Sold by: Quilt Data
Deployed on AWS
Quilt is the scientific data management system (SDMS) built for life sciences on AWS. Centralize, version, and govern research data in Amazon S3 - with rich metadata search, audit trails, and AI-ready data access. Deploys privately in your VPC via CloudFormation. Visit https://quilt.bio
Overview
Quilt is a scientific data management system (SDMS) purpose-built for life sciences teams on AWS. Biotech and pharmaceutical organizations use Quilt to centralize, version, and govern petabytes of research data - from instrument outputs and pipeline results to analyses and regulatory submissions - in a single searchable catalog backed by Amazon S3.
Research teams lose months searching for data scattered across S3 buckets, shared drives, and lab systems. Quilt solves this by organizing data into immutable, versioned packages with rich metadata - making every dataset findable, accessible, interoperable, and reusable (FAIR).
Data Packages and Versioning: Capture files, metadata, and documentation in immutable collections with full version history and audit trails. Every change is tracked, every version is reproducible.
Metadata Catalog and Search: Find any dataset instantly across petabytes of S3 data using metadata facets, full-text search, and Amazon OpenSearch-powered queries. No more guessing which folder has the latest results.
Governance and Compliance: Enforce data quality with configurable workflows, roles, and policies. Maintain complete audit trails for GxP, 21 CFR Part 11, and other regulatory requirements.
AI-Ready Data: Connect Server enables AI assistants to search, browse, and reason over your data catalog via the Model Context Protocol (MCP). Bring AI to your data without moving your data to AI.
Integrations: Link data to Jupyter notebooks, ELNs, LIMS, and any system via immutable S3 URIs and a Python SDK. Works with your existing tools.
Secure by Design: Quilt deploys entirely within your AWS account as a CloudFormation stack running on Amazon ECS. Your data never leaves your VPC. Powered by Amazon S3, Amazon OpenSearch, and Amazon Athena - no external dependencies, no data exfiltration risk.
Trusted by leading life sciences organizations for drug discovery data management, regulatory compliance, and cross-team collaboration.
Learn more at https://quilt.bio
Highlights
- Deploys privately in your AWS account via CloudFormation - your research data never leaves your VPC, with full audit trails for GxP and regulatory compliance.
- Accelerate drug discovery by making every dataset findable: rich metadata search, data versioning, and FAIR data packages across petabytes of Amazon S3 data.
- AI-ready from day one: connect AI assistants to your data catalog via MCP, enforce quality with configurable workflows, and integrate with notebooks, ELNs, and LIMS.
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 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.
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Dimension | Description | Cost/unit |
|---|---|---|
API_Calls | Number of API calls made by or to the platform | $0.00 |
Buckets | Number of buckets attached to the stack | $0.00 |
Data | Total volume of indexed data across all attached buckets | $0.001 |
Deployments | Number of deployed stacks | $0.00 |
Metadata_Facets | Number of metadata facets (including nested facets) | $0.00 |
Objects | Number of indexed objects across all attached buckets | $0.00 |
Packages | Number of packages across all attached buckets. | $0.00 |
Policies | Number of Quilt Policies (both managed and custom) | $0.00 |
Regions | Number of distinct regions across all attached buckets | $0.00 |
Roles | Number of Quilt Roles (both managed and custom) | $0.00 |
Vendor refund policy
We provide a full refund within the first 30 days of use, after this, customers can cancel any time, but are charged for the usage posted.
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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
Connect Server brings AI assistants to Quilt via MCP, plus cross-region S3 fixes, catalog UX improvements, and infrastructure hardening.
Platform
- Fix presigned S3 URLs using wrong region for cross-bucket package files (#2311 )
- Add copy-URI button to S3 browser download buttons for directories and files (#2305 )
- Fix errors when working with newly created cross-region buckets (#2308 )
- Add Connect Server for AI assistant integrations via MCP (#2309 , #2315 )
- Fix text selection in toolbar popover code samples (#2297 )
- File Browser package creation adds current bucket as destination option only when workflow config is missing; workflow successors are always respected when configured (#2296 )
Stack Admin
- Fix access counts lambda timeouts when processing large analytics data (#2319 )
- Abort incomplete multipart uploads in analytics and CloudTrail buckets; expire analytics access logs after 365 days (#2314 )
- Propagate tags to running ECS tasks for Cost Explorer visibility (PropagateTags, EnableECSManagedTags) (#2303 )
Variants
- Upgrade ES instances to 6g for all CloudFormation deployments except tessera-nw-2-0 (#2281 )
Quilt Engineering
- Set default_tags in Terraform AWS provider to automatically tag all infrastructure resources (auto-stack-dev, rc, nightly, stable). (#2279 )
- Require Terraform >= 1.10 in all stacks (enforces use_lockfile compatibility). (#2295 )
Components
- catalog: bd6c...36a5
- nginx: ac7f...75b3
- registry: f474...b9f8
- access_counts: 3aba...adae
- platform-mcp-server: b4a7 (added)
Additional details
Usage instructions
Deploy using the CloudFormation template. See https://docs.quiltdata.com 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. Enterprise SLAs available.
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.
Similar products
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
PTP and Quilt Data have partnered to offer a jumpstart deployment of the Quilt solution in your AWS environment. The jumpstart provides a frictionless way to go from zero to science in just one day. The services included provide you with everything you need to get your scattered, unlabeled data into reproducible, discoverable, and trusted datasets in the cloud. Allowing your company to go from zero to science faster.
This bucket contains multiple datasets (as Quilt packages) created by the
Center for Geospatial Sciences (CGS) at the University of California-Riverside.
The data in this bucket contains the following:
1) Tabular and geographic data from the US Census
2) Land Cover imagery collected from Multi-Resolution Land Characteristics Consortium
3) Road network data processed from OpenStreetMap
This bucket contains multiple datasets (as Quilt packages) created by the Allen Institute for Cell Science. The types of data included in this bucket are listed below:
1) Field of view or cropped images of cells
2) Segmentations of structures in the images (e.g., boundaries of cells, DNA, other intracellular structures, etc.)
3) Processed versions of the above images and segmentations
4) Machine learning predictions and labels of the data listed above
5) Models trained on the previously listed data
6) Additional supporting non-image data related to the above listed data types (e.g., gene expression data, whole genome sequencing data, features derived from the images or model predictions, metadata)
7) Simulation, analysis, and visualization data of in silico cell structures, cells, and cell populations
External funding:
The generation of some datasets was supported by the National Human Genome Research Institute of the National Institutes under Award Number UM1HG011593. The cont[...]
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