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Side-by-side record

Dremio vs. Vertica

A side-by-side view of the product records we have today. Use it to narrow the question, then confirm current pricing, limits, and security details with each vendor.

The short version

Dremio starts at Free per seat, versus Vertica at Custom pricing. Dremio carries the higher aggregate rating (4.6/5 vs 4.3/5). Dremio lists more integrations (23+ vs 13+).

Full comparison

Dremiofrom Free
Verticafrom Custom pricing
Audyense Score78ASStrong56ASFair
PositioningA unified lakehouse platform that lets teams query and analyze data across S3, Snowflake, Redshift, and dozens of other sources directly on open formats like Apache Iceberg.A massively parallel, columnar analytics database for querying and running machine learning on huge datasets, available on-prem, across major clouds, or as a fully managed SaaS.
Free tierYesYes
DeploymentCloud / SaaS, On-premise, HybridCloud / SaaS, On-premise, Hybrid
Best fitSMB, Mid-market, EnterpriseMid-market, Enterprise
Pricing plans
  • Community EditionFree
  • Dremio Cloud$0
  • Dremio EnterpriseCustom pricing
  • VerticaCustom pricing
AI featuresYesAI Semantic Layer and built-in AI Agent, plus MCP integration for Claude/ChatGPT/Gemini.YesNative in-database ML functions plus VerticaPy Python library for in-database data science.
Data governance & lineageYesFine-grained RBAC down to rows/columns, Open Catalog governance, plus compliance certifications.PartialHas RBAC, encryption, and FIPS 140-2 compliance, but no strong evidence of dedicated data-catalog/lineage tooling.
Data pipelines / ETLPartialPositioned to reduce/eliminate traditional ETL via federated live querying rather than being a pipeline/orchestration tool.YesNative real-time ingestion from Kafka, integration with Spark, and connectors for common ETL tools.
Pre-built connectorsYes20+ named native connectors plus ODBC/JDBC/Arrow Flight generic connectivity.YesJDBC/ODBC drivers plus named connectors for Tableau, Power BI, Talend, Informatica, MuleSoft, Qlik, Looker.
Public APIYesDocumented ODBC/JDBC/Arrow Flight interfaces and a Dremio CLI/MCP integration.PartialPrimarily accessed via SQL/JDBC/ODBC client drivers; no clear evidence of a dedicated modern public REST management API.
Role-based access controlYesRole-based access control with row/column-level granularity.YesDocumented role-based access control as part of its security model.
Scheduling & triggersNoNo evidence of job/workflow scheduling distinct from query execution and Reflections refresh.PartialSupports scheduled/streaming data loads but is not a general-purpose workflow-trigger system.
Self-hosting / on-premYesCommunity Edition and Dremio Enterprise both support self-hosted deployment.YesFully supports on-premises, customer-managed deployment (BYOL), the original and still-supported delivery model.
Workflow automationNoAutomation is scoped to query acceleration and AI-agent query assistance, not general workflow automation.NoNot a workflow-automation product; automation happens via external orchestration/ETL tools.
Integrations verified23+13+
Aggregate rating4.6 · 69 reviews4.3 · 216 reviews
Integrations
Amazon S3Azure Data Lake StorageGoogle Cloud StorageAWS Glue Data CatalogApache HiveApache Iceberg REST Catalog+17 more
Apache KafkaApache SparkApache NiFiTableauMicrosoft Power BIInformatica+7 more
Security & compliance
SOC 2 Type IIISO/IEC 27001:2022HIPAAGDPR+1 more
FIPS 140-2
Pros
  • Federated SQL queries across many disparate data sources without moving or duplicating data
  • Reflections acceleration feature delivers sub-second BI query response times
  • Rated highly for ease of use and fast, direct data exploration by both technical and non-technical users
  • Strong data lake / lakehouse integration and cloud processing scores from reviewers
  • Very fast query performance from columnar storage + massively parallel processing
  • Strong scalability and high availability with minimal downtime
  • Powerful built-in analytics and in-database machine learning functions, avoiding data movement to a separate ML platform
  • Eon Mode's separation of compute and storage enables elastic cloud scaling and cost control
Cons
  • Some users report occasional out-of-memory (OOM) errors without clear diagnostic explanations
  • High resource demands and difficulty maintaining the environment at scale
  • Steep learning curve for advanced features despite a low barrier for basic querying
  • Review volume on major platforms is relatively thin for a company of Dremio's market position
  • Expensive — reviewers who comment on cost describe it as pricey, with restrictive data storage limits on some plans
  • Steep learning curve; installation, cluster setup, and scaling require specialized database engineering skill
  • About half of reviewers cite inadequate technical/community support and gaps in documentation
  • Resource-intensive — needs significant CPU/memory/storage to perform well
Visit Dremio ↗Visit Vertica ↗

Editorial read of this comparison

The table summarizes structured facts; this section explains what the differences mean for a real buying decision.

