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Quilt Startup 5 is a scientific data management system (SDMS) for emerging biotech teams on AWS. Get a private data catalog with versioning, metadata search, and governance for up to 5 users - deployed securely in your VPC via CloudFormation. Visit https://quilt.bio
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Overview
Quilt Startup 5 is a scientific data management system (SDMS) designed for early-stage biotech and life sciences teams running on AWS. Get the full power of the Quilt data platform for up to 5 users - ideal for small research teams that need enterprise-grade data management from day one.
Early-stage biotech teams generate critical research data from the start, but most data management solutions are priced for large enterprises or require dedicated engineering teams to operate. Quilt Startup 5 gives small teams the same capabilities that leading pharma organizations rely on, at a scale and price point that fits.
What You Get: A private, secure data catalog deployed entirely within your own AWS account. Organize research data into versioned, immutable packages with rich metadata. Search across all your S3 data instantly. Enforce data quality with configurable workflows. Maintain audit trails for regulatory preparedness from day one.
Data Packages and Versioning: Every dataset is captured as an immutable, versioned package with full lineage. Files, metadata, QC metrics, and documentation travel together. Every version is cryptographically verifiable and reproducible - critical when you need to defend results to a partner, investor, or regulator.
Metadata-Driven Search: Find any experiment result, instrument output, or pipeline artifact instantly across all your S3 data using Amazon OpenSearch-powered metadata queries. Scientists self-serve instead of asking bioinformatics or data engineering for file paths. Entact Bio reduced data lookup time by 90% after deploying Quilt.
Benchling Integration: Quilt is an official Benchling integration partner. Scientists can browse versioned S3 data packages directly from inside the ELN - no manual hand-offs, no broken links between notebook entries and instrument data. For teams already using Benchling, this is the missing bridge to your AWS data.
AI-Ready from Day One: Quilt's open-source MCP (Model Context Protocol) server connects AI assistants directly to your versioned data catalog. Sail Biomedicines used Claude connected to Quilt and Benchling MCP servers to reduce cross-assay analysis from hours to under 10 minutes - no data moved outside their environment, no ETL, no hand-off to a data science team.
Why Start with Good Data Practices Early: Biotech teams that establish FAIR data foundations before their first IND avoid costly remediation and audit failures later. Inari Agriculture cut retrieval times by 50% and eliminated manual data requests by building a searchable catalog on S3. Quilt Startup 5 helps you build that foundation from your first experiment.
Secure by Design: Deploys entirely within your AWS account as a CloudFormation stack on Amazon ECS. Your data stays in your VPC. Powered by Amazon S3, Amazon OpenSearch, and Amazon Athena. No vendor access to your research data.
What's Included: Quilt Platform for up to 5 users. Web catalog, search, and data browser. Python SDK for programmatic access. MCP server for AI connectivity. Benchling App for ELN integration. CloudFormation deployment templates. Support included.
Ready to grow? Upgrade seamlessly to the full Quilt Data Platform (Pay-as-you-Go) as your team and data expand - no migration required, same platform underneath.
Learn more at https://quilt.bio
Highlights
- Enterprise-grade data management for early-stage biotech - deploy a private, secure catalog in your VPC for up to 5 researchers.
- Build regulatory-ready data practices from day one: versioned packages, metadata search, audit trails, and configurable governance workflows.
- Start small, scale seamlessly - upgrade to the full Quilt Data Platform as your team and data grow, with no migration required.
Details
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Delivery method
Supported services
Delivery option
Containers Installed via CloudFormation
Latest version
Operating system
Linux
Deployed on AWS
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Pricing
Free trial
Try this product free for 30 days according to the free trial terms set by the vendor. Usage-based pricing is in effect for usage beyond the free trial terms. Your free trial gets automatically converted to a paid subscription when the trial ends, but may be canceled any time before that.
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. Alternatively, you can pay upfront for a contract, which typically covers your anticipated usage for the contract duration. Any usage beyond contract will incur additional usage-based costs.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/unit/hour |
|---|---|---|
Hours | Container Hours | $2.60 |
Dimensions summary
This listing uses a single usage-based dimension: Container Hours. You pay for the hours the platform's containers run in your own AWS account. There are no separate tiers or instance sizes to choose from here. Your cost scales with how long the software runs, so more running time means more billed hours. The platform deploys inside your VPC, and the data stays in your S3. Because billing is tied to running time rather than data volume or user count, your spend tracks actual container usage over the billing period.
Top-of-mind questions for buyers
A container hour measures the time the platform's containers run inside your AWS account. The software stack runs on AWS Fargate in your VPC, using containers for the catalog, registry, and related services. Each hour these containers run counts toward your billed hours. Counting reflects running time, not data volume or user count.
Billing meters container running time. When containers run, hours accrue. When they are not running, software hours do not accrue. Note that underlying AWS resources in your account, such as S3 storage and databases, may still incur separate AWS charges even when Quilt software containers are idle.
No. This dimension bills only for container running time. Data volume and user count do not change your container-hour charges directly. Your data stays in your own S3 buckets and never moves. Storing more data may raise your separate AWS storage costs, but not the Quilt software hours metered here.
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For refund information please contact us at: support@quiltdata.io
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Delivery details
Containers Installed via CloudFormation
Supported services: Learn more
- Amazon ECS
Container image
Containers are lightweight, portable execution environments that wrap server application software in a filesystem that includes everything it needs to run. Container applications run on supported container runtimes and orchestration services, such as Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS). Both eliminate the need for you to install and operate your own container orchestration software by managing and scheduling containers on a scalable cluster of virtual machines.
