Mage AI
UnclaimedRun AI-ready data engineering workflows that are current, reliable, and reusable.
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TL;DR - Mage AI
- Automates data pipelines and AI workflows for production with unified execution.
- Transforms workflows into reusable, versioned, and reliable AI-ready data outputs.
- Offers AI assistance for building, debugging, and optimizing data workflows.
Pricing: Paid only
Best for: Enterprises & pros
Pros & Cons
Pros
- Ensures data reliability and reproducibility for AI systems.
- Accelerates workflow development with AI assistance and automation.
- Reduces operational overhead and total cost of ownership by consolidating tools.
- Supports a wide range of data sources and programming languages (SQL, dbt, Python, R).
- Offers flexible deployment options to meet various infrastructure and compliance needs.
Cons
- Pricing model based on block runs and AI tokens may require careful monitoring for cost management.
- The platform's advanced features might have a learning curve for new users.
Key Features
Unified execution for ingestion, transformation, and automationModular runtime for isolated workflow units and contained failuresVersioned and addressable data outputs with preserved execution historyNative batch, sync, and streaming data processingSchema-aware ingestion and validationAI-assisted workflow creation with natural languageAI-assisted code generation, refactoring, and optimizationAutomatic validation and debugging with AI suggestions
Pricing Plans
Starter
$100/mo
- 50K AI tokens
- Assisted coding and suggestions
- 1+ cluster
- Prod to build and run workflows
- AI sidekick
- Context-aware coding and instant debugging
Team
$500/mo
- Run up to 15,000 blocks /mo
- Prototypes and light workloads
- AI sidekick
- Context-aware coding and instant debugging
- 250K AI tokens
- Assisted coding and suggestions
- 1+ cluster
- Prod to build and run workflows
- 2+ workspaces
- For collaborative team development
Plus
$2,000/mo
- Run up to 50,000 blocks /mo
- Automate your data stack
- AI sidekick
- Increased limits, faster responses, and more
- 2M AI tokens
- Build faster with smart AI sidekick
- 2+ clusters
- Dev and prod for safer workflows
- 6+ workspaces
- A space for each pro
Business
$5,500/mo
- 200k block runs per month
- 10M AI tokens per month
- 9.5k core hours per month
- 46.5k GB hours per month
- 3+ clusters
- 15+ workspaces
Enterprise
$25,000/mo
- 700k block runs per month
- 50M AI tokens per month
- 32.5k core hours per month
- 235k GB hours per month
- 8+ clusters
- 100+ workspaces
About Mage AI
By Toolradar Team·
Mage AI is a data engineering platform designed to automate pipelines and AI workflows for production environments. It provides a unified execution system for ingestion, transformation, and automation, ensuring reliability and reproducibility. Mage AI focuses on creating AI-ready data by turning workflows into reusable, versioned data outputs with preserved execution states and history, making them trustworthy for downstream applications.
The platform caters to data-driven teams looking to build internal platforms, run critical systems, and power AI systems with reliable data. It integrates seamlessly into existing data stacks, connecting to various data sources and allowing execution of SQL, dbt, Python, and R code. Mage AI also incorporates AI assistance for workflow creation, code generation, debugging, and validation, aiming to accelerate development and reduce operational overhead.
How we evaluate tools →Source: mage.ai
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Mage AI FAQ
How does Mage AI ensure data reliability and reproducibility for critical production systems?
Mage AI achieves reliability by running workflows as isolated, modular units with explicit inputs and outputs, containing failures. Each data output is backed by a preserved execution state and history, allowing for inspection, reproduction, and recovery as data and logic evolve. Outputs are also versioned and addressable, ensuring downstream workflows and AI agents can reuse trusted context.
What specific AI capabilities does Mage AI offer to assist in building and managing data workflows?
Mage AI provides an AI sidekick that enables workflow creation using natural language descriptions. It offers AI-assisted code generation, refactoring, and optimization. The AI also performs automatic validation and debugging, spotting errors, ensuring correctness, and suggesting fixes, and guides updates as data and requirements change.
Can Mage AI integrate with existing data warehouses and tools like dbt?
Yes, Mage AI is designed to fit cleanly between data sources and consumers. It can ingest data from databases, warehouses, lakes, SaaS tools, and APIs. It supports running SQL, dbt, Python, and R code within its execution environment, allowing teams to produce trusted metrics across these workflows.
What are the deployment options available for Mage AI, particularly for organizations with strict data residency requirements?
Mage AI offers several deployment options: a fully managed cloud service, hybrid cloud (control plane in Mage's cloud, data processing in private cloud), private cloud (full platform in your cloud), and on-premises deployment in your data center. These options, along with regional deployment choices and custom region availability, cater to data residency and sovereignty needs.
How does Mage AI's 'block run' metric for pricing work, and when do additional compute charges apply?
A 'block run' is one execution of a modular step within a pipeline. If a pipeline has 5 blocks and runs once, it counts as 5 block runs. On-demand compute charges (billed per CPU or RAM hour) primarily apply when using the Kubernetes (k8s) executor for jobs requiring more than 8GB RAM or horizontal scaling. The default local_python executor typically incurs no additional usage costs.
Source: mage.ai