Last updated: October 7, 2026
By ZapAI Team
TL;DR: For new analytics projects in 2026, Microsoft Fabric is the stronger default. Microsoft’s engineering investment, new features, and roadmap are concentrated there. Azure Synapse is not being shut down, but it has settled into maintenance mode. Teams with heavily tuned dedicated SQL pools, an external Hive Metastore, JDBC-dependent Spark jobs, or strict PaaS-level network control still have good reasons to stay on Synapse a while longer.
Choose Microsoft Fabric for new analytics work and Power BI-centric estates. Keep Azure Synapse for tuned dedicated SQL pools or Spark features Fabric lacks, and migrate those workloads on your own timeline.
If you’re the person actually responsible for making this call, not just reading about it, you already know the marketing decks won’t help you. What follows is the architecture, the pricing mechanics, the benchmark data, and the migration reality, so you can decide with your own workloads in mind instead of Microsoft’s.
| Azure Synapse Analytics | Microsoft Fabric | |
|---|---|---|
| Service model | PaaS: you provision and tune each engine | SaaS: one capacity shared by every workload |
| Storage | Separate per engine | One copy in OneLake, Delta Parquet |
| Billing | DWUs, Spark pools, and serverless queries billed separately | F SKU capacity units |
| Real-time analytics | Data Explorer retired October 7, 2025 | Eventstream and Eventhouse |
| Spark runtimes | Up to 3.5 | Up to 4.0 |
| AI and agents | Azure Machine Learning integration | Copilot, data agents, Fabric IQ |
| Roadmap | Supported, maintenance mode | Where new features land |
What Is Azure Synapse Analytics?
Azure Synapse is Microsoft’s PaaS analytics service that combines data integration, data warehousing, and big data analytics in one workspace. You provision and manage each engine yourself.
Synapse launched as Microsoft’s answer to the fragmented state of enterprise analytics: one workspace instead of five disconnected tools. That PaaS distinction matters more than it sounds like it should, because in Synapse you run the individual pieces:
- Dedicated SQL pools. MPP compute for predictable, high-performance queries, sized in Data Warehousing Units (DWUs).
- Serverless SQL pools. On-demand, ad hoc querying without provisioning.
- Apache Spark pools. Compute with a defined node count, used for data engineering and ML prep.
- Synapse pipelines. Built on Azure Data Factory, handling orchestration and data movement.
You’re responsible for sizing, scaling, and tuning each of these independently. That’s not a flaw. For teams that want granular control over cost and performance per workload, it’s the point.
What Is Microsoft Fabric?
Microsoft Fabric is a SaaS analytics platform that unifies data integration, engineering, data science, real-time analytics, warehousing, and Power BI on one shared capacity and one data lake, OneLake.
Fabric takes the opposite bet from Synapse. Everything sits on top of OneLake, a single logical data lake that every workload (Lakehouse, Warehouse, Data Factory, Real-Time Intelligence, Power BI) reads from and writes to. There’s no provisioning of individual pools. You buy one capacity, and every engine draws from the same pool of compute. Microsoft announced Fabric at Build in May 2023 and made it generally available that November, an unusually fast move from preview to GA. For a fuller tour, see what Microsoft Fabric is.
What’s the Real Architectural Difference?
Storage. Synapse keeps data per engine, so it gets copied between them. Fabric stores it once in OneLake, and every engine reads that same copy.
Most comparison posts start with a feature checklist. That’s the wrong place to start, because the feature list changes every quarter. The storage model doesn’t.
In Synapse, dedicated SQL pools, Spark pools, and serverless SQL each work against their own storage patterns, so data movement between engines is common and operationally expensive. Data gets copied several times on its way from raw ingestion to a queryable warehouse layer before a report ever renders.
In Fabric, every workload’s data, including Warehouse tables, is persisted in Delta Lake (Parquet) format in OneLake, so Spark, SQL, Power BI, and even external engines can read it without conversion. That’s not a minor convenience. It’s the difference between “export this table so Power BI can use it” and “Power BI already sees this table.”
OneLake also supports shortcuts, references to data in Amazon S3, ADLS Gen2, or other OneLake locations without physically copying it. If your data is already scattered across storage accounts, that’s a much lower migration bar than rebuilding a warehouse from scratch.
How Do the Compute Models Compare?
Synapse makes you provision and scale each engine. Fabric gives every workload one shared capacity, which is simpler but means heavy jobs compete with each other.
In Synapse, someone on your team decides when to scale a dedicated SQL pool up or down, sizes Spark pools by node count, and watches the hard limits. Per Microsoft’s memory and concurrency limits, a dedicated SQL pool tops out at 128 concurrent queries, with the rest queued. That becomes a real bottleneck during peak reporting windows for organizations with large Power BI audiences.
Fabric replaces this with one capacity model. You buy an F SKU, and every workload, whether Spark, SQL, Power BI, or Data Factory, draws from the same pool of capacity units. There’s no per-engine provisioning decision. The tradeoff is that heavy jobs running at the same time compete for that shared resource, so a Spark job hogging capacity can slow a Power BI refresh in a way that wouldn’t happen with Synapse’s isolated pools.
