e6data with Microsoft Fabric: Faster Queries, Lower Bills—Without Leaving OneLake

We have been following the buzz around Microsoft Fabric for a while and recently integrated e6data directly into Fabric. Now you can query your existing OneLake data about 33% faster, cutting analytics costs by 2-3x without changing your Fabric workflow. e6data sits alongside Fabric with zero data duplication. It layers in extra security (column-level masking, GDPR/SOC2/HIPAA compliance) on top of your current Fabric setup. 

Doubling down on the cost aspects, in this blog post, we’ll cover:

• Microsoft Fabric’s pricing model 
• e6data’s compute engine: that bills per vCPU usage (not uptime), with results up to 60% lower costs. (pay-as-you-go billing, no wasted idle capacity, no overprovisioning).
• Example scenarios comparing Fabric-alone vs. Fabric + e6data cost 

‍

Microsoft Fabric’s Pricing Model

Microsoft Fabric uses a capacity-based pricing model: you purchase a Fabric capacity (measured in capacity units, CUs) from which all your workloads draw. Fabric supports bursting (temporarily boosting resources) and smoothing (averaging usage over time) to handle peaks. While the simplicity of this model is appreciated, the fixed nature of a purchased capacity can sometimes lead to higher spending in certain workload patterns. A few examples of such scenarios include:

1. Variable workloads: Fabric capacities essentially give you a fixed CU-based allotment of compute hours per day. It’s very hard to predict usage spikes. If the capacity is under-sized, workloads may get throttled; if it’s over-sized, you pay for idle resources.
   
2. High concurrency peak workloads: By design, if you fully utilize your purchased CUs (100% usage) for a given day, Fabric will start throttling further usage to cap consumption. In other words, without the ability to go beyond your fixed CU pool, extreme peak demands may be constrained or delayed. There is an option to exceed 100% of capacity by paying for the excess on the fly under a different pricing model.
3. ‍Complex, Ad-hoc Workloads: Fabric capacities are shared by all workspaces within a single tenant region, which can lead to resource contention and performance bottlenecks. That unified pool can become a single point of contention. For example, a heavy data engineering job could consume most of the CUs and degrade a critical Power BI report refresh. Currently, the only way to fully isolate such workloads is to buy separate capacities (e.g. one for ETL pipelines and another for BI reports). This approach protects each workload’s performance, but it increases cost and adds management overhead.

‍

e6data + Fabric: 2-3x lower TCO compared to native compute engines

e6data is a lakehouse compute engine that can query data directly in OneLake at a 33% faster speed and 2-3x lower TCO than other query engines, as validated by internal TPC-DS benchmark testing. There’s no data silo or fork, you could have Fabric’s own SQL engine and e6data querying the same OneLake data simultaneously for different workloads.

‍

Cost Comparison: MS Fabric-only Vs. MS Fabric + e6data

Here’s how Fabric alone compares to Fabric + e6data in real-world enterprise analytics.
(For reference, approximate pricing: Fabric F2 costs about $263/month pay-as-you-go, so an F32 is around $4,200/month. e6data usage is ~$0.10 per vCPU-hour in these examples.)

‍

e6data pricing: atomic architecture, pay-per vCPU usage, zero idle costs

e6data’s architecture is built on an atomic architecture that scales granularly with stateless services, with scaling granularity down to 1 vCPU increments to handle petabyte-scale of analytics workloads:

1. You only pay for actual vCPU usage: Your e6data compute bill depends purely on the vCPU seconds your queries consume. That means costs are super predictable, especially handy if your Fabric workloads are all over the place. For example, if you run nothing on e6data for an hour, you pay $0 for that hour. If you have a sudden spike requiring 100 vCPUs for 5 minutes, you pay only for those 5 minutes of 100 vCPUs – then it scales back down.
   
2. You don’t pay for idle time: Because e6data scales atomically on Kubernetes, so you’re not stuck paying for idle time or spare capacity. No more overprovisioning. You can handle peak demands without having to permanently allocate for peak. It’ll auto-scale instantly when demand spikes, then scale back without manual intervention.
   
3. Performance gains = additional savings: In our tests, e6data (with Fabric) is ~33% faster than Fabric’s native compute engine. This means a query that takes 60 minutes on the built-in engine might finish in ~40 minutes on e6data. Performance gains translate directly to reduced Microsoft Fabric Costs — faster queries reduce billed vCPU usage, so you're saving costs above and beyond the pricing structure itself.

‍

Here's the deal: Fabric’s great for your core workloads, but pairing it with e6data significantly reduces Microsoft Fabric costs during bursty, ad-hoc, or high-concurrency scenarios.

Think of e6data as a specialized compute layer that slides neatly into Fabric whenever your workloads get tricky without replacing Fabric itself.

You get the best of both worlds:

• Keep Fabric's unified workflow (OneLake, Power BI, etc)
• Add e6data to handle dynamic workloads efficiently
• Save ~50-66% on costs, and keep everything simple

Win-Win!

‍

Increase Microsoft Fabric Performance and Reduce Costs—Get Started with e6data on OneLake Today!

If you’re a data engineer working with Fabric, give e6data a try on your OneLake data. One-click connect, run your queries, and see the query performance immediately. If you want to gain early access or learn more about our upcoming performance upgrades, reach out to us here, and we’ll be in touch soon!

‍

‍

‍