Microsoft Fabric vs Snowflake vs Databricks - Which Data Platform

May 10, 2026•12 min read•Michael Ridland

Every couple of months a client asks me which data platform they should pick. The conversation usually starts with one of three openings: "Our Microsoft rep says Fabric does everything we need", or "Our consultancy is pushing Databricks for AI", or "We had a free Snowflake POC and the numbers look good". All three platforms are real options. None of them is the right answer for every business. The wrong choice will cost you somewhere between AUD $200,000 and a few million dollars over three years.

I run Team 400. We work with Australian businesses on Microsoft AI, data platforms, and Power BI delivery. We're a Microsoft Fabric shop by default, but I've sat on the other side of enough Snowflake and Databricks builds to have strong opinions on when each one is the right fit and when it isn't. This post is for the person who's been asked to make a recommendation and wants the honest version of the trade-offs, with prices, and without the vendor pitch.

The Three Platforms In One Paragraph Each

Microsoft Fabric is Microsoft's all-in-one data platform. Data engineering (Spark notebooks), data warehousing (T-SQL), real-time analytics, data science, and Power BI all in one workspace, billed on a single capacity. Released in late 2023, mature enough to bet on in 2026, still has rough edges in places. Pricing is consumption-based via F-SKUs, denominated in capacity units. Strong if you're already in the Microsoft stack and using Power BI heavily.

Snowflake is a cloud data warehouse that grew up. Separated storage and compute, sold itself on simplicity, then expanded into data engineering (Snowpark), data sharing, applications (Streamlit), and AI (Cortex). Pricing is consumption-based, charged per second of compute time. Strong if you have multi-cloud requirements, large data sharing use cases, or want a database-first analytics platform.

Databricks started as a Spark-as-a-service company and evolved into a unified analytics and AI platform. Lakehouse architecture, Delta Lake storage, MLflow for ML, Mosaic for AI, and now Genie for natural language analytics. Pricing is DBU-based (Databricks Units), charged per compute hour. Strong if you have serious data science and ML workloads, or you have a code-first data engineering team.

The Question Microsoft, Snowflake, and Databricks Salespeople Avoid

Which platform fits the team you actually have, not the team you might hire in two years?

This is the question that gets buried under feature comparisons and TCO spreadsheets. Every platform can run a modern data stack. The differences are not really about capability anymore. They're about which platform's defaults align with your team's skills, your existing tools, and your decision-making style.

A SQL-heavy team running Power BI all day will move five times faster on Fabric than on Databricks. A Python and Spark team doing real ML will fly on Databricks and struggle on Fabric. A polyglot data team that values simplicity above all else will love Snowflake and find both Fabric and Databricks too busy.

We have one client who switched from Databricks to Fabric because they couldn't keep enough Spark engineers and their use case was 90% reporting. We have another who tried Fabric, found it too constraining for their data science team, and moved core ML workloads to Databricks while keeping Power BI on Fabric. Both decisions were correct for their context.

What Each Platform Actually Costs In Australia

Let me give you real numbers, with the caveat that all three vendors discount heavily and your committed spend will be different from your pay-as-you-go rate.

Microsoft Fabric (Australia East, May 2026)

F2 (smallest): roughly AUD $400/month
F8: around AUD $1,700/month
F32: roughly AUD $6,700/month
F64 (Power BI Premium equivalent): around AUD $13,500/month
F256: roughly AUD $54,000/month

A typical mid-sized Australian organisation with 50-200 Power BI users and moderate data volumes runs comfortably on F32 to F64. Add OneLake storage (AUD $0.04/GB/month), egress, and any AI features. Fabric pricing is more predictable than the alternatives because you pay for capacity, not per-query consumption.

Snowflake (Sydney region, May 2026)

Snowflake bills per credit, and credits convert to AUD roughly as follows depending on edition:

Standard: about AUD $4.50 per credit
Enterprise: about AUD $6.75 per credit
Business Critical: about AUD $9.00 per credit

A small warehouse running 4 hours/day at one credit/hour is around AUD $810/month on Enterprise. A larger production workload often lands at AUD $15,000 to AUD $60,000/month. Storage is around AUD $35/TB/month. Snowflake's bill scales surprisingly with usage patterns, and a poorly tuned query can cost real money before someone notices.

Databricks (Australia East, May 2026)

DBU pricing varies by compute type and workload:

Jobs Compute: around AUD $0.22/DBU
All-Purpose Compute: around AUD $0.85/DBU
SQL Pro: around AUD $0.85/DBU
Photon-enabled: 2x DBU consumption but much faster

On top of DBUs you pay Azure or AWS infrastructure costs for the underlying VMs. A typical mid-market Databricks bill runs AUD $8,000 to AUD $50,000/month, more if you're doing heavy ML or running many always-on SQL endpoints.

