Buying snapshot
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| Decision point | What Rocket states |
|---|---|
Where it runs | On AWS as a 64-bit (x86) Amazon Machine Image on Amazon Linux 2023. Rocket says Vertica also runs on-premises on industry-standard hardware and on Hadoop nodes. |
Launch on AWS | Subscribe, then use the Manual Launch tab to complete a CloudFormation template. Delivery options: Management Console with a 3-node Eon Cluster, or the Management Console deployed into a new or an existing VPC. |
Cancellation and refunds | AWS Marketplace subscriptions have no end date and can be cancelled at any time. Vendor refund policy on both listings: no refunds, cancel at any time. |
Python access | The vertica-python client library connects Python code to a Vertica database; it supports one cursor per connection. |
Our take
Choose Vertica if your team is ready to launch and run the database cluster itself, in its own AWS account or on its own hardware, and wants Eon Mode's split of compute and storage. If no one on the team can own that operating work, weigh it before you compare hourly rates.
Evaluate Vertica
Comparisons
Where it stands out
Eon Mode for changing workloads. Rocket describes Eon Mode as the separation of compute from storage, with rapid elasticity for changing workloads and subclusters for workload isolation. On AWS, Rocket says you can scale cluster size to variable demand and/or scale S3 storage. This matters when different teams or jobs should not slow each other down.
One database across AWS and your own servers. A team can keep part of its estate on its own hardware or Hadoop nodes and still use the AWS listings for cloud clusters.
Analytics inside the database. Rocket lists built-in machine learning, predictive analytics, integrated BI/reporting and data ingestion, and says the machine learning and predictive analytics features help pre-process data, discover insights and predict outcomes.
Fits existing data pipelines. Rocket says Vertica integrates within an existing data pipeline built on Kafka, Spark and/or Hadoop.
Bulk loading from Python. vertica-python can load client-side files or streams with COPY FROM STDIN or COPY FROM LOCAL, and supports server-side prepared statements, which its README says are useful for preventing SQL injection attacks.
Trade-offs to weigh
Support depends on how you buy. On the by-the-Hour listing, Enterprise Support requires at least 180 hours of usage per month, so a cluster you start only now and then may not qualify. On BYOL, vendor support is based on your license, with community and forum support (Rocket asks you to contact Vertica for more information).
The Python client is pure Python. The vertica-python README says it can run much slower than another Python client such as pyodbc, or clients in compiled languages, and suggests a performance-oriented interpreter such as PyPy. Plan large Python extracts with this in mind.
Older servers return complex types as text. Servers before v12.0.2 cannot give vertica-python enough metadata for complex types (ARRAY, SET, ROW, MAP), so the client receives them as JSON strings and converts them to str instead of Python lists, sets or dicts.
Pricing and plan conditions
Rocket sells Vertica on AWS Marketplace in two ways. On the by-the-Hour listing, pricing is based on actual usage, with an hourly rate for each EC2 instance type you run. On the BYOL listing, you activate the product with a license bought outside AWS Marketplace, while AWS provides the infrastructure; cancelling the AWS subscription does not affect the external license, and you can request a private offer for a custom quote. On both listings, Rocket notes that additional AWS infrastructure costs may apply and points to the AWS Pricing Calculator to estimate them.
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| Listing and instance type | Published price | Conditions |
|---|---|---|
By the Hour: c4.2xlarge | $1.00 per hour | Usage cost per hour for this instance type |
By the Hour: i3en.2xlarge | $1.00 per hour | Usage cost per hour for this instance type |
By the Hour: i3en.3xlarge | $1.50 per hour | Usage cost per hour for this instance type |
By the Hour: r4.4xlarge | $2.00 per hour | The instance type the listing marks as Recommended |
By the Hour: c4.4xlarge, c5.4xlarge or c5d.4xlarge | $2.00 per hour | Same usage cost per hour for each of these three instance types |
By the Hour: d2.8xlarge | $4.50 per hour | Usage cost per hour for this instance type |
By the Hour: d3en.12xlarge | $6.00 per hour | Usage cost per hour for this instance type |
By the Hour: c6i.32xlarge | $16.00 per hour | Usage cost per hour for this instance type |
BYOL | License bought from the vendor outside AWS Marketplace; custom quote through a private offer | The first three nodes and one terabyte are free for non-production use; Rocket directs you to Vertica.com for more capacity or production licensing |
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
0 GitHub commits 90d386 GitHub stars0 vulnerabilities across 2 packagesOpenSSF score 3.2/10
See all signals from 8 sources
npm · vertica-nodejs@1.1.4 · PyPI · vertica-python@1.4.0
Alternatives to Vertica
The reviewed substitutes for Vertica among the cloud data warehouses, and what would make each one the better answer.
