Sold by: Elementary Data
Elementary is the control plane for data and AI reliability. It unifies observability, quality, governance, and discovery, enabling teams to deliver trusted data at scale in the AI era.
4.5
Overview
Elementary Cloud is the Enterprise Data & AI Control Plane that brings data engineering, analytics, and data science into one place to scale trusted data and AI products. Instead of siloed solutions, it offers observability, quality, governance, and discovery tools with a single platform built on a shared context engine. Elementary is designed for modern stacks where end-to-end reliability is key. It captures context from dbt and Python pipelines, all major cloud warehouses and lakes, orchestrators, data visualisation tools, AI systems, and more. At the core, the context engine continuously learns from lineage, validations/test results, incidents, performance signals, semantics, and usage patterns, so every workflow, and every AI agent, operates on the same, up-to-date understanding of what your data is, how it is built, and whether it is healthy. Elementary is built on an open core, and metadata stays in the customer environment and can be extended or leveraged to build your own workflows. On top of this shared context, Elementary adds a network of AI agents that take on operational reliability work while teams keep ownership and approvals, such as: triage & resolution, test/monitor coverage, performance & cost optimization, pre-merge prevention workflows. Agents even propose changes via pull requests as part of a code-first approach. Elementary also exposes this context through the Elementary MCP Server (Model Context Protocol) so MCP-compatible clients and copilots can query incidents, lineage, coverage, and health signals directly from where teams work (IDE, assistants, internal tools). For enterprise needs, Elementary Cloud includes organization-wide collaboration workflows (catalog + governance), centralized incident management, and enterprise controls (e.g., RBAC/audit logs). For offers, volume discounts, custom pricing, or private contracts and security questions, please reach out at contactus@elementary-data.com .
Highlights
- One control plane for observability, governance, and discovery: Unify metadata, lineage, validations, incidents, and health signals into a shared context engine so every team and workflow operates on the same source of truth - without bouncing between siloed tools.
- AI agents that do the heavy lifting (with human guardrails): Automate triage and resolution, recommend coverage based on downstream impact, optimize performance & cost, and integrate into dev workflows via PRs and MCP, so reliability becomes proactive, not reactive.
- End-to-end reliability across SQL, Python, and AI pipelines: Capture context from all workloads and monitor assets across major warehouses and lakes. Use an extensive suite of automated and customized monitors and tests, while also leveraging existing testing frameworks like dbt, Great Expectations and DQX.
Details
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Deployed on AWS
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Pricing
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
Elementary Platform | Subscription to Elementary Platform starting at | $120,000.00 |
Vendor refund policy
No refunds offered
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Support
Vendor support
Reach out to us at support@elementary-data.com . Your support SLA and terms will be included in your plan and contract.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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Customer reviews
Financial Services
Data Observability made simple
Reviewed on Feb 21, 2025
Review provided by G2
What do you like best about the product?
- Seamless integration with dbt, via the open source package which is super easy to set up
- Webinars to present the tool are very effective
- Documentation site is great
- Cool features of the Cloud paid offering: example column level lineage and integration with other tooling you might have (BI Tool, Data Catalog); possibility to bulk set tests
- Possibility to tailor and customise alerts; warnings in dbt do not become silent, but are still tracked and monitored
- Elementary Data Report gives you a pulse on your data quality status in your dbt project keeping track of the history and making it accessible outside of the data org, to the whole business
- Anomaly detection package is an amazing way to integrate stats (and AI in the cloud offering) to time series monitoring
- Webinars to present the tool are very effective
- Documentation site is great
- Cool features of the Cloud paid offering: example column level lineage and integration with other tooling you might have (BI Tool, Data Catalog); possibility to bulk set tests
- Possibility to tailor and customise alerts; warnings in dbt do not become silent, but are still tracked and monitored
- Elementary Data Report gives you a pulse on your data quality status in your dbt project keeping track of the history and making it accessible outside of the data org, to the whole business
- Anomaly detection package is an amazing way to integrate stats (and AI in the cloud offering) to time series monitoring
What do you dislike about the product?