Version release notes
Quilt Startup 5 - Quilt Data Platform 1.69.7. Includes security updates (Amazon Linux 2023 base bump, dependency patches for cryptography, aiohttp, idna), Connect Server (MCP) refinements, cross-region S3 fixes, and per-bucket Iceberg/Athena improvements. See https://github.com/quiltdata/deployment for full changelog.
Additional details
Usage instructions
Deploy using the CloudFormation template. See https://docs.quilt.bio/quilt-platform-administrator/installation for setup instructions.
Resources
Vendor resources
Support
Vendor support
Email support@quiltdata.io for technical assistance. Visit https://quilt.bio for documentation, live chat, and getting started guides.
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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AI generated from product descriptions
Data Versioning and Immutability
Datasets captured as immutable, versioned packages with full lineage, cryptographic verification, and reproducibility across all versions.
Metadata-Driven Search
Amazon OpenSearch-powered metadata queries enabling instant search across S3 data with rich metadata including files, QC metrics, and documentation.
Enterprise Laboratory Notebook Integration
Official Benchling integration allowing scientists to browse versioned S3 data packages directly from within the ELN without manual hand-offs or broken links.
AI Assistant Connectivity
Open-source Model Context Protocol (MCP) server enabling AI assistants to connect directly to versioned data catalog without data movement or ETL processes.
Secure VPC Deployment
CloudFormation-based deployment entirely within customer AWS account on Amazon ECS with data isolation in VPC, powered by Amazon S3, Amazon OpenSearch, and Amazon Athena.
Direct S3 Data Indexing
Indexes Amazon S3 data without transformation or schema changes, enabling immediate access to all data as-is
SQL and Search Query Support
Enables SQL queries and search workloads on indexed S3 data through open APIs compatible with analytics tools
Machine Learning Workload Capability
Supports machine learning workloads on indexed data stored in Amazon S3 with infinite scalability
Unlimited Data Retention
Provides unlimited retention of indexed data, enabling historical analysis across any time horizon without data purging or archival requirements
Fully Managed Service Architecture
Operates as a fully managed service eliminating administrative overhead including re-indexing, sharding, load balancing, and compute/storage management
Native AWS Service Integration
Native API integration with over 60 AWS services including Amazon Redshift, AWS Glue, Amazon QuickSight, and Amazon SageMaker for immediate data access without additional development.
Unified Data Management
Ability to unify all data types including unstructured, semi-structured, and structured data from both data warehouse and data lake environments across multiple workloads.
Built-in Data Catalog
Integrated data catalog functionality for search and sharing of data assets across the organization.
Access Control and Identity Management
Centralized management of users, roles, permissions, and security policies within a single platform.
Data Ingestion and Transformation
Support for multiple data ingestion methods including single file upload, batch processing, and streaming with ELT capabilities for data transformation.
Standard contract
No
No
No
Customer reviews
Josh Morris, Engineering Manager Machine Learning at DSP Concepts
The data hub I've been looking for
Reviewed on Dec 08, 2021
Review from a verified AWS customer
Quilt allows my team at DSP Concepts to focus on solving customer problems instead of data versioning problems. It is well established at this point that data quality is the foundation of serious and well performing data teams. Organization is key to building and retaining value in high quality data over time. Quilt solves this problem head on giving you a reliable single source of data truth with a suite of features to inspect the data and its documentation easily. This in turn allows each member of my team to find data in a self serve fashion without having to rely on institutional knowledge that only one team member, if any, might have depending on how long ago the data was collected, cleaned, processed and labelled.
A computational biologist
A necessity for every data-driven company
Reviewed on May 11, 2020
Review from a verified AWS customer
Quilt is an indispensable tool for anyone that wants to properly manage their data in AWS. A key element to Quilt is that the programmatic interface is intuitive and flexible, offering multiple ways to integrate it into the data analysis workflow (python, R, command line). As only a handful of Quilt functions provide a majority of core functionality, there is not an overwhelming learning curve to get started, but many additional features improve the usability (e.g., reading data directly into memory, single file installation). Beyond the programmatic functionality, the Quilt web-based interface is extremely useful for browsing files and packages and switching between the different versions. I would highly recommend integrating Quilt into your data science workflow.
Grzegorz M.
Missing tool in Data Science pipeline
Reviewed on Oct 05, 2019
Review from a verified AWS customer
Quilt simplified our flow in data maintenance and versioning. Now, it became extremely easy to keep track of changes in a dataset and refer in a reproducible manner a specific revision without worrying if someone overwrites the data. We have it already integrated into our flow, so the dataset updates interfere with model building no more.
Quilt team provides us with ongoing support. Bugs happen in every software, but in the case of small bug we found, we received a fixup in no time, so we could smoothly continue our work.
We spotted some drawbacks in Quilt Teams some time ago. These are mostly resolved here, and remaining "wishes" are on the roadmap. It's really nice that devs listen to our needs!
What we love most about Quilt, is the caching feature. We reduced data transfer costs while keeping low complexity of scripts.
Overall grade is 5/5 since that tool was missing heavily in the flow we had for Machine Learning. At this moment we use it also for versioning models (especially that we generate models in a bunch of formats each time) and Jupyter Notebooks (for which Git isn't the best option)