Direct alternatives
Reviewed substitutes: products bought for the same job, where a team picks one.
- Snowflake
- Choose Snowflake if your team wants a zero-maintenance warehouse with strong SQL analytics, governance features, and the ability to share live data across organizations without replication.Applies to: Choosing between two products of the same kind for one job.
- Amazon Redshift
- Choose Redshift if your organization is already invested in AWS and wants tight integration with services like SageMaker for ML, Kinesis for streaming, and S3 for data lake queries.Applies to: Choosing between these two for the cloud data warehouses decision.
- Teradata
- Two analytical warehouses serving the same central-store decision. They are compared for one budget, differ on pricing shape and deployment, and an organisation loads its data into one.Applies to: Choosing the warehouse that will hold the organisation's analytical data.
- Google BigQuery
- Choose BigQuery if you want true serverless analytics with no infrastructure to manage and your workloads align with a pay-per-query or slot-based model.Applies to: Choosing between these two for the cloud data warehouses decision.
- Azure Synapse Analytics
- Vertica and Azure Synapse Analytics both run large analytical SQL workloads as a central warehouse. Synapse is a managed Microsoft service that ties warehousing to Spark and data integration on Azure. Vertica is a columnar database a team installs on-premises or on any cloud, with advanced compression and in-database machine learning.Applies to: Choosing between these two for the cloud data warehouses decision.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- Databricks
- Choose Databricks if your workloads span data engineering, ML model training, and analytics on a single platform, and your team is comfortable with Spark-based processing.Applies to: Deciding whether analytical data is loaded into one platform or queried in open formats where it sits.
- MotherDuck
- Columnar SQL analytics for reporting and ad-hoc questions is the job of both MotherDuck and Vertica. Vertica is database software that a team installs and operates on its own hardware or cloud instances. MotherDuck is a fully managed serverless warehouse on top of DuckDB, which removes cluster work for lean teams.Applies to: For teams that would rather use a managed service than install and tune a columnar database themselves.
See detailed alternatives analysis
Vertica (now OpenText Analytics Database) is a columnar MPP analytics platform known for fast query performance, advanced compression, and in-database machine learning. With quote-based licensing options (enterprise license, DBaaS subscription, OEM) and a 10/10 rating from 30 reviews, Vertica has served enterprise analytics teams for years. However, the rise of cloud-native data warehouses, open-source OLAP engines, and lakehouse architectures has created compelling Vertica alternatives for teams seeking lower operational overhead, more flexible deployment models, or better price-performance at scale.
Top Alternatives Overview
Snowflake is the fully managed cloud data platform that separates compute from storage and runs across AWS, Azure, and Google Cloud. Snowflake eliminates infrastructure management entirely: there are no clusters to tune, no indexes to maintain, and no capacity planning required. It uses consumption-based pricing measured in credits, with Standard edition at approximately $2/credit and Enterprise at roughly $3/credit on-demand. Storage costs around $23 per compressed TB per month with pre-purchased capacity, with higher rates for on-demand usage depending on region. With an 8.7/10 rating from 455 reviews, users consistently praise its ease of use with standard ANSI SQL, automatic scaling for concurrent workloads, and zero-copy cloning for development environments. Choose Snowflake if your team wants a zero-maintenance warehouse with strong SQL analytics, governance features, and the ability to share live data across organizations without replication.
Databricks is the unified analytics and AI platform built on lakehouse architecture, combining data lake flexibility with warehouse structure. Built on Apache Spark with Delta Lake storage, Databricks provides collaborative notebooks, managed compute, and integrated ML tooling. Pricing uses Databricks Units (DBUs) that vary by workload type: DBU rates vary significantly by workload type, with Jobs compute being the most affordable and Serverless SQL the most expensive, plus underlying cloud infrastructure costs that typically add 50-200% on top of the DBU charges. With an 8.8/10 rating from 109 reviews, users highlight its strength in data engineering, machine learning workflows, and handling both structured and unstructured data. Choose Databricks if your workloads span data engineering, ML model training, and analytics on a single platform, and your team is comfortable with Spark-based processing.
