

In Databricks, managed and external tables live in your own cloud storage accounts and are registered in Unity Catalog. Delta Lake is the native format, and Databricks now documents managed Iceberg tables, read-only foreign Iceberg tables and an Iceberg REST Catalog API as generally available.

In Fabric, every workload reads and writes OneLake, which Microsoft describes as a single tenant-wide store with one namespace, organised into workspaces and items such as lakehouses and warehouses. Warehouse tables are Delta tables too, so Spark and T-SQL can work on the same data. Shortcuts give zero-copy access to data in Azure Data Lake Storage, Amazon S3, Google Cloud Storage and other OneLake locations. OneLake also virtualises table formats: Delta tables get Iceberg metadata so Iceberg readers can use them, and shortcuts to Iceberg tables appear as Delta tables, with documented limitations (Parquet only, Iceberg V2 output, a 5,000-commit limit and some unsupported types and partition transforms).

Workloads and skills: Spark first versus T-SQL and low-code

Databricks is organised around notebooks, jobs and Spark, with SQL warehouses for analysts. It suits teams of data engineers and data scientists who write Python, Scala and SQL and want control over runtimes, cluster policies and networking.

Fabric spreads the same tasks across role-based workloads. Data Engineering provides Spark with starter pools (Microsoft documents a typical start of 5 to 10 seconds when prewarmed capacity is available) and custom pools whose size is bounded by the capacity: one CU equals two Spark vCores, and with the documented 3x burst factor an F64 allows up to 384 Spark vCores. Data Warehouse is primarily developed with T-SQL and supports multi-table ACID transactions, stored procedures and materialized views; every lakehouse also gets a read-only SQL analytics endpoint. Data Factory adds pipelines and Power Query dataflows with more than 200 connectors. In our view, Fabric is easier to adopt for SQL Server and Power BI teams, while Databricks offers more depth for engineering-heavy teams.

-- T-SQL (Fabric Data Warehouse): multi-table transaction on Delta tables
BEGIN TRANSACTION;
INSERT INTO dbo.fact_sales (sale_id, product_id, amount)
SELECT sale_id, product_id, amount FROM dbo.stage_sales;
DELETE FROM dbo.stage_sales;
COMMIT TRANSACTION;

Power BI integration and licensing

Power BI is a Fabric workload, so reports can query lakehouse and warehouse tables in OneLake in Direct Lake mode, as well as through import or DirectQuery. Licensing has one rule worth knowing before sizing: on F64 and larger capacities, users with only a free Fabric licence and a viewer role can view Power BI content; on capacities smaller than F64, every viewer needs a Power BI Pro, Premium Per User or individual trial licence. Creating and sharing Power BI items outside My workspace needs Pro or PPU regardless. Microsoft also states that it is retiring Power BI Premium per-capacity P SKUs and recommends F SKUs instead.

With Databricks, Power BI is a separate product outside the platform; Databricks has its own dashboards and Genie for natural-language questions. If Power BI is your organisation's reporting standard, Fabric removes a hop; if you use several BI tools or clouds, that matters less.

Using Azure Databricks and Fabric together

Microsoft documents a mirrored Azure Databricks catalog in Fabric. It mirrors only the Unity Catalog structure; Microsoft states there is no data movement or replication, and the underlying tables are accessed through OneLake shortcuts. Fabric creates a mirrored Azure Databricks item and a SQL analytics endpoint, so you can query the tables with read-only T-SQL and build Power BI reports in Direct Lake mode. Schema and table additions and deletions can sync automatically. Microsoft lists limitations: materialized views and streaming tables are not shown, external tables that are not in Delta format are not shown, and changes can take from a few seconds to several minutes to appear.

A common pattern this enables, in our assessment, is engineering and ML in Azure Databricks with Unity Catalog as the source of truth, and Power BI consumption through Fabric. Fabric mirroring also supports other sources, including Snowflake and Azure SQL Database, if you have a mixed estate.

Pricing and licensing

Microsoft Fabric. Capacity is bought as F SKUs (F2, F4, F8 and so on up to F8192, sized in capacity units) through Azure, pay-as-you-go per second with a one-minute minimum, or as a yearly reservation, which the Azure pricing page describes as about 41% cheaper than pay-as-you-go. OneLake storage (hot, cool and cold tiers), SQL database storage and OneLake cache are billed per GB per month on top. Prices vary by region and currency, and the Azure pricing page shows them only after you choose a region, so we do not quote a figure here; use the Azure pricing calculator or Microsoft's Fabric capacity estimator. Power BI Pro or PPU licences may also be needed (see the F64 rule above). A free 60-day Fabric trial provides an F64-equivalent capacity.

Databricks. Compute is billed in DBUs per second, pay-as-you-go or through committed-use contracts, at rates that vary by product and compute type, tier, cloud and region; the per-DBU rates on databricks.com load dynamically, so we could not read and quote one reliably. For classic compute, add your cloud provider's VM, storage and networking charges. On Azure, Azure Databricks is billed through your Azure subscription; Microsoft Learn states that Standard tier workspaces reach end of life on 1 October 2026 and remaining ones are upgraded to Premium, so budget on Premium rates. Databricks lists a 14-day free trial, and Free Edition is available for non-commercial use only.

