Matterbeam
a company-wide write-ahead log for your data
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What is Matterbeam?
Matterbeam is a Data Agility Platform that automates data flow through your business by replacing traditional one-way data pipelines with a live data movement layer. Instead of building separate pipelines for each use case, you collect data once from any source (databases, APIs, cloud services) into immutable, time-ordered streams, then emit it anywhere using unlimited emitters. The platform supports real-time streaming, historical data replay, in-transit transforms, and branching for multiple destinations without re-reading source systems.
Key features include replayable logs that let you backfill, rebuild, and fork data streams; a searchable catalog with data lineage showing where data came from and where it goes; automated schema detection and learning; multi-step transform composer with Python user-defined transforms; stream joins for stateful transformations; parallel sync for zero-downtime data migrations; and native integrations with Amazon Redshift, Google Analytics, HubSpot, Kafka, MySQL, Pinecone, Salesforce, Shopify, Snowflake, and AWS SNS. The platform is built on AWS with a serverless architecture for elastic scale.
Matterbeam is designed for data engineering teams, data scientists, and companies building AI/ML initiatives who are frustrated with months-long data project timelines, brittle pipelines that break on schema changes, and expensive record-based pricing from traditional tools like Fivetran or Hevo. It enables data scientists to access historical training data instantly via time travel, helps teams cut pipeline costs by 50-70%, and reduces data project delivery from 6 months to 2 weeks.
Matterbeam pricing
Pricing model: Freemium
Matterbeam uses transparent, simple pricing with three components: (1) Bulk load your data is FREE with no storage charges. (2) Stream, replay, or transform costs $25 per GB per month - charged only for data in motion. (3) Emit live datasets anywhere is FREE. There is no record-based pricing or active vs inactive surprises. Matterbeam charges only on GB of data movement, aligned with work actually done. For custom arrangements and design partners, contact sales to discuss needs and prove value before committing.
Matterbeam pros
- Collect data once, use infinitely for unlimited use cases
- Replay historical data without re-reading production systems
- Time travel to any point in data history for audits or recovery
- Self-healing pipelines that recover without rebuilds
- Serverless architecture with elastic scale up and down
- Transparent GB-based pricing with no record math surprises
- Free bulk data loading with no storage charges
- Free live data emission to any destination
- Multi-step transform composer with data previews
- Custom user-defined Python transforms for complex logic
- Stream joins enable stateful transformations
- Automated schema detection and learning
- Searchable catalog with full data lineage tracking
- Real-time streaming keeps data fresh between batches
- Zero-downtime data migrations with parallel sync
- Roll back instantly if migration issues occur
- 50-70% lower costs compared to Fivetran and Hevo
- Works alongside existing tools without replacement
- Unlimited emitters from single collector avoids triple billing
- AI teams ship models in 2 weeks instead of 6 months
Matterbeam cons
- No free trial available, requires contacting sales for custom arrangements
- Still in early access with actively developing features
- Some integrations like HubSpot and Pinecone are in beta
- Pricing at $25/GB for streaming/replaying/transforming can be expensive at high volumes
- Requires custom sales conversation for pricing arrangements
- Platform fee is flat monthly in addition to data movement charges
- Newer platform founded in 2022 with smaller ecosystem than established tools
- Limited public documentation compared to mature data platforms
- Not suitable for teams needing simple no-code ELT without replay needs
Frequently asked questions about Matterbeam
How is Matterbeam different from traditional data pipelines?
Traditional pipelines are brittle, one-directional, and purpose-built. If you need data for a new report, AI model, or dashboard, you start from scratch with each pipeline as a separate engineering project. Matterbeam flips this: data flows into immutable datasets once. From there, you can add new transforms or emitters without touching the source, replay historical data through new logic, time travel to any point in data history, and spin up new use cases in minutes instead of months.
How is Matterbeam different than Fivetran?