Amazon Redshift is the fully managed, petabyte-scale cloud data warehouse from AWS. Like Vertica, Redshift uses columnar storage and massively parallel processing, but it integrates deeply with the AWS ecosystem including S3, Glue, SageMaker, and QuickSight. Redshift Serverless lets teams run analytics without provisioning infrastructure. With an 8.9/10 rating from 218 reviews, users praise its query performance, AWS ecosystem integration, and parallel processing capabilities. Choose Redshift if your organization is already invested in AWS and wants tight integration with services like SageMaker for ML, Kinesis for streaming, and S3 for data lake queries.
Google BigQuery is the serverless cloud data warehouse with pay-per-query pricing. BigQuery requires no cluster management, no index tuning, and no capacity planning. Its pricing model charges per TB of data scanned (first 1 TB free per month, then $5-$6.25 per TB) or through reserved capacity slots. With an 8.8/10 rating from 310 reviews, BigQuery stands out for its serverless architecture and deep integration with the Google Cloud ecosystem. Choose BigQuery if you want true serverless analytics with no infrastructure to manage and your workloads align with a pay-per-query or slot-based model.
ClickHouse is the open-source, column-oriented OLAP database built for real-time analytical reporting using SQL. ClickHouse is free and open-source with linear scalability across trillions of rows and petabytes of data. ClickHouse Cloud offers a managed serverless option. With a 7.1/10 rating from 9 reviews and over 46,900 GitHub stars, ClickHouse has strong community adoption for high-throughput analytical workloads. Choose ClickHouse if you need sub-second query latency on large analytical datasets and want an open-source foundation with no licensing costs.
Starburst is the enterprise analytics platform built on Trino (formerly PrestoSQL) that enables federated queries across data lakes, warehouses, and databases without moving data. Starburst offers a free tier with up to 3 clusters, a Pro tier starting at $0.50/credit, and an Enterprise tier starting at $0.75/credit. Choose Starburst if you need to query data across multiple heterogeneous sources (lakes, warehouses, databases) through a single SQL interface without consolidating everything into one platform.
Architecture and Approach Comparison
Vertica uses a shared-nothing MPP architecture with columnar storage, advanced compression algorithms, and projections (pre-sorted, pre-aggregated data structures) to deliver fast analytical query performance. It supports deployment on-premises, in the cloud, or on Apache Hadoop, and includes in-database machine learning capabilities. Vertica's architecture excels at large-scale batch analytics where data is loaded and then queried repeatedly, leveraging its K-safety protocol for fault tolerance and high availability.
Snowflake and BigQuery take fundamentally different architectural approaches by fully separating compute from storage. Snowflake uses virtual warehouses that can be independently scaled, paused, and resumed, with automatic optimization handling most performance tuning. BigQuery goes further with a fully serverless model where compute is allocated dynamically per query. Both eliminate the cluster sizing and node management that Vertica requires.
Databricks operates on a lakehouse architecture where data stays in open formats (Delta Lake, Apache Iceberg) on cloud object storage (S3, ADLS, GCS), and compute clusters process workloads on demand. This means storage costs scale independently from compute, and the same data can serve both data engineering pipelines and SQL analytics without duplication.
Redshift shares Vertica's columnar MPP heritage but adds Redshift Serverless for on-demand workloads and deep AWS integration for zero-ETL patterns. Redshift can query data directly in S3 through Spectrum, blending warehouse and data lake approaches.
ClickHouse uses a column-oriented storage format with vectorized query execution designed for maximum throughput on analytical workloads. Its MergeTree engine family provides real-time data ingestion alongside fast analytical queries, making it particularly strong for time-series and event analytics where Vertica's batch-oriented loading is less competitive.
Starburst's federated approach is architecturally distinct: it pushes queries down to data sources rather than requiring all data to be loaded into a central warehouse. This means you can query across Vertica, S3, PostgreSQL, and other sources simultaneously without data movement, which can also serve as a gradual migration bridge away from Vertica.