- Only integrates with dbt, so not available for other Data Trasformation tools (yet)
What problems is the product solving and how is that benefiting you?
- Auditing dbt projects data quality over time
- Auditing dbt models runtime over time
- Overview of data quality in your dbt project at a glance with the Elementary Data Report
All of this enable more visibility on data quality and track bottlenecks and issues in your data pipelines.
The dbt package is open source and gives already lots of benefits.
- Auditing dbt models runtime over time
- Overview of data quality in your dbt project at a glance with the Elementary Data Report
All of this enable more visibility on data quality and track bottlenecks and issues in your data pipelines.
The dbt package is open source and gives already lots of benefits.
Jing W.
Advanced Our Data Quality by a Big Step
Reviewed on Feb 12, 2025
Review provided by G2
What do you like best about the product?
1. Made data quality measurable and transparent through its dashboard.
2. Significantly reduced the effort required to build and maintain a robust data monitoring and observability system.
3. Amplified the impact of data validation in DBT by tracking validation history and seamlessly integrating with our alerting system.
4. Fast response to any issue and inqueries.
2. Significantly reduced the effort required to build and maintain a robust data monitoring and observability system.
3. Amplified the impact of data validation in DBT by tracking validation history and seamlessly integrating with our alerting system.
4. Fast response to any issue and inqueries.
What do you dislike about the product?
It would be helpful to include information on the underlying mechanisms, such as how data freshnes anamoly were checked.
What problems is the product solving and how is that benefiting you?
Built a new data platform under a highly constrained timeline, ensuring scalability, reliability, and efficient data processing while meeting critical business needs.
Facundo V.
Growing Data Governance tools
Reviewed on Jan 30, 2025
Review provided by G2
What do you like best about the product?
- Simplicity
- Ease of use
- Dashboards Analytics
- Ease of use
- Dashboards Analytics
What do you dislike about the product?
- No integration with Metabase
- Not Ai tool to docs
- Not Ai tool to docs
What problems is the product solving and how is that benefiting you?
Understanding relationships between data and ensuring good data quality
Christophe O.
A solid platform to add observability on your dbt workloads
Reviewed on Jan 27, 2025
Review provided by G2
What do you like best about the product?
If you're using dbt, Elementary data is great addon to your data stack to monitor your queries, test coverage and report failures on Slack. The cloud version adds quality of life improvements to the OSS version.
It's fairly easy to plug in your dbt project and the different options to be added to the alerts are helpful to do some triage. The community and team is helpful and provide some support on the company's Slack.
It's fairly easy to plug in your dbt project and the different options to be added to the alerts are helpful to do some triage. The community and team is helpful and provide some support on the company's Slack.
What do you dislike about the product?
The platform is adding metadata through pre-hooks and post-hooks in dbt which slows down a bit your workflow.
The Slack alert template has little customization support and though it's OK, I think it could a bit more readable.
The Slack alert template has little customization support and though it's OK, I think it could a bit more readable.
What problems is the product solving and how is that benefiting you?
Elementary Data helps to cover dbt test reporting on Slack, dbt test coverage reporting and dbt model lineage.
Alonso A.
Effortless Data Quality Monitoring Made Simple
Reviewed on Jan 27, 2025
Review provided by G2
What do you like best about the product?
Elementary Data excels in simplifying the process of data quality monitoring. Its intuitive interface and seamless integration with modern data stack make it a standout tool. I particularly appreciate the anomaly detection and detailed insights it provides, which help identify and resolve data issues before they escalate. Additionally, the visual reporting dashboards are both user-friendly and highly informative, enabling stakeholders to quickly grasp key metrics without needing deep technical expertise.
What do you dislike about the product?
I would say that some sections of the UI are not clear enough and I hope Metrics feature and their automatic anomaly detection could improve soon, but so far I am very confortable with the tool.
What problems is the product solving and how is that benefiting you?
Anomaly detection for models, visualization and management of incidents with data quality, including alerting, etc.