Pricing checked on the vendors' official pages on 7 October 2026. Prices change; confirm before buying.

Where each one leads

Databricks strengths

Runs on AWS, Azure and Google Cloud with the same Unity Catalog governance model
Deep data engineering and ML tooling: Spark runtimes, Lakeflow, MLflow, Model Serving, serverless GPU compute
Each workload sized and billed separately, so one job does not throttle another
Delta Lake and Iceberg tables in your own storage, with an Iceberg REST Catalog API
Classic and pro compute can run inside your own cloud network

Microsoft Fabric strengths

One SaaS capacity covers ingestion, Spark, T-SQL warehouse, real-time analytics and Power BI
Power BI built in, with Direct Lake reports over OneLake tables
T-SQL warehouse with multi-table transactions suits SQL Server teams
OneLake shortcuts and mirroring bring in data from ADLS, S3, Google Cloud Storage, Azure Databricks and Snowflake without copying
Predictable capacity bill, with pause and resume and yearly reservations

Limitations

Databricks limitations

More configuration: compute types, cluster policies, runtimes and tiers to manage
For classic compute, costs are split between the Databricks and cloud provider bills
Power BI is an external tool rather than a built-in workload
Free Edition cannot be used commercially

Microsoft Fabric limitations

Microsoft cloud only; no deployment on AWS or Google Cloud
All workloads share the capacity, so heavy jobs can trigger throttling of interactive work
Viewers need Pro or PPU licences unless the capacity is F64 or larger
Iceberg support is through metadata virtualisation, with documented limits on versions, types and partitioning
Fewer low-level controls over Spark clusters and networking than a dedicated Spark platform

When to choose each

Choose Databricks if

Data engineering, streaming or machine learning is the core of your data work
You need the platform on AWS or Google Cloud, or across more than one cloud
You want per-workload compute isolation and fine control over clusters and networking
Your team works mainly in Python and Spark notebooks

Choose Microsoft Fabric if

Power BI is your reporting standard and you want lakehouse, warehouse and BI in one place
Your team is stronger in T-SQL, Power Query and low-code pipelines than in Spark
You prefer one capacity bill to metering individual workloads
You are already licensed for Power BI Premium capacity, which Microsoft is moving to F SKUs
You want to keep Azure Databricks for engineering but serve BI from Fabric through mirroring

When neither is right

Your workload is mainly SQL analytics and you want a fully managed warehouse on any cloud: see Snowflake vs Databricks and Snowflake vs Azure Synapse.
You are on Google Cloud: compare Databricks vs BigQuery.
Your data fits comfortably in one relational database and you mostly need reports: see Data warehouse vs database and Azure SQL Database vs SQL Server.

Final recommendation

Bottom line

For an organisation built on Microsoft 365 and Power BI, with SQL and BI skills rather than Spark engineers, Microsoft Fabric is the simpler choice: one capacity, one lake, and Power BI built in, as long as you size capacities to avoid throttling and account for the F64 licensing threshold. For engineering- and ML-heavy teams, or anyone who needs more than one cloud, Databricks offers more control and depth. Many Azure customers will not have to choose outright: Microsoft documents mirroring an Azure Databricks Unity Catalog into Fabric with no data copy, so Databricks can remain the engineering platform while Fabric and Power BI serve the business.

Frequently asked questions

Is Microsoft Fabric built on Databricks?

No. Fabric is Microsoft's own SaaS platform with its own Spark, warehouse and Power BI engines over OneLake. Both use Delta Lake as the table format, and Microsoft documents mirroring an Azure Databricks Unity Catalog into Fabric, but they are separate products.

Can I use Azure Databricks and Microsoft Fabric together?

Yes. A mirrored Azure Databricks catalog in Fabric mirrors the Unity Catalog structure and reads the tables through OneLake shortcuts, with no data movement. Fabric creates a read-only SQL analytics endpoint and you can build Power BI Direct Lake reports on the tables. Materialized views, streaming tables and non-Delta external tables are not shown.

How is Microsoft Fabric priced?

You buy a capacity (F2 to F8192, measured in capacity units) through Azure, billed per second with a one-minute minimum or reserved yearly, and pay for OneLake storage separately. All workloads assigned to the capacity share it. Power BI viewers need Pro or PPU licences unless the capacity is F64 or larger.

What happens when a Fabric capacity is overloaded?

Fabric first smooths usage and allows 10 minutes of future capacity without throttling. Beyond that, new interactive operations are delayed by 20 seconds, then rejected after 60 minutes of future capacity is used, and all new requests are rejected after 24 hours, until the overage is paid off. You can scale up, pause and resume, or enable overage billing.

Does Fabric support Apache Iceberg?

Through OneLake table format virtualisation: shortcuts to Iceberg tables appear as Delta tables in Fabric, and Delta tables get Iceberg metadata for Iceberg readers. Microsoft documents limitations, including Parquet data files only, Iceberg V2 output and some unsupported types and partition transforms.

Sources

Checked October 2026.

How we research comparisons: our editorial method.