Looking for a Metaplane alternative?
Move to a dedicated data reliability layer, without rebuilding from scratch.
Elementary brings monitoring, tests, lineage, incidents, and reliability context into one place, so data teams can understand what’s happening with their data and give people and AI agents confidence to act on it.
Already using Metaplane? Elementary can help handle the migration, from mapping your existing monitoring setup to getting your warehouse and integrations connected.
Talk to us about migrating from Metaplane →
What happened to Metaplane?
Datadog acquired Metaplane in April 2025. Metaplane continues to support existing and new customers, while Datadog has been bringing data observability into its broader observability platform. Datadog now offers Data Observability alongside its application, infrastructure, jobs, and data streams monitoring products.
So if you're evaluating Metaplane alternatives, the question isn't necessarily whether Metaplane is going away.
It's whether you want data observability to become part of a broader software observability platform, or whether you want a dedicated reliability layer built around your data stack and the people and AI systems consuming that data.
That's where Elementary takes a different path.
Why teams consider Elementary instead of Metaplane
1. Go beyond detecting problems to understanding what data can be trusted
Both Elementary and Metaplane can monitor data for problems like freshness, volume, schema changes, nulls, and unexpected behavior. Both provide lineage and incident management.
Elementary's goal is to bring those signals together into a reliability layer.
Tests, automated monitors, lineage, incidents, ownership, usage, and pipeline context live together, so teams can understand not only that something failed, but what happened, what's affected, and whether the data can still be trusted.
That context becomes useful beyond the observability dashboard too. It's a source of truth that data teams, business users, and AI systems can use when they need to understand the current state of the data.
2. Build reliability your way
Elementary started in the dbt ecosystem, but dbt isn't required.
You can connect Elementary directly to your warehouse, combine that coverage with tests running in dbt and Python pipelines, and add Cloud Tests that run independently of either pipeline.
That gives teams one place for:
- Automated freshness and volume monitoring
- dbt tests
- Python pipeline tests
- Configured data quality tests
- Custom SQL tests
- Data contracts
- No-code test configuration
- AI-assisted reliability workflows
Elementary keeps the engineering workflows that data teams want, while also making it possible to configure and manage reliability from the UI.
It's code-first when you want it, without being code-only.
3. Make reliability available to people and AI agents
As more people consume data through AI, detecting broken pipelines is only part of the problem.
An AI agent can generate perfectly valid SQL against data that is late, incomplete, or currently affected by an upstream incident.
Elementary's MCP Server makes reliability context available directly to AI tools and agents. They can access information such as data health, lineage, test coverage, and active incidents before acting on the data.
The same reliability layer used by the data team can therefore answer a different kind of question:
Can I trust this data right now?
Elementary Runtime extends that model further. When a reliability or AI workflow requires access to the underlying data, supported tasks can execute inside the customer's environment while Elementary Cloud orchestrates the workflow. Sensitive data and credentials stay inside the customer environment.
4. Scale coverage without scaling complexity
Pricing matters when observability expands beyond a small set of critical tables.
Metaplane's pricing is based on monitored tables, and Datadog currently prices Data Observability Quality Monitoring per monitored table. Datadog lists Quality Monitoring at $16 per monitored table per month on an annual plan.
Elementary uses a more predictable capacity-based model.
Plans have a base price with included table capacity, and additional coverage is added in increments of 1,000 tables.
That means teams don't have to think about the incremental cost of adding a monitor to every individual table.
If the goal is to expand reliability coverage across a large warehouse, pricing shouldn't force you to decide which assets are worth keeping an eye on.
The core observability capabilities overlap significantly.
The bigger difference is what you want the platform to become part of.
Already using Metaplane? We'll help you move.
Switching data observability platforms shouldn't mean rebuilding years of monitoring work from scratch.
Elementary can work with your team to map your existing Metaplane setup, migrate the relevant monitoring and reliability workflows, connect your warehouse and integrations, and validate your coverage before you move over.
The goal is to make switching a migration project, not a new implementation project.
Talk to us about your Metaplane migration →
When Metaplane and Datadog may be the better fit
If your organization is already deeply standardized on Datadog and wants data quality tightly integrated with application, infrastructure, and software observability, Datadog's direction may be a natural fit.
That connection is one of the explicit goals behind the Metaplane acquisition: connecting data quality with the systems and applications upstream from it.
When Elementary may be the better fit
Elementary is worth evaluating if you want a dedicated reliability layer centered on the data stack itself.
Especially if you want to:
- Combine warehouse monitoring with dbt, Python, and independent data quality tests
- Keep reliability workflows in code where it makes sense without requiring everything to live there
- Expand monitoring without paying for every individual monitored table
- Retain an open-source option
- Run deeper reliability workflows securely inside your own environment
- Make reliability context available to people and AI agents
- Get help migrating your existing Metaplane implementation
The aim isn't simply to create another place where the data team gets alerts.
It's to create one place to manage data reliability and one source of truth for what data can be trusted.
Frequently asked questions
Is Metaplane shutting down?
There is no public announcement that Metaplane is shutting down.
Datadog acquired Metaplane in April 2025. At the time, Metaplane said it would continue as a standalone product under "Metaplane by Datadog," while the companies worked toward a unified vision for software and data observability. Datadog also said Metaplane would continue supporting existing and new customers.
Who owns Metaplane?
Datadog acquired Metaplane on April 23, 2025. The acquisition is part of Datadog's expansion into data observability.
Can Elementary replace Metaplane?
For many teams, yes.
There is substantial overlap across warehouse monitoring, anomaly detection, data quality tests, lineage, alerting, and incident management.
Elementary can also work with teams migrating from Metaplane to map existing monitoring and reliability workflows into Elementary rather than requiring them to start over.
Does Elementary require dbt?
No.
Elementary can connect directly to your warehouse and supports reliability workflows with or without dbt. If you use dbt, Elementary can use its tests, lineage, and metadata as additional context. If you don't, Elementary builds the reliability layer from the rest of your stack.
How is Elementary's pricing different from Metaplane?
Metaplane uses a monitored-table pricing model. Datadog's current Quality Monitoring product is also priced per monitored table.
Elementary starts with a base plan that includes table capacity, with additional capacity added in increments of 1,000 tables. That makes costs more predictable as reliability coverage expands across the warehouse.
What are the main Metaplane alternatives?
The data observability market includes platforms such as Elementary, Monte Carlo, Sifflet, Anomalo, and Soda.
The right alternative depends on why you're considering a change. If you're looking for a dedicated data reliability platform that combines code and UI workflows, predictable scaling, an open-source foundation, and reliability context for AI systems, Elementary is designed around that approach.
Considering a move from Metaplane?
See how your current Metaplane setup maps to Elementary and what migration would look like for your team.
We'll help you move your monitoring, connect your stack, and make sure you don't have to start from zero.
