Buying snapshot
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| Decision point | What Yellowbrick states |
|---|---|
Deployment | Installs into your own AWS, Azure or GCP cloud account or an on-premises datacenter: your network, data on your own object storage, and a self-contained management plane. |
Where it runs | Public clouds, private clouds, data centers, laptops and the edge, on a Kubernetes cloud-native architecture. |
Day-to-day management | Suspend, resume, scale and monitor instances and compute clusters through SQL or a web app without dealing with Kubernetes, or with kubectl, the Yellowbrick operator and your own OCI registry. |
Support | Global 24x7 support is always included, with no additional support charges. |
Authentication and access | OAuth2, database-local authentication, or an external identity provider including legacy LDAP; role-based access control, columnar data encryption and end-to-end network encryption, with partnerships with Protegrity and Immuta. |
JDBC driver | MIT-licensed driver that extends the PostgreSQL JDBC driver with OAuth2 device-flow login; uses the jdbc:yb: prefix, with jdbc:postgresql: kept for legacy compatibility. |
Our take
Shortlist Yellowbrick if your warehouse data must stay in your own cloud account or data center and you run mixed SQL workloads at scale. In the public cloud, it is a weaker fit if you want one vendor to run and bill the whole stack: Yellowbrick sells the software, and the compute and storage run in your own cloud account. On-premises buyers can also buy infrastructure subscriptions from Yellowbrick.
Evaluate Yellowbrick Data
Popular comparisons
See all 7 Yellowbrick Data comparisons
Where it stands out
Mixed workloads on one platform. Workload management keeps long-running jobs from delaying interactive queries and stops bad queries from taking resources or overrunning cost budgets. With separate storage and compute, you can put loads and queries on dedicated compute clusters and load-balance across them.
Streaming and batch in the same tables. A hybrid row-column store handles real-time ingest, bulk loads and columnar queries with ACID transactions, and the column store persists data on object storage. Yellowbrick says the row store commits streaming inserts from Kafka, AirByte, Informatica and other ETL and CDC tools in microseconds.
A path off legacy warehouses. Yellowbrick looks like PostgreSQL and adds SQL extensions for Teradata, Oracle, Redshift, SQL Server and others. Automated tooling, Next Pathway, Datometry and systems integrators cover discovery, cost and project assessment, moving ETL, BI, objects and data, testing and post-migration support.
Hybrid disaster recovery. Asynchronous replication of data and DDL across instances and clouds supports failover, failback and hot standby replicas, plus full, incremental and cumulative backups. You can run the primary on-premises with a live DR instance in the cloud.
Performance claims to test. Yellowbrick claims the lowest cost per query in the industry from LLVM-accelerated execution and its Direct Data Accelerator, and says it scales from one user with gigabytes to thousands of concurrent users with petabytes. These are vendor claims, so check them on your own workload.
Product screenshot
Trade-offs to weigh
Two bills to forecast in the cloud. You pay your own cloud storage and compute through your own contracts and credits, and pay Yellowbrick only for its software. Your budget needs both parts.
A subscription is a vCPU ceiling, not a bank of hours. You pay a flat rate for use up to the vCPU maximum you chose, and unused vCPU hours do not roll forward. Yellowbrick says a subscription makes sense when a cluster runs more than 25% of the time.
Node changes move the price. The vCPU count in use is the only factor in the software cost, so a node-size change that changes vCPUs can change what you pay.
OAuth2 login needs setup and care. The OAuth2 JDBC driver requires a one-time CREATE EXTERNAL AUTHENTICATION setup in SQL by a privileged administrator. Its token cache file holds OAuth2 tokens that could let an attacker act as the user if the file is not secured.
Pricing and plan conditions
Yellowbrick prices its data warehouse software per vCPU. You choose a subscription, on-demand use, or both: Yellowbrick says customers often use on-demand for periodic ETL or bursting and a subscription for 24/7 query processing.
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| Plan | Published price | Billing terms |
|---|---|---|
Subscription, one year | $613 per vCPU / yr | Predictable value; your cloud or on-prem costs are paid separately |
Subscription, three year | $482 per vCPU / yr | Guaranteed 3-year price |
On-demand (burst pricing) | $0.28 per vCPU / hr | Per-second metering, billed monthly |
On-premises infrastructure subscription | Contact sales@yellowbrick.com | Infrastructure bought from Yellowbrick for on-premises use |
Public signals
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Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
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Alternatives
Yellowbrick positions itself against Amazon Redshift, saying customers moved to it over Redshift's concurrency and scalability challenges. It pitches itself as an add-on to Databricks for complex, high-volume workloads rather than a replacement. It also features NAVSUP's migration from IBM Netezza, which makes it a candidate for teams leaving that platform.