Matterbeam is more than a connector-based pipeline tool. While Fivetran focuses on copying data and charging based on rows and connectors, Matterbeam is a live data movement layer that stores the stream once, supports in-transit transforms, and enables replayability without re-reading production systems. This architectural difference reduces pipeline rebuilds, eliminates record-based pricing surprises, and gives teams more control. Customers typically see 50-70% lower data movement costs compared to Fivetran as their stack and data volumes grow.
How is Matterbeam different than Hevo?
Hevo is designed as a no-code ELT pipeline tool focused on extracting and loading data into warehouses. Matterbeam is a live data movement layer that stores the stream once, supports transforms during transit, and enables replayability without re-reading production systems. Instead of managing connector jobs and record-based pricing, teams move data through a durable stream architecture with transparent, GB-based pricing. This reduces rebuilds, increases control, and typically results in 50-70% lower data movement costs as data volume and destinations scale.
Can Matterbeam help with our AI initiatives?
Absolutely. Most AI projects fail because teams can't access the data they need when they need it. Getting historical data cleaned and prepared takes months, blowing project timelines. With Matterbeam, data scientists access datasets directly, time travel gives instant historical data for training, transforms let them experiment with features fast, and there's no waiting for data engineering sprints. One customer went from 6-month data projects to 2-week delivery using Matterbeam, with the AI team shipping models instead of tickets.
What pain points and use cases does Matterbeam solve for?
Matterbeam solves: AI/ML projects blocked by data access (give data scientists instant clean historical data, time travel to create training sets in hours instead of months - 95% of AI projects fail on data infrastructure not models); data migrations and integrations (collect from old and new systems into unified datasets, replay historical data as you migrate with no temporary pipelines); engineering backlogs measured in quarters (collect once, use anywhere, deliver in days); customer 360 views (combine Salesforce, Stripe, support tickets, product usage with real-time updates); and real-time analytics without infrastructure burden (stream from production databases to warehouses without impacting performance).
Do I need to replace my existing data tools to use Matterbeam?
No, you don't need to replace anything. Matterbeam works alongside everything you have today - your data warehouse, BI tools, databases, and existing pipeline tools like Fivetran, Hevo, or Airbyte. But once teams experience replay and transformation capabilities (replay historical data, transform on the fly, test without breaking production), they naturally phase out point-to-point tools. These are capabilities traditional tools can't match. Plus, Matterbeam is cheaper - instead of paying for three Salesforce connectors to send data to three destinations, you pay for one collector and spin up unlimited emitters with no triple billing. Teams typically save 50-70% while gaining capabilities they never had before.
Is there a free trial?
Contact Matterbeam to discuss your needs. They work with design partners on custom arrangements that let you prove value before committing. There is no standard free trial, but bulk data loading is free and live data emission is free, so you can test core functionality.
What integrations does Matterbeam support?
Matterbeam connects to Amazon Redshift, Google Analytics, HubSpot (beta), Kafka, MySQL, Pinecone (beta), Salesforce, Shopify, Snowflake, and AWS SNS. Collectors pull from any system and emitters shape JSON, Parquet, vectors, or tables for any target. If a system holds data, Matterbeam can connect, shape, and emit it anywhere. The platform continues to add more integrations beyond this core list.
How does data replay work in Matterbeam?
Data replay lets you endlessly emit data across time. Matterbeam's Emitter translates the internal format to suit the target system's requirements and operates independently from data collection. You can pause and restart emitters without impacting the source, simultaneously replay data across multiple destinations, and introduce new emitters at any point. When using replay, you choose a start date to ensure your new target system is up to date. The replay will be billed at $25/GB, but any data emitted live after that is free.
What transforms does Matterbeam support?
Matterbeam supports multi-step transform composer with data previews, user-defined transforms in Python for custom logic, and Stream Joins for stateful transformations. The same dataset can be transformed and used for endless use cases without disrupting upstream data flows. Matterbeam automatically detects and learns your data's schema, and you can explore the schema or preview records right after starting collection. Transforms happen in motion during streaming.