Sold by: Matillion
Deployed on AWS
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Vendor Insights
Reduce manual data work. Deliver governed data for AI and analytics at scale.
Maia is an AI Data Automation platform from Matillion designed to remove the manual data work that slows AI and analytics delivery.
As AI and analytics demands grow, many data teams still rely on fragmented tooling and operational processes that slow delivery, increase maintenance overhead, and make it harder to scale production data delivery.
Organizations using Maia have reduced pipeline build times by up to 93% while improving delivery capacity and reducing operational overhead.
Maia securely connects through Matillions external application experience, helping organizations automate governed production data delivery while maintaining enterprise visibility, governance, and operational control.
4.5
Overview
What Maia does Maia brings together agentic automation, operational context, and enterprise infrastructure to deliver governed data pipelines at scale.
Maia Team An always-on team of AI agents that autonomously build, modify, and maintain production data pipelines across the delivery lifecycle.
Maia Foundation The enterprise automation backbone that provides the governed, cloud-native infrastructure required to automate production data delivery at scale.
Context Engine Captures business rules, governance standards, and operational knowledge so automated outputs remain aligned, reusable, and trusted over time.
Enterprise-ready operations Maia is designed for enterprise data environments where governance, operational visibility, and reliability matter.
Capabilities include: Git-compatible, production-ready pipeline output Integrated lineage and operational visibility Schema drift detection and remediation workflows Support for CI/CD and modern engineering practices Pushdown SQL architecture Integration with Snowflake services and AI capabilities Enterprise security and compliance support
Real-world outcomes Organizations use Maia to help constrained data teams scale AI and analytics delivery without increasing operational complexity at the same rate.
Examples include: Faster onboarding of new data sources and workflows Reduced manual maintenance effort Improved delivery capacity without proportional headcount growth Accelerated modernization of legacy data workflows
Highlights
- Accelerate AI and analytics delivery Eliminate the manual data work that slows production data delivery so teams can move AI and analytics initiatives from development to production faster.
- Modernize legacy data operations Replace fragmented tooling, brittle workflows, and manual processes with a unified, AI-powered delivery model.
- Reduce operational overhead Automate repetitive operational work across the entire data pipeline lifecycle so lean data teams can focus on higher-value initiatives instead of maintenance.
Details
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Buyer guide
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Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Free trial
Try this product free according to the free trial terms set by the vendor.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/unit |
|---|---|
Usage in cent of the product | $0.01 |
Dimensions summary
Matillion Data Productivity Cloud uses a consumption-based pricing model measured in credits. The platform charges based on actual compute resources consumed during data execution of data pipelines. Matillion's AWS Marketplace integration is carefully designed to allow billing through AWS without sacrificing the power of Matillion's usage aggregation and pricing. This is achieved by computing pricing on the Matillion side and reporting the total due to AWS Marketplace in the form of a usage-based charge that costs $0.01 per unit.
Top-of-mind questions for buyers like you
How does Matillion's credit-based pricing work on AWS Marketplace?
Matillion uses a consumption-based model where Credits are charged based on product usage. When Matillion charges are generated, either through Paygo or contract subscriptions, the total due is reported to AWS Marketplace in the form of a usage-based charge that costs $0.01 per unit.
How can I estimate my monthly usage costs?
Your monthly costs depend on the volume and complexity of your data workflows. Matillion provides monitoring tools to track credit consumption in real-time, and you can start with a small project to understand your usage patterns before scaling up operations.
Vendor refund policy
Refunds are not provided, but you can cancel at any time.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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.
Resources
Support
Vendor support
Matillion Support is designed to keep your data pipelines running smoothly and your teams productive. Every subscription includes standard Support with access to our highly qualified technical resources for issue resolution, a 24x7 support portal, and knowledge articles to help you troubleshoot and succeed.
For more demanding needs, Matillion offers Premium Support levels including Mission Critical and Mission Critical Plus. These services provide faster response times, proactive engagement, and direct access to a dedicated team of senior support experts. Customers can also benefit from Technical Account Managers (TAMs) who provide best-practice guidance, coordinate escalations, deliver health assessments, and help plan upgrades or new development projects.