Side-By-Side Comparison

Aspect	Microsoft Fabric	Snowflake	Databricks
Best for	Microsoft-aligned organisations, Power BI users	Multi-cloud SQL analytics, data sharing	Code-first data engineering, ML/AI workloads
Primary skill	T-SQL, DAX, Power BI	SQL, Snowpark Python	PySpark, SQL, MLflow
Pricing model	Capacity-based (predictable)	Per-second consumption (variable)	DBU + infrastructure (variable)
Mid-market AU monthly	AUD $6,000-$25,000	AUD $10,000-$40,000	AUD $12,000-$50,000
Multi-cloud	No (Azure only)	Yes (AWS, Azure, GCP)	Yes (AWS, Azure, GCP)
BI integration	Native Power BI	Connectors for everything	Strong Power BI/Tableau support
AI/ML maturity	Improving	Growing (Cortex)	Strong (MLflow, Mosaic)
Data sharing	OneLake shortcuts	Industry-leading	Delta Sharing
Learning curve	Low if you're MS-stack	Moderate	Steep
Lock-in risk	High (Microsoft ecosystem)	Low (open formats)	Low (open Delta Lake)

When Microsoft Fabric Is The Right Call

Pick Fabric when your organisation is already Microsoft-centric and Power BI is your primary analytics tool.

We delivered a Fabric platform for a financial services client in Sydney last year. About 800 users across compliance, finance, and operations. Data sources included Dynamics 365, a SQL Server data mart, SharePoint metadata, and three external APIs. They were already on Power BI Premium so the F64 license was effectively a sideways move on cost. Build took fourteen weeks, came in at around AUD $260,000, and replaced an aging combination of Azure SQL DW, ADF pipelines, and a Databricks workspace that nobody really owned.

The reasons Fabric won there were specific. Their team had no Spark experience and no appetite to hire. They wanted T-SQL and DAX, not PySpark. They valued the predictable capacity pricing because their CFO wouldn't sign off on consumption pricing again after a previous AWS bill blow-out. And the integrated Power BI experience saved them from building yet another semantic layer.

Where Fabric wins:

Heavy Power BI usage (more than 100 users with frequent self-service)
Teams whose primary skills are T-SQL and DAX
Organisations that value predictable monthly bills
Existing Microsoft 365 and Azure stacks
Mid-market businesses where simplicity matters more than maximum power
Real-time analytics needs that pair with Power BI dashboards

Where Fabric loses:

Multi-cloud requirements
Serious data science and MLOps work
Teams that need to run on AWS or GCP for compliance reasons
Edge cases where you need raw Spark capability not yet wrapped in Fabric
Organisations with strong cultural anti-Microsoft sentiment (which is real and worth respecting)

For more on what a Fabric engagement looks like, see our Microsoft Fabric consultants page.

When Snowflake Is The Right Call

Pick Snowflake when you want a SQL-first data platform that can run anywhere and you have a use case around data sharing or external data products.

A retail client (multi-state, large product catalogue, several thousand suppliers) needed a data platform that allowed external suppliers to access their own sales data securely. Snowflake's data sharing made this almost trivial - share a secure view with another Snowflake account, the supplier sees the data in their own tools, no exports, no SFTP, no security gymnastics. The same use case in Fabric or Databricks involves more plumbing.

Snowflake is also the right call when:

You have a multi-cloud mandate (legitimate ones exist, mostly for sovereignty or vendor risk)
You want the cleanest SQL experience available
Data sharing with external parties is a core capability you need
Your team values vendor neutrality
You have variable, spiky workloads where pay-per-second pricing helps

Where Snowflake loses:

Cost predictability (we've seen monthly bills double without any clear cause)
Tight Power BI integration (it works, but Fabric is one step closer)
Heavy machine learning workloads (Cortex is improving, but it's not Databricks)
Australian sovereignty edge cases (data residency is fine, but data sovereignty as a legal posture is more complex with US-headquartered vendors)

The Snowflake honeymoon ends about month nine when the consumption bill becomes its own headache. Plan for FinOps from day one if you go this way.

When Databricks Is The Right Call

Pick Databricks when you have a code-first data engineering team and serious AI or ML ambitions.

