Starburst vs. Dremio: Breaking Down the Numbers

Whether Dremio is the right engine for you now comes down to a single question AI has made unavoidable, which is whether it can reach all of your data. Universal data access, the ability to query all of your data wherever it lives, was always the better foundation for analytics. Data federation delivered it by reaching data across every system instead of forcing everything into one store first, and for years that was a genuine advantage rather than a strict requirement. Plenty of workloads stayed inside a single lakehouse, and an engine built to serve that narrower case, as Dremio was, could compete on its merits.

AI has changed that calculation considerably, and that has big implications for Dremio users. Agents do not sit inside one lake and wait their turn. Instead, they reason across the whole enterprise estate, launch dozens of queries at once, and expect current answers at production speed. An agent that cannot reach a system simply cannot reason about what is in it, which means the data it cannot access becomes a gap in its judgment. What was once a nice-to-have for analytics is now the precondition for trustworthy AI, and universal access through data federation is the only way to provide it.

Which brings us right back to data federation, and one core question. How do you achieve the best data federation? My answer, perhaps unsurprisingly is Starburst, especially when considering Dremio. 

Why? A few basics stand out. 

First, is focus and scope. Starburst is a data federation engine, built from the start to query more than 50 sources wherever the data lives; Dremio is a lakehouse engine, built to bring data into the lake and query it there. That difference in reach is the cause behind nearly every number that we see whenever we run head-to-head comparisons. 

There’s a pattern here. For example, the 2.5x connector gap, the 30–55% price-performance advantage, and the 45% lower total cost of ownership are not separate wins. They are the same architectural decision, universal access versus a single lake, showing up on different lines of the scorecard, and AI is what turned that decision from a preference into a dividing line.

Why look at Starburst vs Dremio now

There has never been a more important time to consider Dremio than now. Dremio’s acquisition by SAP makes this a natural moment to run that comparison, and since we’ve written separately about what the acquisition itself means, this piece will focus on what can be measured.

The comparison at a glance

Figures from Enterprise Strategy Group (now part of Omdia), The Economic Benefits of the Starburst Data Platform for Apps and AI, 2026, and public Gartner Peer Insights and PeerSpot reviews, 2026. Validate against your own proof of value.

The numbers, category by category

Each row below traces to the same root cause. Here is how the difference in data access shows up on each line of the scorecard.

How many data sources can each platform reach?

Starburst ships more than 50 native connectors with pushdown optimization. Dremio ships around 20, concentrated on lakehouse storage and object stores. That is 2.5x the native reach, and reach is what universal access is made of, because the more sources an engine connects to, the more of your estate it can query directly, without first moving anything.

For years that breadth was an edge. In the AI era it is decisive, because an agent is only as capable as the data it can reach, and the enterprise estate is not one lake. It is databases, warehouses, object stores, streaming sources, and SaaS systems, spread across clouds and on-premises. This is why data federation is the access layer agentic workloads depend on and why structured data across the whole estate is the ground truth an agent reasons from. Every source an engine cannot reach natively forces a choice between leaving it out and building a pipeline to move it in, and Dremio’s narrower connector list forces that choice far more often.

How do they compare on concurrency and price-performance?

Independent benchmarking from Enterprise Strategy Group (now part of Omdia) found Starburst delivered 30–55% better price-performance at scale (ESG/Omdia, 2026). The reason is architectural. Starburst runs on open-source Trino, a distributed SQL engine built for high-concurrency workloads with fault-tolerant execution and workload isolation across federated sources. Dremio’s Apache Arrow execution and Reflections are tuned for the lakehouse, so its performance holds only there; the moment queries reach external systems or enterprise concurrency, it degrades.

That degradation is the failure mode AI can least afford. Agents do not issue one query and wait; they issue many at once, continuously, across every source they can reach. An engine that stays fast only inside its own lake is fast in precisely the conditions agentic workloads never operate in, and slow in the ones they always create.

Which acceleration model fits unpredictable workloads?

The two platforms accelerate queries in opposite ways, and the contrast comes back to how each one reaches data. Starburst’s Warp Speed applies caching, indexing, and runtime optimizations that adapt automatically as workloads shift, on top of live federated access to the source. Dremio’s Reflections precompute and materialize datasets in advance, which is a copy of your data, refreshed on a schedule, that queries run against instead of the source itself.