Where each tool fits, and where it may not

Dremio

Dremio is a data lakehouse platform built around Apache Iceberg and Apache Arrow that lets analysts and engineers query data in place across cloud storage, data warehouses, and relational databases via a single SQL interface. It offers a semantic layer and an acceleration/caching feature called 'Reflections' for sub-second BI query performance, plus AI Semantic Layer and AI agent/MCP integration. It ships as a fully managed cloud service, a self-managed enterprise product, and a free open-source Community Edition.

A particularly good fit for: Data engineering / analytics teams that need to query data across multiple disparate sources without building and maintaining ETL pipelines Organizations standardizing on Apache Iceberg and wanting an open lakehouse architecture instead of vendor lock-in Mid-market to enterprise teams that need self-hosted/on-prem or hybrid deployment for compliance or infrastructure-control reasons

May be a poor fit if: Small teams or startups without dedicated data engineering resources, given the reported operational complexity Teams wanting a simple, fully turnkey BI tool rather than a lakehouse query/virtualization platform

Vertica

Vertica is a column-oriented analytics database built for high-speed SQL queries and in-database machine learning at terabyte-to-petabyte scale. It runs on-premises, self-managed on AWS/Azure/GCP, or as a fully managed cloud service, with Eon Mode separating compute and storage for elastic cloud scaling. Originally founded in 2005, it passed through Hewlett-Packard, Micro Focus, and OpenText, and was acquired by Rocket Software from OpenText in May 2026.

A particularly good fit for: Enterprises running large-scale (terabyte-to-petabyte) analytical or data-warehouse workloads that need very fast SQL performance Data science/analytics teams that want to run machine learning directly against data in the database Organizations needing flexible deployment across on-prem, multiple public clouds, or a managed cloud service

May be a poor fit if: Small businesses or startups with limited budgets or without dedicated database/infrastructure engineering staff Teams wanting a lightweight, fully self-serve cloud analytics tool with minimal setup and no licensing negotiation

Pricing and plan structure

Dremio: Published starting price Free

  • Community EditionFree
  • Dremio Cloud$0 per DCU (consumption-based)
  • Dremio EnterpriseCustom pricing

Vertica: Published starting price Custom pricing

  • VerticaCustom pricing

Capabilities worth validating

  • Dremio: AI features, Data governance & lineage, Data pipelines / ETL, Pre-built connectors, Public API Integrations include Amazon S3, Azure Data Lake Storage, Google Cloud Storage, AWS Glue Data Catalog.
  • Vertica: AI features, Data governance & lineage, Data pipelines / ETL, Pre-built connectors, Public API Integrations include Apache Kafka, Apache Spark, Apache NiFi, Tableau.

Questions to answer before switching

  • Does Dremio's selected plan include the features and limits we need?
  • Does Vertica's selected plan include the features and limits we need?
  • Does the exact integration path for Amazon S3, Azure Data Lake Storage, Google Cloud Storage support the sync direction, permissions, and volume we need?
  • Will the deployment and data-residency model meet our security and procurement requirements?

How to evaluate this shortlist

A useful comparison turns the differences between Data Infrastructure tools into a concrete test. Use these steps to avoid choosing from a feature table or the lowest headline price alone.

  1. Start with a representative Data Infrastructure workflow, not a feature checklist. Define who will use it, what data goes in, and what outcome the team needs.
  2. Test the complete path through Amazon S3, Azure Data Lake Storage, Google Cloud Storage: permissions, sync direction, failure handling, and volume limits often matter more than the integration name.
  3. Compare the cost of the real scenario, including users, usage, storage, support, and contract requirements. The entry price alone does not measure adoption cost.
  4. Before switching, list the evidence gaps, request a demo of the critical workflow, and confirm security, data residency, export, and support with each vendor.

Structured signals help narrow the shortlist, but a trial with a real workflow is still the best way to validate the decision.

Research basis

Last checked: 2026-07-31. Pricing, integrations, feature support, and review signals can change, so treat this as a research snapshot and verify the final decision with the vendor.

Sources consulted: Dremio product site, G2, PeerSpot; Vertica product site, G2, TrustRadius