If you run Snowflake and are weighing Microsoft Fabric, the 2026 answer is rarely "rip one out." The real decision is migrate, coexist, or stay — and since Microsoft and Snowflake made OneLake interoperability generally available in February 2026, coexistence is now a first-class option rather than a workaround. Migrate when Power BI and Microsoft 365 are your center of gravity and you want one capacity to govern; coexist when Snowflake is a system of record you cannot or should not move; stay when a disruptive migration buys you nothing a connector cannot already deliver.

This guide is for the CIOs, data platform owners, and IT architects who have to defend that call on cost, governance, and risk — not on a feature demo. It lays out what changed this year, how to choose among the three paths, how Snowflake–Fabric coexistence actually works, what a migration really costs and saves, and the phased sequence that moves you without an outage.

Key takeaways

• The decision is no longer binary. OneLake and Snowflake interoperability went generally available in February 2026, so a single copy of data can serve both platforms through open Apache Iceberg and Delta formats.
• Coexistence is cheaper to start than migration. Fabric does not charge ingress or mirroring compute to replicate Snowflake into OneLake; the recurring cost sits on the Snowflake side for change-data reads.
• Migrate for ecosystem gravity, not for a cheaper base SKU. The savings come from folding Power BI, engineering, and warehousing onto one Fabric capacity — and from Copilot grounding living next to the data in OneLake.
• "Cheaper" depends on workload shape. Steady, Power-BI-heavy Microsoft estates often land cheaper on Fabric's capacity model; spiky, isolated, multi-cloud compute often stays cheaper on Snowflake's per-second credits.
• Some workloads should stay on Snowflake. Deep data sharing, clean rooms, Snowpark at scale, multi-cloud requirements, and dbt-snowflake-heavy pipelines are reasons to coexist rather than migrate.
• Sequence protects you. Buy and validate the Fabric target first, run both in parallel, and cut over only after reconciliation — never cancel the source before the target is proven.

The short answer: migrate, coexist, or stay?

Decide on three inputs in order — where your BI and AI gravity lives, whether Snowflake is a system of record you must keep, and the shape of your compute demand. The platforms converge on features every quarter; the operating model and cost structure are where the real divergence lives.

• Migrate when Power BI is your dominant BI surface, you are Azure- and Microsoft-365-centric, and you want one governed capacity instead of a stitched-together stack.
• Coexist when Snowflake is a system of record, a data-sharing hub, or a multi-cloud warehouse — keep it as the writer and surface its data in Fabric through interoperability.
• Stay when your Snowflake estate is stable, Power BI reach is modest, and a migration would spend budget and risk to replace something a connector already solves.

For the feature-and-architecture version of the choice, our Microsoft Fabric vs Snowflake decision guide is the companion to this one; this guide picks up where that leaves off — at whether and how to move.

What changed in 2026 — and why this decision reopened

For years the Snowflake-or-Fabric question assumed you had to pick one and copy data into it. That assumption broke in 2026. At Snowflake's BUILD conference, Microsoft and Snowflake announced that OneLake and Snowflake interoperability is generally available, letting you natively store Snowflake-managed Iceberg tables in OneLake and automatically expose Fabric data to Snowflake in Iceberg format.

The mechanism is open table formats. Because OneLake automatically translates between Iceberg and Delta Lake metadata, the same Parquet files can be read by Snowflake and by every Fabric engine without rewriting or duplicating data. That single change is why coexistence moved from a brittle pipeline project to a supported pattern — and why the buying decision reopened for thousands of joint customers.

Fabric's own momentum raises the stakes of getting this right: Microsoft reported the Fabric business passing $2 billion in annual recurring revenue with more than 31,000 customers in its fiscal Q2 2026. The platform is maturing fast, which is exactly why a defensible, workload-based decision beats a reaction to either vendor's roadmap.

Migrate, coexist, or stay: how to choose

Use the paths below as a decision block, not a leaderboard. Each is "right" for a specific combination of ecosystem, ownership, and cost shape.

Migrate to Fabric

• Best when: Power BI is the primary BI surface, the estate is Microsoft-anchored, and you want Copilot grounding and Purview governance next to the data.
• Why it wins: One capacity covers engineering, warehousing, real-time, and Power BI, and Direct Lake removes the import-refresh tax on reporting.
• Watch out for: Snowflake-specific features (clean rooms, Snowpark Container Services, deep dbt-snowflake pipelines) that do not have a one-to-one Fabric equivalent.

Coexist (Snowflake writes, Fabric consumes)

• Best when: Snowflake is a system of record or a cross-organization data-sharing hub you cannot move, but Power BI and Microsoft 365 need governed access to that data.
• Why it wins: Interoperability gives you one copy of data across both platforms, so you add Fabric analytics without a migration project or a dual-write tax.
• Watch out for: Clear ownership of the system of record and the storage layer, so two teams do not quietly co-own the same tables without rules.