Pricing Comparison
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| Platform | Pricing Model | Entry Point | Mid-Range | Enterprise | Key Cost Driver |
|---|---|---|---|---|---|
| Vertica | Quote-based | Quote only | Enterprise license (custom) | OEM/ISV license (custom) | Compute hours + licensing |
| Snowflake | Credit-based | ~$2/credit (Standard) | ~$3/credit (Enterprise) | ~$4/credit (Business Critical) | Credits consumed + storage |
| Databricks | DBU-based | Jobs compute (lowest DBU rate) | All-Purpose compute (mid-range) | Serverless SQL (highest DBU rate) | DBUs + cloud infrastructure |
| Amazon Redshift | Instance/serverless | Serverless from $0.375/RPU-hr; provisioned from $0.543/node-hr | Provisioned (custom) | Reserved instances (custom) | Node hours or RPU-hours |
| Google BigQuery | Pay-per-query | 1 TB/month free | $5-$6.25/TB scanned | Capacity slots (custom) | Data scanned or slot hours |
| ClickHouse | Open Source / Cloud | Free (self-hosted) | Cloud (usage-based) | Cloud Enterprise (custom) | Infrastructure (self-hosted) |
| Starburst | Credit-based | Free (3 clusters) | $0.50/credit (Pro) | $0.75/credit (Enterprise) | Credits consumed |
Vertica publishes no prices; its licenses are quoted to the compute, storage, or transfer required, which suits teams with predictable, steady-state workloads. Cloud-native options like Snowflake and BigQuery offer more elasticity, scaling costs with actual usage rather than provisioned capacity. Databricks' dual-cost structure (DBUs plus cloud infrastructure) requires careful planning, as cloud provider charges typically add 50-200% beyond the DBU fees alone. ClickHouse offers the most cost-effective path for teams willing to self-manage, eliminating licensing costs entirely. Starburst's federated model can reduce total costs by avoiding data duplication across systems.
When to Consider Switching
Consider moving to Snowflake or BigQuery when infrastructure management overhead is consuming too much engineering time. Vertica requires capacity planning, node sizing, projection design, and ongoing cluster maintenance. Both Snowflake and BigQuery eliminate these operational burdens entirely, freeing data teams to focus on analytics rather than infrastructure.
Consider Databricks when your use cases extend beyond traditional SQL analytics into data engineering, machine learning, and AI workloads. Vertica's in-database ML covers some scenarios, but Databricks provides a complete platform for the full data and ML lifecycle, from ingestion through model serving, with native support for Python, R, Scala, and SQL.
Consider Amazon Redshift when your organization is deeply embedded in the AWS ecosystem. Redshift's zero-ETL integrations with Aurora, DynamoDB, and Kinesis, combined with Redshift Serverless for variable workloads, provide a migration path that keeps your data close to the rest of your AWS infrastructure.
Consider ClickHouse when your primary workload is high-throughput real-time analytics, especially time-series or event data, and your team has the engineering capacity to manage open-source infrastructure. ClickHouse can deliver sub-second query latency on billions of rows without licensing costs.
Consider Starburst when you need to query data across multiple systems (including an existing Vertica deployment) without consolidating everything. Starburst's federated architecture lets you keep data where it lives while providing a unified SQL query layer, which can serve as a gradual migration strategy rather than a hard cutover.
Migration Considerations
Vertica uses ANSI-compliant SQL, which simplifies migration to other SQL-based platforms like Snowflake, Redshift, and BigQuery. Most standard SQL queries translate directly, though Vertica-specific features like projections, flex tables, and Vertica-proprietary SQL functions will require refactoring. Plan for query audit and rewrite cycles, particularly for workloads that rely on Vertica's projection-based optimization.
Data export from Vertica is straightforward using standard COPY or EXPORT commands to formats like Parquet, ORC, or CSV. Cloud platforms universally support ingesting these formats from object storage. For large datasets, parallel export to S3 or cloud storage followed by bulk load into the target platform is the most efficient approach.
Snowflake and BigQuery migrations tend to be the smoothest because both platforms handle optimization automatically. You do not need to recreate projections, sort keys, or distribution keys. Redshift migrations require some attention to sort keys and distribution styles, though Redshift's automatic table optimization reduces this burden.
For organizations with complex Vertica deployments, consider a phased migration: use Starburst to federate queries across both Vertica and the new platform during the transition period, then migrate workloads incrementally. This avoids a disruptive big-bang cutover and lets teams validate query results against Vertica before decommissioning.
ETL pipelines connecting to Vertica via JDBC or ODBC generally require only connection string changes when pointing to a new platform. However, any code using Vertica-specific client libraries (vsql, vertica-python) will need to be updated to use the target platform's drivers and APIs. Budget for thorough regression testing of all downstream dashboards, reports, and applications that consume data from the warehouse.