Alternatives to Yellowbrick Data
The reviewed substitutes for Yellowbrick Data 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.
- Azure Synapse Analytics
- As an enterprise SQL warehouse for BI and ad-hoc analytics, Azure Synapse Analytics and Yellowbrick Data do the same work. Synapse is a managed Azure service that bundles warehousing with Spark and data pipelines. Yellowbrick runs on Kubernetes in public clouds, private clouds or a company's own data center, which suits teams that need their data to stay under their own control.Applies to: Choosing between these two for the cloud data warehouses decision.
- Exasol
- For a fast enterprise warehouse that stays under a team's own control, Exasol and Yellowbrick Data are both candidates. Exasol puts the emphasis on in-memory, massively parallel query speed for analytics. Yellowbrick is built on Kubernetes and stresses deployment in private clouds and data centers for data residency and sovereignty.Applies to: Choosing between two products of the same kind for one job.
- Firebolt
- Firebolt and Yellowbrick Data are SQL warehouses aimed at fast analytics on large data sets. Yellowbrick runs on Kubernetes and can be deployed in a company's own data center or private cloud as well as in public clouds. Firebolt is a managed cloud service, which puts the operations work on the vendor and the focus on low-latency queries.Applies to: Choosing between these two for the cloud data warehouses decision.
- Vertica
- Vertica and Yellowbrick Data each give enterprises a SQL warehouse they can deploy in their own data centers or in the cloud. Yellowbrick is built on Kubernetes and stresses keeping data under the customer's control for residency rules. Vertica stresses columnar compression and in-database machine learning.Applies to: Choosing between these two for the cloud data warehouses decision.
- Teradata
- Enterprises that want a data warehouse in public clouds, private clouds or their own data center can pick Teradata or Yellowbrick Data. Teradata is the long-established platform for very large, mixed workloads. Yellowbrick runs on Kubernetes and stresses data sovereignty and a private-cloud experience under the customer's control.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.
- Snowflake
- Both are MPP SQL warehouses evaluated for the same enterprise analytics workload. The difference in kind is who runs it: Yellowbrick is a SQL data platform deployed on your own Kubernetes, including private cloud and on-premises, while snowflake is consumed as a fully managed service. That is the same distinction that made Snowflake and Vertica a conditional pair rather than a direct one.Applies to: Enterprise SQL analytics on a central warehouse: BI dashboards, ad-hoc exploration and scheduled transformation over the same governed dataset. Choose Yellowbrick when the warehouse must run inside your own infrastructure or Kubernetes estate; choose snowflake when a managed service is acceptable.
- Amazon Redshift
- Both are MPP SQL warehouses evaluated for the same enterprise analytics workload. The difference in kind is who runs it: Yellowbrick is a SQL data platform deployed on your own Kubernetes, including private cloud and on-premises, while redshift is consumed as a fully managed service. That is the same distinction that made Snowflake and Vertica a conditional pair rather than a direct one.Applies to: Enterprise SQL analytics on a central warehouse: BI dashboards, ad-hoc exploration and scheduled transformation over the same governed dataset. Choose Yellowbrick when the warehouse must run inside your own infrastructure or Kubernetes estate; choose redshift when a managed service is acceptable.
- Google BigQuery
- Both are MPP SQL warehouses evaluated for the same enterprise analytics workload. The difference in kind is who runs it: Yellowbrick is a SQL data platform deployed on your own Kubernetes, including private cloud and on-premises, while google-bigquery is consumed as a fully managed service. That is the same distinction that made Snowflake and Vertica a conditional pair rather than a direct one.Applies to: Enterprise SQL analytics on a central warehouse: BI dashboards, ad-hoc exploration and scheduled transformation over the same governed dataset. Choose Yellowbrick when the warehouse must run inside your own infrastructure or Kubernetes estate; choose google-bigquery when a managed service is acceptable.
- Databricks
- For the central cloud data warehouse, Databricks and Yellowbrick Data can both carry the SQL and BI workload. Yellowbrick is a dedicated SQL warehouse on Kubernetes that can run in private clouds and data centers for data sovereignty. Databricks puts the warehouse on a lakehouse over cloud object storage, with Spark pipelines and ML in the same platform.Applies to: Choosing between these two for the cloud data warehouses decision.
- MotherDuck
- MotherDuck and Yellowbrick Data each offer a SQL warehouse for BI and ad-hoc analytics. Yellowbrick is built for enterprises that need to deploy in their own data centers or private clouds to keep data in place. MotherDuck is a serverless cloud service built on DuckDB, with hybrid execution between a laptop and the cloud.Applies to: For analytics teams that need a serverless cloud warehouse and do not have to keep data on their own hardware.