Stay on Snowflake

• Best when: The Snowflake estate is stable, multi-cloud independence is a hard requirement, and Power BI demand is small enough to serve through DirectQuery or a thin connector.
• Why it wins: You avoid migration cost and risk entirely and keep per-second elasticity that bills to near zero when idle.
• Watch out for: Paying a Microsoft-ecosystem integration tax over time that a coexistence pattern would remove.

How does Snowflake–Fabric coexistence actually work?

There are three practical architectures for surfacing Snowflake data in Fabric, and the right one depends on a single question: which platform is the system of record and primary writer. Everything else — cost, performance, ownership — flows from that answer.

Pattern 1: Fabric Mirroring of Snowflake

• How it works: Fabric continuously replicates Snowflake tables into OneLake as Delta, with an auto-generated SQL analytics endpoint and Direct Lake for Power BI, per Microsoft's mirroring documentation.
• Cost shape: Fabric charges no ingress or mirroring compute into OneLake and mirrored storage is free within your capacity limit; Snowflake-side compute is billed for the change-data reads.
• Best for: A clean platform boundary where Snowflake stays the system of record and Fabric consumes a managed, self-healing replica.

Pattern 2: Snowflake-managed Iceberg plus a OneLake shortcut

• How it works: Snowflake writes Iceberg tables to external storage and Fabric reads them in place through a OneLake shortcut — zero data copied.
• Cost shape: You pay Snowflake compute to maintain the Iceberg tables and Fabric compute to query them; no replication, storage duplication, or ingestion jobs.
• Best for: Existing Snowflake-managed Iceberg data where you want read-oriented Fabric analytics with minimal architectural change.

Pattern 3: Snowflake writes Iceberg directly into OneLake

• How it works: Snowflake uses an external volume to write Iceberg tables directly into OneLake, creating one shared physical copy both platforms read.
• Cost shape: You pay Snowflake compute to write and maintain tables and Fabric compute to analyze them — no duplicate storage or replication.
• Best for: The cleanest long-term open-lakehouse architecture when both platforms need the same data and Snowflake remains the primary writer.

The question that settles almost every coexistence design is not which platform is better, but which one owns the single copy of the truth.

If you are weighing these patterns against a full migration and need a defensible recommendation rather than a vendor pitch, this is exactly the kind of decision BabyBots runs as a fixed-fee data platform assessment — we map your workloads, ownership, and cost shape to a migrate-coexist-stay plan in a single working engagement. See how we approach enterprise data and decision intelligence before you commit budget or engineering time.

What does a Snowflake to Fabric migration cost — and what will you actually save?

The two platforms bill in fundamentally different units, which is why "cheaper" is always workload-dependent. Fabric sells a reserved pool of Capacity Units through F SKUs at a predictable monthly price; Snowflake bills per-second virtual-warehouse compute in credits, plus storage, so it flexes to near zero and up without a fixed floor.

The cost mechanics, side by side

Microsoft Fabric

• Compute: F SKUs on the Azure pricing page — an F64, the tier that unlocks free Power BI viewers, runs about $8,410 per month pay-as-you-go or roughly $5,003 per month on a one-year reservation (about 41% less).
• Storage: OneLake billed separately at roughly $23 per TB per month.
• BI: Power BI viewers are free at F64 and above, which often removes a large per-user licensing line.

Snowflake

• Compute: Per credit, roughly $2 on Standard, $3 on Enterprise, and $4 on Business Critical, scaling linearly with warehouse size and runtime.
• Storage: Roughly $23 per TB per month, billed independently of compute.
• BI: A separate BI tool and its licensing sit on top unless you connect Power BI.

Third-party migration models show the gap can be large for steady Microsoft-anchored estates: one published Fortune 500 example modeled a 50 TB, 200-BI-user, 50-engineer Snowflake footprint at roughly $1.8 million per year against an equivalent Fabric architecture near $516,000 per year, before one-time migration cost. Treat any such figure as directional — your real number comes from measured workloads, and sizing the target capacity is its own exercise, which our Fabric capacity sizing guide walks through in detail.

The migration playbook: five phases that avoid an outage

A Snowflake-to-Fabric migration is a controlled, parallel-run exercise, not a weekend cutover. The sequence below keeps the source authoritative until the target is proven.

1. Inventory and dependency mapping. Catalog every database, schema, table, view, stored procedure, task, stream, and dynamic table, plus downstream BI and dbt dependencies, so nothing surfaces mid-migration.
2. Translate the patterns. Map Snowflake constructs to Fabric equivalents — Snowpark to Fabric notebooks with PySpark, dynamic tables to materialized lake views, secure sharing to OneLake shortcuts with RBAC — and flag anything with no clean equivalent.
3. Stand up Fabric and run in parallel. Provision the right F SKU, replicate via mirroring or interoperability, and run both platforms side by side so you can reconcile outputs against the Snowflake source.
4. Reconcile and validate. Compare row counts, key metrics, refresh behavior, and row-level security between source and target before any consumer is repointed; treat unexplained deltas as blockers.
5. Cut over, then decommission. Repoint reporting and pipelines to Fabric, confirm stability through a close cycle, and only then retire the Snowflake workloads you intended to move — never the reverse order.