Key capabilities include: 24x7 critical issue response with 1-hour SLA for Priority-1 cases. Accelerated response times for urgent issues (Priority-1 and Priority-2). Expert sessions with technical specialists to optimize use of Matillion. Support service reviews and proactive case monitoring to ensure timely resolution. Advanced services such as developer support, release guidance, and environment health assessments.
Matillion mission-critical Support gives you the peace of mind that your team can deliver reliable, AI-ready, analytics-ready data pipelines on time, with the confidence that experts are available whenever you need them.
Learn more at support.matillion.com
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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By Matillion
Sentiment is AI generated from actual customer reviews on AWS and G2
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AI generated from product descriptions
Agentic Automation
Autonomous AI agents that build, modify, and maintain production data pipelines across the delivery lifecycle
Schema Drift Detection
Automated detection and remediation workflows for schema drift in data pipelines
Git-Compatible Pipeline Output
Production-ready pipeline output with Git compatibility for version control and CI/CD integration
Integrated Data Lineage and Visibility
Built-in lineage tracking and operational visibility for data pipeline monitoring and governance
Pushdown SQL Architecture
SQL computation pushdown architecture for optimized query execution and performance
Codeless Visual Development Interface
Drag and drop visual UI for building data integrations without requiring coding, with pre-built templates and integration wizards to accelerate development
Parallel Data Integration Architecture
Highly scalable parallel data integration architecture with ETL and ELT pushdown optimization patterns for maximum throughput and performance into Amazon Redshift
Multi-Source Connectivity
Native connectors supporting hundreds of applications and data sources across on-premises and cloud environments including AWS services (Redshift, S3, RDS, Aurora) and enterprise applications (Salesforce, Workday, Oracle, SAP, ServiceNow)
FedRAMP Compliance
FedRAMP authorization with Integration Base, Data Integration service, and tiered connectors (Tier B, C, D) supporting regulated government cloud deployments
Data Integration and Synchronization
Capabilities for developing, running, and scheduling data integration flows, synchronization tasks, and data warehousing and data lake initiatives
AWS Data Source Integration
Secure connectivity to Amazon S3, Amazon Redshift, and Amazon RDS with push-down computation capabilities.
Elastic Compute Scaling
Distributed data and machine learning processing powered by Amazon EKS supporting Python, R, Spark, and additional frameworks.
AWS AI Service Integration
Pre-built workflows integrating AWS AI services including Amazon SageMaker and Amazon Comprehend for accelerated AI development.
Large Language Model Connectivity
LLM Mesh capability enabling connections to Amazon Bedrock for Chat, Retrieval-Augmented Generation (RAG), and Agentic workflows.
Visual Analytics and ML Interface
Low-code visual platform for data preparation, pipeline creation, and machine learning model development accessible to both technical and non-technical users.
Validated by AWS Marketplace
FedRAMP
GDPR
HIPAA
ISO/IEC 27001
PCI DSS
SOC 2 Type 2
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Standard contract
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Customer reviews
Srinivasan S.
Maia unlocks portal to the world of fully automated solution design
Reviewed on Jun 05, 2026
Review provided by G2
What do you like best about the product?
Overall, the product feels well designed and intuitive to use. I was genuinely impressed by how Maia could automatically pull user requirements from Jira/Azure DevOps and then begin building code to address the problem. Seeing the solution take shape in real time was an amazing experience, and it made the whole process feel smooth and surprisingly natural. The whole process hardly took a few minutes to complete as compared to what would have taken hours for someone manually solving the problem.
What do you dislike about the product?
In practice, not every solution will end up fully automated, and some items will still need to move through manual workflows. What hasn’t been clear to me is how to clearly distinguish or segment what’s being built by Maia versus what’s being handled manually within the native Jira/Azure DevOps platform.
What problems is the product solving and how is that benefiting you?
One of the main issues with developing BI and analytics solutions is that requirements are hard to interpret and often undergo multiple iterations of back and forth between developers and business users. Maia is able to use knowledge graphs to interpret the requirements and kick of automated builds that drastically cuts down the time spent between multiple iterations.
Vibhu S.
Easy, Reliable Pipelines with Maia Integration—But Missing Some METL Features
Reviewed on Jun 05, 2026
Review provided by G2
What do you like best about the product?
The pipelines are easy and reliable for doc. The integration with Maia helps build etl pipelines at least 40% better
What do you dislike about the product?