That design carries two costs. First, Reflections must be configured, refreshed, and lifecycle-managed, with platform limits on how many can exist or refresh in a given window. Second, and more important for AI, precomputation assumes you know the query in advance, and agents do not cooperate with that assumption. They generate query shapes no one precomputed for. Live federated access answers those queries against current data as they arrive; a precomputed copy cannot, and falls back to slower paths or stale results.

What do the platforms actually cost?

The same ESG/Omdia analysis found organizations on Starburst achieved 414% ROI over three years and 45% lower total cost of ownership, and it named the driver directly, citing improved query efficiency and reduced data movement (ESG/Omdia, 2026). This is universal access stated in dollars. Reaching data where it lives, rather than copying it into an acceleration layer first, cuts spend on compute, on duplicated storage, and on the engineering hours that pipelines quietly consume. In the most demanding environments the same logic decides competitive advantage, which is why financial-services firms now find that edge belongs to those who connect and govern data in place rather than moving it. If you are already questioning whether the economics of your current platform still hold up, this is where the answer lives.

How open is each platform, really?

Starburst supports Apache Iceberg, Delta Lake, and Apache Hudi, so the same data stays reachable by multiple engines without conversion or duplication. Dremio centers on Apache Iceberg with Polaris catalog support. Iceberg is a sound bet, one we back heavily ourselves, but committing to a single format ties your interoperability to that one format’s ecosystem, and anything outside it becomes another copy to manage. Three-format support on an open-source Trino engine is a wider footprint measured the only way that counts in the AI era, which is how much of your data the engine can access directly.

What do users report?

These are the softest numbers here, so weight them accordingly, but they point the same way. Starburst holds 4.7/5 on Gartner Peer Insights across 60+ reviews against Dremio’s 4.4/5, and PeerSpot splits roughly 9.8 to 8.5 (Gartner Peer Insights and PeerSpot, 2026). Notably, Dremio reviews frequently cite stability and support at scale, the exact dimension that AI workloads stress hardest.

So which one is right for you?

The honest answer depends on how much of your data lives in one place. If your workload is genuinely contained, meaning SQL analytics on a single Iceberg lakehouse you own with nothing to reach beyond it, Dremio’s specialization can cover it and these numbers may not decide anything for you. That case is real, and we account for it.

It is not, however, where most enterprises are, and it is nowhere near where AI is taking them. The moment your data spans more than one system, universal access becomes the constraint that governs every number above, and the cost of Dremio’s narrower reach, in pipelines built, copies maintained, and sources left unreachable, starts compounding. Every measurable dimension in the comparison favors universal access over a single lake, and every one of them did so before AI made the case undeniable and before SAP entered the picture.

To make that concrete, here is the short version of who each platform fits.

When is Starburst the right choice?

• Your data spans more than one system, and you need to query databases, warehouses, object stores, and SaaS sources without moving them first
• You are putting AI agents into production and need them to reason across the whole estate at high concurrency
• Your workloads are unpredictable, so precomputed acceleration cannot be defined in advance
• You want price-performance and total cost of ownership that improve as you consolidate query paths rather than duplicate storage
• You want the freedom of multiple open table formats and an open-source engine rather than a single-format commitment

When might Dremio still work?

• Your analytics live inside a single Iceberg lakehouse you own, with no meaningful need to reach beyond it
• Your query patterns are predictable enough to precompute for, and repeat often enough to justify managing that acceleration
• Concurrency stays within the lake, and you do not expect agentic workloads to widen it
• A single open table format covers your interoperability needs for the foreseeable future

That is the real lesson in the numbers. Universal data access was the smarter foundation for analytics all along, and AI has simply removed the option of ignoring it. 

The way to confirm it is a proof of concept on your own workloads, with your own concurrency and your own mix of sources, because vendor benchmarks are a starting point, not a verdict. If you want a structured way to run that evaluation, the Data Lakehouse Buyer’s Guide lays out the criteria, and for the full head-to-head, see the complete Starburst vs. Dremio comparison. Then bring your hardest workload, the one that spans the most sources, and test both.