Evidence tier Source Confirmed · 4 captures on record
What the publisher says
As described on AWS Marketplace.
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
Highlighted by the publisher on AWS Marketplace.
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.
Agent build and provenance
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The layer-by-layer build, the evidence behind each claim, the risk basis and the cross-marketplace links are open to any account. Some rows are disclosed, some the source leaves Unknown; a free account shows you which.
Plans and pricing as listed
3 listed- Units
- Units
- Units
Refund terms
As stated by the publisher on AWS Marketplace.
No refunds offered
Sources
Linked repositories
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