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Dremio vs. Vertica

Elaborado a partir del registro investigado y revisado de cada herramienta. Las cifras se contrastan con las páginas públicas de precios en el momento de la investigación — confirma siempre el precio actual con el proveedor antes de comprar.

La versión corta

Dremio parte de Free por usuario, frente a Vertica con Custom pricing. Dremio tiene la valoración agregada más alta (4.6/5 frente a 4.3/5). Dremio lista más integraciones (23+ frente a 13+).

Comparación completa

Dremiodesde Free
Verticadesde Custom pricing
Audyense Score78ASSólido56ASAceptable
PosicionamientoA 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.
Plan gratuitoSíSí
ImplementaciónNube / SaaS, Instalación local, HíbridoNube / SaaS, Instalación local, Híbrido
Mejor encajePYME, Mediana empresa, EmpresaMediana empresa, Empresa
Planes de precio
  • Community EditionFree
  • Dremio Cloud$0
  • Dremio EnterpriseCustom pricing
  • VerticaCustom pricing
AI featuresSíAI Semantic Layer and built-in AI Agent, plus MCP integration for Claude/ChatGPT/Gemini.SíNative in-database ML functions plus VerticaPy Python library for in-database data science.
Data governance & lineageSíFine-grained RBAC down to rows/columns, Open Catalog governance, plus compliance certifications.ParcialHas RBAC, encryption, and FIPS 140-2 compliance, but no strong evidence of dedicated data-catalog/lineage tooling.
Data pipelines / ETLParcialPositioned to reduce/eliminate traditional ETL via federated live querying rather than being a pipeline/orchestration tool.SíNative real-time ingestion from Kafka, integration with Spark, and connectors for common ETL tools.
Pre-built connectorsSí20+ named native connectors plus ODBC/JDBC/Arrow Flight generic connectivity.SíJDBC/ODBC drivers plus named connectors for Tableau, Power BI, Talend, Informatica, MuleSoft, Qlik, Looker.
Public APISíDocumented ODBC/JDBC/Arrow Flight interfaces and a Dremio CLI/MCP integration.ParcialPrimarily accessed via SQL/JDBC/ODBC client drivers; no clear evidence of a dedicated modern public REST management API.
Role-based access controlSíRole-based access control with row/column-level granularity.SíDocumented role-based access control as part of its security model.
Scheduling & triggersNoNo evidence of job/workflow scheduling distinct from query execution and Reflections refresh.ParcialSupports scheduled/streaming data loads but is not a general-purpose workflow-trigger system.
Self-hosting / on-premSíCommunity Edition and Dremio Enterprise both support self-hosted deployment.SíFully 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.
Integraciones verificadas23+13+
Valoración agregada4.6 · 69 reviews4.3 · 216 reviews
Integraciones
Amazon S3Azure Data Lake StorageGoogle Cloud StorageAWS Glue Data CatalogApache HiveApache Iceberg REST Catalog+17 más
Apache KafkaApache SparkApache NiFiTableauMicrosoft Power BIInformatica+7 más
Seguridad y cumplimiento
SOC 2 Type IIISO/IEC 27001:2022HIPAAGDPR+1 más
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
Contras
  • 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
Visitar Dremio ↗Visitar Vertica ↗

Lectura editorial de la comparación

La tabla resume los datos estructurados; esta sección explica cómo interpretar las diferencias para una decisión de compra real.

Encaje y límites de cada herramienta

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.

Encaja especialmente con: 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

Puede no encajar si: 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.

Encaja especialmente con: 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

Puede no encajar si: 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

Precios y estructura de planes

Dremio: Precio inicial publicado Free

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

Vertica: Precio inicial publicado Custom pricing

  • VerticaCustom pricing

Capacidades que conviene validar

  • 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.

Preguntas antes de cambiar

  • ¿El plan de Dremio incluye las funciones y límites que necesitamos?
  • ¿El plan de Vertica incluye las funciones y límites que necesitamos?
  • 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?

Cómo evaluar esta lista corta

Una comparación útil convierte las diferencias de Infraestructura de Datos en una prueba concreta. Usa estos pasos para evitar elegir por una tabla de funciones o por el precio más bajo.

  1. Empieza con un flujo de trabajo representativo de Infraestructura de Datos, no con una lista de funciones. Define quién lo usará, qué datos entran y qué resultado debe producir.
  2. Prueba el recorrido completo con Amazon S3, Azure Data Lake Storage, Google Cloud Storage: permisos, dirección de sincronización, errores y límites de volumen suelen importar más que el nombre de una integración.
  3. Compara el coste del escenario real, incluidos usuarios, uso, almacenamiento, soporte y cualquier requisito de contrato. El precio inicial por sí solo no mide el coste de adopción.
  4. Antes de cambiar, registra qué evidencia falta, pide una demostración del flujo crítico y confirma seguridad, residencia de datos, exportación y soporte con cada proveedor.

Las señales estructuradas ayudan a reducir la lista, pero una prueba con un flujo real sigue siendo la mejor forma de validar la decisión.

Base de investigación

Última comprobación: 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.

Fuentes consultadas: Dremio product site, G2, PeerSpot; Vertica product site, G2, TrustRadius