I don’t like the name and some of the components lack some of the good features what METL used to have.
What problems is the product solving and how is that benefiting you?
Building data pipelines efficiently
Higher Education
ETL Made Easy with a Clear, End-to-End Pipeline
Reviewed on Jun 05, 2026
Review provided by G2
What do you like best about the product?
ETL is made easy. By using simple words, the entire pipeline can be built clearly and end to end.
What do you dislike about the product?
I still need to explore it more to identify any shortcomings, but based on the demo I saw at the Snowflake Summit, it looks very promising.
What problems is the product solving and how is that benefiting you?
It simplifies the ETL process while also providing cataloging and orchestration through an AI agent.
Ian W.
Revolutionized Our ELT with AI Assistance
Reviewed on Jun 04, 2026
Review provided by G2
What do you like best about the product?
I really like the ability to use Maia’s AI tool for development. It’s a great help when I get stuck or encounter errors because instead of having to search online for solutions, I can just ask Maia to assist me in fixing the issue. Another thing I appreciate is how Maia helps test my pipelines, and when errors pop up, it aids me in debugging and resolving them.
What do you dislike about the product?
Sometimes you have to refresh the screen to be able to join components. Without doing that it doesn’t let you connect components.
What problems is the product solving and how is that benefiting you?
Maia connects to various data sources and loads data to Snowflake, while its AI tool helps me test pipelines and fix errors, improving productivity.
Keith G.
Maia Scaled 800+ Pipeline Migrations Without Added Overhead
Reviewed on Jun 04, 2026
Review provided by G2
What do you like best about the product?
Maia helped us scale delivery across 800+ pipeline migrations without adding overhead.
What stood out with Maia was how it helped us mature into a more robust CI/CD process rather than just improving individual pipelines. It enabled us to take generated transformations and integrate them into a structured Git-driven workflow, with consistent versioning, promotion, and automated actions. That shift is what made the approach scale across hundreds of pipelines instead of breaking down under volume. It also reduced the day-to-day operational overhead, which freed up time for more exploratory work instead of repetitive pipeline management.
What stood out with Maia was how it helped us mature into a more robust CI/CD process rather than just improving individual pipelines. It enabled us to take generated transformations and integrate them into a structured Git-driven workflow, with consistent versioning, promotion, and automated actions. That shift is what made the approach scale across hundreds of pipelines instead of breaking down under volume. It also reduced the day-to-day operational overhead, which freed up time for more exploratory work instead of repetitive pipeline management.
What do you dislike about the product?
A limitation is that Maia depends on having the right context and setup to be effective. Early on, working with limited context—before building out stronger skills and patterns—led to inconsistent results. It requires upfront investment in structuring context and workflows to get consistent outcomes.
What problems is the product solving and how is that benefiting you?
I was involved in evaluating and proving out Maia ahead of a migration of 800+ pipelines, including Informatica workloads, where the real challenge was operationalizing pipelines at scale.
Before Maia, getting pipelines production-ready is where most of the friction sat. Generated outputs didn’t fit cleanly into our CI/CD process, and aligning them with Git workflows took extra effort. That slowed promotion across environments and made it harder to keep changes consistent as volume increased.
With Maia, we were able to integrate generated pipelines into a Git-backed CI/CD workflow with automated actions. Instead of treating pipelines as one-off artifacts, we could version, iterate, and promote them consistently. Running deployments through native Snowflake app runners also kept execution aligned to Snowflake, which simplified data sovereignty and avoided introducing external dependencies. The result was a more repeatable delivery process—we were able to move a large volume of pipelines forward without adding proportional operational overhead.
Before Maia, getting pipelines production-ready is where most of the friction sat. Generated outputs didn’t fit cleanly into our CI/CD process, and aligning them with Git workflows took extra effort. That slowed promotion across environments and made it harder to keep changes consistent as volume increased.
With Maia, we were able to integrate generated pipelines into a Git-backed CI/CD workflow with automated actions. Instead of treating pipelines as one-off artifacts, we could version, iterate, and promote them consistently. Running deployments through native Snowflake app runners also kept execution aligned to Snowflake, which simplified data sovereignty and avoided introducing external dependencies. The result was a more repeatable delivery process—we were able to move a large volume of pipelines forward without adding proportional operational overhead.