What Do Microsoft’s Benchmarks Show?
Microsoft’s own tests found Fabric Warehouse 50–90% faster than Synapse dedicated pools at similar price points on a 10 TB dataset, with up to 71% better price per query.
Microsoft’s Fabric team published those results in its guide to mapping Synapse dedicated SQL pools to Fabric Warehouse compute, based on TPC-H-derived tests at 100 GB, 1 TB, and 10 TB. The same guide maps DWU levels to approximate Fabric capacity sizes, which is useful when you’re budgeting a move.
This is Microsoft grading its own newer product, so treat the exact multiples with some skepticism and test your own queries. But dedicated SQL pools do have documented architecture-level capacity limits, including tempdb and concurrency ceilings, and those limits are one reason platform teams with heavy concurrent-query workloads tend to migrate first.
How Does Pricing Compare?
Synapse bills each component separately. Fabric bills one capacity in capacity units, which is simpler to buy but harder to forecast for spiky workloads.
Synapse charges DWUs for dedicated SQL pools, separate consumption for Spark pools, and separate rates for serverless queries. Fabric collapses all of that into one number. Per the Azure Fabric pricing page, capacities run from F2 to F2048, each tier doubling the capacity units, at about $0.18 per capacity-unit hour pay-as-you-go in US regions. That puts F2 at about $263 a month and F64 at about $8,410 a month, with roughly 40% off for a one-year reservation.
The number that matters most for budgeting isn’t the sticker price of any one SKU. It’s that F64 is the pivot point: the smallest capacity where free users can view Power BI content, according to Microsoft’s Fabric licensing documentation. If you already pay for hundreds of Power BI Pro licenses, that alone can offset a large share of the F64 bill. Our Fabric vs Power BI Premium breakdown runs the break-even math, which lands around 350 viewers.
The honest complication is forecasting. Because every workload shares the same capacity, a spike in Spark usage, an unmonitored Copilot rollout, or a burst of report views all draw against the same number, and throttling hits when capacity runs out. Synapse’s per-service billing is more fragmented, but each piece is individually predictable in a way Fabric’s shared pool isn’t, at least until you have a few months of usage data.
How Do Governance and Security Differ?
Synapse offers granular PaaS-level control configured service by service. Fabric centralizes governance through Microsoft Purview on top of one storage layer.
For most organizations, Fabric’s approach is less work. Sensitivity labels, data loss prevention policies, and lineage apply consistently because there’s one storage layer underneath everything, governed through Microsoft Purview.
Strictly regulated environments, financial institutions especially, often rely on the detailed network configurations Synapse provides. Fabric’s private endpoint and network isolation options have expanded steadily, but verify your specific requirements against Fabric’s current documentation rather than assuming parity.
Which Is Better for Real-Time Analytics?
Fabric, clearly. Synapse Data Explorer was retired on October 7, 2025, and Microsoft’s recommended path is Fabric’s Eventhouse.
Fabric’s Real-Time Intelligence is built around Eventstream, for no-code ingestion and routing from sources like Event Hubs, and Eventhouse, KQL-based storage optimized for time-series and event data with native analysis functions. If you still have Synapse Data Explorer workloads, Microsoft’s migration guidance moves them to Eventhouse. This is one of the few places where “migrate to Fabric” isn’t a strategic nudge. It’s close to mandatory.
Where Is Fabric Pulling Ahead on AI?
Conversational data agents and open agent tooling. Synapse’s AI path runs through Azure Machine Learning, which is solid but more traditional.
Fabric data agents reached general availability at FabCon in March 2026. They let teams build conversational Q&A over lakehouses, warehouses, semantic models, and KQL databases, generating SQL, DAX, or KQL and answering under the requesting user’s own permissions.
Microsoft also opened Fabric up beyond its own Copilot. In June 2026 it released Skills for Fabric, an open-source toolkit that lets external AI agents, including GitHub Copilot and Anthropic’s Claude, author and work with Power BI reports, semantic models, and other Fabric assets. Synapse has no comparable agent-native layer. If you’re planning agents on your data estate, our AI agent development team builds on exactly this stack.
Is Azure Synapse Being Retired?
No, not as a whole product. Specific components have been retired, and new development has moved to Fabric, but Synapse SQL, pipelines, and Spark remain supported.
Microsoft has published retirement plans for specific pieces, such as Synapse Data Explorer and the GPU-accelerated Spark pool preview, which was retired in July 2024. It has not announced an end date for Synapse itself, as Microsoft staff confirm on Microsoft Q&A.
But “supported” and “actively developed” aren’t the same thing. Microsoft’s Fabric and Synapse Spark comparison lists Spark 4.0 for Fabric and not for Synapse, and newer Delta capabilities arrive in Fabric first. In practice, Synapse looks like an older Windows Server release: still patched, still running production workloads at scale, but no longer where new features show up.
