OpenText Vertica alternatives

3 tools to consider instead of OpenText Vertica, shown against it.

OpenText Vertica Exasol Teradata VantageCloud Greenplum
Vendor OpenText Corporation Exasol AG Teradata Corporation Greenplum open-source community (originally Pivotal/VMware)
Pricing model Quote only Quote only Quote only Open source + paid options
Free tier Yes Yes No Yes
Deployment Cloud, Self-hosted Cloud, Self-hosted Cloud Self-hosted
Open source No No No Yes (Apache-2.0)
Best for Enterprises with existing Vertica investments needing flexible on-premises-to-cloud deployment for heavy analytical SQL. Analytics teams running complex, ad hoc SQL who want strong price/performance without manual index tuning. Large enterprises with complex, high-concurrency SQL workloads who need proven mixed-workload management at scale. Organizations wanting a self-managed, open-source MPP warehouse with PostgreSQL compatibility.
Pricing

Licensed by data capacity or consumption depending on deployment; a free Community Edition exists for smaller datasets, and full pricing requires contacting OpenText sales.

Pricing has not been verified yet — see the vendor's site.

Licensed by capacity (historically RAM-based) with SaaS, self-managed, and a free Community Edition; the public pricing page does not publish figures and directs prospects to a free trial or sales contact.

Checked on the vendor's own page on September 21, 2026: no prices are published. Expect to be quoted.

Priced through a unified Teradata Unit consumption model combining base and on-demand capacity; the pricing page provides a cost calculator and directs prospects to a sales specialist rather than publishing figures.

Checked on the vendor's own page on September 21, 2026: no prices are published. Expect to be quoted.

Free and open source under the Apache 2.0 license; no vendor-hosted SaaS tier, so costs are limited to the infrastructure it runs on.

Pricing has not been verified yet — see the vendor's site.

Features
  • Columnar storage with Eon Mode storage-compute separation
  • Runs on-premises, in Kubernetes, or across AWS/Azure/GCP
  • In-database machine learning (classification, regression, clustering)
  • Time-series and event/pattern-matching SQL extensions
  • Geospatial analytics functions
  • Flex tables for querying semi-structured data without a fixed schema
  • Free Community Edition for evaluation and small deployments
  • In-memory columnar storage with automatic compression
  • Massively parallel processing with automatic, self-tuning indexing
  • SaaS, self-managed, and on-premises deployment options
  • In-database machine learning via Python, R, and Java UDFs
  • Virtual schemas for federated queries into other databases
  • Free single-node Community Edition for evaluation and small workloads
  • ACID-compliant transactional consistency
  • Massively parallel processing (MPP) SQL engine tuned for high concurrency
  • VantageCloud Lake (public cloud) and Enterprise (managed private cloud) deployment models
  • Unified Teradata Unit consumption pricing across compute and features
  • In-database machine learning and graph analytics functions
  • Workload management for prioritizing mixed, concurrent queries
  • ClearScape Analytics for in-platform data science
  • Multi-cloud support across AWS, Azure, and Google Cloud
  • Massively parallel processing built on PostgreSQL
  • Column- and row-oriented table storage options
  • In-database machine learning via MADlib
  • PostGIS-based geospatial analytics
  • Resource groups for workload management and query prioritization
  • External tables for querying data in S3-compatible object storage
  • Standard PostgreSQL drivers, extensions, and SQL compatibility

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