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Dremio

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The Agentic Lakehouse for AI and Analytics, providing fast, governed, and unified data access.

Free plan available, paid tiers above
FreemiumData & DatabasesAnalyticsBig Data Analytics
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Reviews onG2
71 reviews tracked

The Bottom Line

Price
Free plan available, paid tiers above. Compare plans

Key facts

  • Unifies data across sources with zero ETL for AI and analytics.
  • Provides an AI Semantic Layer for trusted AI context and accurate answers.
  • Offers 20x performance at lower cost through autonomous optimizations and open standards.

Pros

  • Significantly improves query performance (e.g., 10x faster, millisecond latency)
  • Reduces data setup time and eliminates manual work (e.g., 90% reduction, 60hrs eliminated)
  • Provides unified access to data across diverse sources without ETL
  • Offers robust data governance and security with fine-grained access control
  • Built on open-source standards (Apache Iceberg, Arrow, Polaris) for flexibility and avoiding lock-in

Cons

  • No explicit mention of a free tier or trial on the provided pages
  • Requires understanding of lakehouse architecture concepts

What is Dremio?

Dremio is an "Agentic Lakehouse" platform designed to accelerate AI and analytics initiatives by providing a unified, high-performance data layer. It allows organizations to federate queries across diverse data sources, including on-premises and cloud data lakes, without the need for complex ETL processes. Dremio creates an AI Semantic Layer that gives AI models the necessary context to deliver accurate and trusted answers, supporting both integrated analyst agents and custom AI frameworks. The platform is built on open lakehouse standards like Apache Iceberg, Apache Arrow, and Apache Polaris, ensuring interoperability and avoiding vendor lock-in. It offers features like Autonomous Reflections for query acceleration, Automatic Iceberg Clustering for data layout optimization, and a Columnar Cloud Cache for faster data access. Dremio aims to provide data warehouse performance and functionality with data lake flexibility, reducing costs and complexity for data teams across various industries.

Available on: Web

how we evaluateSourcedremio.com ↗

Ratings Across the Web

4.6(71 reviews)

Dremio holds an aggregate rating of 4.6 out of 5 from 71 reviews across G2, last checked September 23, 2026.

G271 reviews
4.6/5

Ratings aggregated from independent review platforms. Learn more

Preview

Key Features

  • AI Semantic Layer for trusted AI context
  • Data Unification with Zero ETL and AI functions for unstructured data
  • Agent Choice for data querying and visualization (integrated or custom)
  • Open Catalog (Apache Polaris) with fine-grained access control
  • Autonomous Reflections for query acceleration
  • Automatic Iceberg Clustering for data layout optimization
  • Arrow-Based Intelligent Query Engine with LLVM-based code generation
  • Columnar Cloud Cache (C3) for speeding up data access

Pricing Plans

Free Trial

Pricing checked Oct 4, 2026

Dremio plans and prices, checked October 2026
PlanPriceDetails
Dremio Cloud

$0.20 / DCU

  • Zero Infrastructure Management
  • Instant Setup
  • Automatic Feature Releases
+4 more
  • Dremio-managed Infrastructure Management
  • AWS (Azure Coming Soon) Supported Infrastructure
  • Automatic - zero downtime Upgrades
  • Auto Scale Scalability
Dremio Enterprise

Contact Sales

  • Complete Infrastructure Control
  • Flexible Deployment
  • Custom Integration
+4 more
  • Self-managed Infrastructure Management
  • K8S (On prem) or Cloud (AWS, Azure, GCP) Supported Infrastructure
  • Self-managed Upgrades
  • Manual Scale Scalability

Included in all plans

  • AI Agent
  • AI Semantic Layer
  • Intelligent Query Engine
  • Open Catalog
  • Consumption based Pricing Model
  • Enterprise Ready Security & Compliance
Calculate your costView full pricing

Is Dremio worth the price?

Good value

Dremio's pricing structure is fair, offering a consumption-based model that scales with usage.

Dremio Cloud at $0.20 per DCU provides an accessible entry point for cloud-native analytics, while Dremio Enterprise caters to organizations needing full infrastructure control. This model is best for businesses seeking flexibility and control over their data infrastructure, from cloud-managed to self-managed solutions.

Hidden Costs & Gotchas

Self-managed upgrades in Enterprise tier require internal resources.

Full Dremio pricing: every plan, hidden costs and alternatives

Reviews

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4.6/5

Across 71 verified user reviews on G2

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Dremio FAQ

How does Dremio enable faster analytics?

Dremio accelerates analytics by providing a unified, high-performance data layer and features like Autonomous Reflections for query acceleration. It is designed to deliver data warehouse performance and functionality with data lake flexibility, significantly improving query speeds.

Which teams benefit most from using Dremio?

Data teams focused on AI and analytics initiatives, particularly those dealing with diverse data sources, will find Dremio most beneficial. It helps reduce data setup time and eliminates manual work, allowing these teams to achieve faster insights.

How does Dremio compare to Databricks for data access?

Dremio focuses on providing unified access to data across diverse sources without requiring complex ETL processes, acting as an Agentic Lakehouse. It emphasizes federating queries across on-premises and cloud data lakes, built on open lakehouse standards.

What kind of data governance does Dremio offer?

Dremio provides robust data governance and security features, including fine-grained access control. This ensures that data access is managed effectively across the unified data layer.

Does Dremio support open data standards?

Yes, Dremio is built on open lakehouse standards such as Apache Iceberg, Apache Arrow, and Apache Polaris. This design promotes interoperability and helps organizations avoid vendor lock-in.

How is Dremio priced?

Dremio is available on a free tier, with additional paid plans offered for increased usage and advanced features. This allows users to start with basic functionality and scale up as needed.

What are the main trade-offs when implementing Dremio?

A primary consideration for implementing Dremio is that it requires an understanding of lakehouse architecture concepts. While it offers significant performance and unification benefits, familiarity with these principles is helpful.

Source: dremio.com

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