If a vendor uses “Synapse is dying” as pressure to close a migration deal on a tight timeline, that pressure isn’t backed by a retirement announcement. The real argument for moving is the roadmap gap, not a shutdown clock.
When Should You Choose Microsoft Fabric?
When you’re starting fresh, Power BI is central, or you want less infrastructure to run. Most new projects fit.
- You’re starting a new analytics project with no legacy Synapse investment.
- Power BI is central to your reporting, especially with 350 or more viewers, where F64 licensing economics kick in.
- You want unified governance through Purview without stitching together per-service security settings.
- Real-time analytics on streaming, IoT, or event data is a core requirement.
- Your team wants less overhead from provisioning and tuning separate compute pools.
- You’re investing in conversational AI or agent-based access to your data.
When Should You Stay on Azure Synapse?
When existing workloads are well tuned and depend on features Fabric doesn’t offer yet. Staying is a timing decision, not a permanent one.
- You have heavily tuned dedicated SQL pools with complex distribution strategies that perform well today and would need significant re-engineering.
- Your Spark jobs need an external Hive Metastore or JDBC connections, which Microsoft lists as Synapse-only capabilities.
- You rely on fixed Spark pool node sizing (3 to 200 nodes) for predictable performance.
- Your network or security controls exceed what Fabric currently supports in your region.
- Your Spark workloads are written in .NET for Spark, and rewriting them isn’t on the roadmap.
How Do You Migrate From Synapse to Fabric Without Breaking Production?
Sequence by risk: new work on Fabric first, then pipelines, then Spark notebooks, and dedicated SQL pools last.
Teams that get this wrong usually try to migrate everything at once. A more workable order, based on how the two platforms map to each other:
- Start new workloads on Fabric. Build new data engineering and analytics projects on Fabric from day one. This builds team fluency without touching anything that works today.
- Migrate pipelines. Synapse pipelines and Fabric Data Factory share Azure Data Factory roots, which makes this the lowest-risk starting point.
- Migrate Spark notebooks. PySpark code generally runs with modest changes, mostly around Lakehouse references and mount points.
- Migrate dedicated SQL pools last. This is the heaviest lift. Check your code against Fabric Warehouse’s T-SQL surface area, since some commands and table options differ, and test thoroughly outside production before cutting over.
For the full sequence, including capacity sizing, the Migration Assistant, and parallel validation, read our step-by-step Microsoft Fabric migration guide.
Where Does Fabric Fit Against Databricks and Snowflake?
Fabric suits Microsoft-centric organizations focused on BI and reporting. Databricks wins on large-scale engineering and AI across clouds, and many enterprises run both.
The clearest constraint on Fabric is that it runs on Azure, while Databricks and Snowflake both run across AWS, Azure, and Google Cloud. OneLake shortcuts can read data stored in other clouds, but if multi-cloud compute flexibility matters more to your architecture than deep Microsoft integration, weigh that before you commit. Our post on why organizations are migrating to Fabric covers that tradeoff in more depth.
Wrapping Up
Choosing between these two isn’t really a “which is better” question. It’s a “which matches the shape of your workloads, your team’s risk tolerance, and your existing investment” question. Fabric is the default for anything new. Synapse remains a reasonable home for tuned workloads that depend on what Fabric doesn’t do yet.
If you’re looking at a Synapse environment and wondering whether now is the time, our Microsoft Fabric team can assess your workloads and map a migration order before you commit. Get in touch to start that conversation.
Fabric vs Synapse Questions We Hear Most
Is Microsoft Fabric replacing Azure Synapse?
For new investment, effectively yes. Microsoft’s engineering focus and roadmap are concentrated in Fabric. But there’s no retirement announcement for Synapse as a whole. Specific components like Synapse Data Explorer were retired, while dedicated SQL pools, pipelines, and Spark remain supported.
Can I run Fabric and Synapse together?
Yes, and many organizations do during a phased migration, running new workloads on Fabric while existing Synapse production systems stay in place until each one is migrated.
Is Microsoft Fabric more expensive than Azure Synapse?
It depends on workload shape. Fabric’s shared capacity can be cheaper for concurrent, mixed workloads because you aren’t paying for idle per-service compute, and Microsoft’s own tests showed up to 71% better price per query at 10 TB. But shared-capacity billing is harder to forecast for spiky usage.
Does Fabric support everything Synapse dedicated SQL pools support?
Not everything. Fabric Warehouse’s T-SQL surface area differs from dedicated SQL pools in some commands and table options, so review Microsoft’s documentation and test your code before migrating. Gaps have narrowed. OPENROWSET, for example, became generally available in Fabric Warehouse in 2026.
How long will Azure Synapse be supported?
Microsoft hasn’t published an end-of-support date for Synapse as a whole. Individual components have their own retirement dates, and Spark runtimes follow a published lifecycle, but core SQL, Spark, and pipeline capabilities remain supported while new development shifts to Fabric.