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Matillion

Cloud-native ELT and transformation for cloud data platforms, with AI agent Maia

Overview

Matillion was founded in 2011 by Matthew Scullion and Ed Thompson in Manchester, England, and now operates dual headquarters in Denver and Manchester[11]. Its platform is a visual pipeline designer that pushes transformation work down into the customer’s cloud data platform rather than running a separate processing engine. Matillion names Cisco, DocuSign, St. James’s Place, Slack and TUI among its customers[11]. The company also offers Maia, an AI agent for building pipelines, which can be trialled on a time-bound, fair-use basis[10].

Matillion sells three plans, Developer for individuals, Teams for growing businesses and Scale for large organisations, and charges in Matillion Credits[10]. Credits are consumed by task hours, which are billed only while a pipeline runs (validation and sampling are not charged), and by developer users beyond the number included in a plan; fixed annual packages with included credits are also available[10]. No credit rate or plan price is published, so cost comparisons need a sales conversation. The pushdown design keeps data inside the warehouse, but it also means performance and compute cost depend on the warehouse that executes the generated SQL.

What Matillion does

  • Visual low-code pipeline designer
  • Pushdown transformation into the cloud warehouse
  • Maia AI agent for pipeline building
  • Task-hour billing only while pipelines run
  • Developer, Teams and Scale plans
  • Low-code canvas with SQL and Python
  • Pre-built connectors
  • Built-in Git repository

Matillion pricing

Credit-based (Matillion Credits) consumed by task hours and additional developer users; fixed annual packages with included credits. Plans: Developer (try free), Teams and Scale (contact sales). No list prices published.

Matillion strengths and limitations

Strengths

  • Visual design suits mixed-skill teams
  • Billing only when pipelines run
  • Free Developer entry point
  • Pushdown avoids moving data out of the warehouse
  • Visual design with code options
  • Push-down transformation in warehouse

Limitations

  • No published credit price
  • Compute costs land on the warehouse bill as well
  • Fewer prebuilt SaaS connectors than ELT specialists
  • Credit model needs monitoring
  • Credit prices not published
  • Focused on cloud data platforms

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