A mining client in WA needed sensor data from hundreds of pieces of equipment processed in near-real-time, with anomaly detection driving predictive maintenance. The data volumes were enormous (multi-TB per day) and the ML models were domain-specific and needed proper MLOps. Databricks was the natural fit. Build took six months, the team was three data engineers and one ML engineer working alongside our specialists, and the platform now ingests, processes, and analyses 30+ TB/month with model retraining pipelines that run weekly.

The reasons Databricks won there were specific too. The client had existing PySpark expertise. The data volumes justified the operational complexity. The ML use case was real, not aspirational. And they needed to run some workloads on AWS for unrelated reasons.

Where Databricks wins:

Heavy data engineering with code-first teams
Real ML and AI workloads, not just dashboards
Multi-TB daily data volumes
Streaming data at scale
Multi-cloud deployments
Open source preferences (Delta Lake, Apache Iceberg)

Where Databricks loses:

BI-heavy organisations (you'll still want another tool for self-service)
Teams without strong Python skills
Mid-market organisations who'd be better served by something simpler
Cost predictability (notoriously hard to forecast without strong governance)
Speed to first dashboard (slower than Fabric or Snowflake for simple cases)

The Migration Path Most People Miss

Here's something rarely discussed in vendor comparisons. The migration risk between these platforms is not symmetric.

Moving from Fabric to Snowflake or Databricks is hard because you have to rebuild your Power BI semantic layer, your dataflows, and often your reports.

Moving from Snowflake to Databricks is the easiest because both store data in open formats (Snowflake's Iceberg tables, Databricks' Delta Lake) and the SQL dialects are close.

Moving from Databricks to Snowflake is moderate, harder if you have ML pipelines.

Moving anything to Fabric is moderate to hard depending on how you've used it. Microsoft has invested in shortcuts that let Fabric read Delta Lake and Iceberg tables from external storage without copying data, which has made some migrations a lot easier than they used to be.

This asymmetry matters. If you're not sure, picking a platform that's easier to leave is itself a hedge. Snowflake and Databricks both win on this front compared to Fabric.

Decision Framework We Use With Clients

Walk through these in order. The first clear answer usually points you somewhere.

Are 80%+ of your data users in Power BI today? Strong signal for Fabric.
Do you have a hard multi-cloud requirement (compliance, contracts, risk)? Rules out Fabric.
Is data sharing with external parties a key capability you need? Strong signal for Snowflake.
Do you have serious ML or AI workloads with real business value? Strong signal for Databricks.
Is your team primarily T-SQL/Python notebook users versus production PySpark engineers? Notebook users do better on Fabric or Snowflake.
What does your finance team think about consumption pricing versus predictable capacity? If they want predictability, Fabric.
Who is going to operate this 12 months from now? Pick the platform whose skills you can hire or train into your existing team.

If your answers don't converge, you probably have a hybrid platform requirement. That's fine, but understand that operating two platforms costs more than operating one, and you'll need clear rules about what lives where.

Common Objections And Misconceptions

"Fabric isn't mature enough."

It was true in 2023. It's largely not true now. Fabric in 2026 has handled production workloads at scale across many of our clients. There are still rough edges, especially around capacity management and some less-used features, but for mainstream analytics and data engineering it's a credible choice.

"Snowflake is too expensive."

It can be. It also has the cleanest cost optimisation patterns of the three. A well-governed Snowflake environment with auto-suspend, right-sized warehouses, and good query practices is often cheaper than a poorly governed Fabric capacity. The expense problem is usually a governance problem.

"Databricks requires too many specialists."

Yes, this is mostly true. If you can't hire or train PySpark engineers, Databricks will be a struggle. SQL Warehouses make some of this easier but you'll still need real Spark expertise for the data engineering layer.

"We can run all three."

You can, technically. We've seen this work for very large organisations with strong platform teams. For everyone else, running multiple data platforms in parallel multiplies the operational complexity by more than two. Strong opinion, picks fights, but I'd encourage strategic clarity over platform diversification.

What This Looks Like With Real Help

We've done all three platforms across mining, financial services, healthcare, manufacturing, and government. Most of our work is Fabric because most Australian mid-market businesses are already on Microsoft. We're honest with clients when Snowflake or Databricks is the better fit, even though we'd usually prefer to deliver on Fabric.

If you're stuck between options or you've been quoted wildly different numbers for similar work, get in touch. Our Power BI consultants page covers how we approach analytics delivery, and our services page has more on engagement structures.

The honest truth on data platforms in 2026: all three are good. The right one for you depends on your team, your existing stack, and your actual use cases. Pick the one that fits, not the one with the loudest sales rep.