What you should not migrate off Snowflake

A disciplined plan is as much about what stays as what moves. Keep these on Snowflake and coexist instead of forcing a migration.

• Live data sharing and clean rooms: Snowflake's governed, cross-organization sharing has no direct one-to-one equivalent in Fabric today.
• Multi-cloud requirements: If AWS or GCP data gravity is a hard requirement, Snowflake's cloud-neutral story is a reason to keep it.
• Deep Snowpark and native UDF workloads: Heavy in-engine Python and Snowpark Container Services are costly to re-platform.
• dbt-snowflake-first teams: Pipelines built tightly on the Snowflake dbt adapter carry real re-engineering cost.
• Sub-1 TB footprints: Below Fabric's practical floor, a small Snowflake warehouse can be cheaper than a minimum capacity.

Frequently asked questions

Is migrating from Snowflake to Microsoft Fabric a lift-and-shift?

No. SQL dialects, stored procedures, tasks, streams, and Snowpark code all need translation to Fabric equivalents, and BI and dbt dependencies must be re-pointed. The data itself moves easily through open formats, but the pipeline and logic layer is where the real work lives, so budget for pattern translation and parallel validation rather than a copy.

Can Snowflake and Microsoft Fabric share data without duplicating it?

Yes. Since the February 2026 general availability of OneLake–Snowflake interoperability, both platforms can read and write a single copy of data in open Iceberg and Delta formats. Snowflake can store managed Iceberg tables natively in OneLake, and Fabric data is exposed to Snowflake in Iceberg automatically, so you avoid maintaining separate copies.

Does Fabric Mirroring of Snowflake cost extra on the Fabric side?

Fabric does not charge ingress fees or mirroring compute to replicate Snowflake data into OneLake, and mirrored storage is free within your capacity limit. The recurring cost sits on the Snowflake side, where change-data reads consume virtual-warehouse compute. Mirroring runs continuously rather than on a schedule, so monitor Snowflake consumption and reuse an existing warehouse to control it.

Will a migration break my Power BI reports?

If you move reporting onto Fabric, semantic models, reports, and row-level security generally carry over, and Direct Lake can read OneLake data without an import-refresh cycle. The risk is not the reports themselves but the upstream data contracts — validate refresh behavior and security filters against the source before repointing any consumer.

How does the Power BI Premium retirement affect this decision?

Microsoft is retiring Power BI Premium per-capacity P SKUs and moving customers to Fabric F SKUs, with a 30-day grace period after an agreement ends, throttling from day 31, and a full block on day 91 per Microsoft's migration decision guide. If you are already moving to a Fabric capacity for Power BI, folding Snowflake-sourced analytics onto that same capacity is a natural coexistence play.

Is coexistence a permanent architecture or a stepping stone?

It can be either. Many enterprises run Snowflake as the system of record and Fabric as the consumption and AI-grounding layer indefinitely, because open formats make one copy of data serve both. Others use coexistence as a low-risk first phase, then migrate specific workloads once they have measured cost and performance on real traffic.

Where this is heading

The industry is converging on open table formats, and both vendors are leaning into it rather than fighting it. As Iceberg and Delta interoperability deepens, the migrate-or-stay framing keeps softening into a question of where each workload's gravity — BI, AI grounding, governance, and sharing — wants the data to live. The organizations that win are the ones that decide per workload, instrument real cost, and keep the single copy of truth under clear ownership.

Plan your Snowflake and Fabric decision in one working session

If you are staring at a Snowflake estate and a Fabric mandate and need a defensible answer before the next budget review, book a BabyBots data platform assessment. In a single working session we map your workloads, ownership, and cost shape to a migrate-coexist-stay plan with a sequenced path and a real number — start at babybots.ai.

Sources

• Microsoft Fabric Blog — OneLake and Snowflake interoperability generally available
• Microsoft Fabric Blog — Delivering on the promise of openness and interoperability
• Microsoft Learn — Mirroring Snowflake in Microsoft Fabric
• Microsoft Learn — Mirroring Snowflake FAQ (cost efficiency)
• Microsoft Learn — Use Snowflake with Iceberg tables in OneLake
• Microsoft Azure — Microsoft Fabric pricing (F SKUs, reservations)
• Microsoft Learn — Power BI Premium P SKU to Fabric F SKU migration decision guide
• Snowflake Blog — Unlock the open lakehouse with Microsoft OneLake and Snowflake
• MSDynamicsWorld — OneLake interoperability with Snowflake reaches general availability (Feb 3, 2026)