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 dataset is cryptographically verifiable. Every version is reproducible. Data lineage flows automatically from instrument to pipeline to analysis to submission.
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 - scientists self-serve instead of filing tickets.
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. Quilt's immutability and lineage tracking are purpose-built for environments that require regulatory rigor.
AI-Ready Data: Quilt's open-source MCP (Model Context Protocol) server lets AI assistants search, browse, and run queries directly against your versioned data catalog - without moving data out of your VPC. Sail Biomedicines connected Claude to Quilt and Benchling MCP servers and reduced cross-assay analysis from hours to under 10 minutes.
Benchling Integration: Quilt is an official Benchling integration partner. Scientists can browse versioned S3 data packages directly from inside the ELN - linking experiment entries to instrument outputs, pipeline results, and analyzed data without leaving Benchling. No manual hand-offs, no broken links.
Nextflow and Pipeline Integration: Quilt's Nextflow plugin automatically packages pipeline outputs with metadata at the end of every run. Tessera Therapeutics uses this integration to manage over 1 petabyte of genomics data on AWS, achieving 3x faster NGS analysis and 50% reduction in data search and retrieval time - with more than 80% of scientists and data engineers at the company using Quilt daily.
Proven at Scale in Life Sciences: Resilience replaced 3 legacy platforms with Quilt, saving 3,000+ hours per month and $3M in costs - with NGS processing times cut from 7-10 weeks to under 1 hour. Entact Bio reduced data lookup time by 90%. Inari Agriculture cut retrieval times by 50% and eliminated manual data requests across their research teams. These outcomes are consistent across organizations ranging from early-stage biotech to large-scale pharmaceutical manufacturing.
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, no vendor access to your research data.
What's Included: Full Quilt Platform with web catalog, search, and data browser. Python SDK for programmatic access and pipeline integration. Nextflow plugin for automated packaging. MCP server for AI connectivity. Benchling App for ELN integration. CloudFormation deployment templates. Enterprise support.
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
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 | 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 |
Dimensions summary
You pay based on your actual usage of the platform, which deploys inside your own AWS account. Billing tracks separate meters: the number of deployed stacks, attached buckets, regions, and users. It also counts indexed data volume, indexed objects, packages, workflows, and metadata facets. Access controls are metered through Quilt Policies and Quilt Roles, and activity is measured by API calls. Each meter is billed independently, so your total scales with how many buckets, users, and data assets you manage. Twelve additional dimensions are reserved for future use and carry no current charge.
Top-of-mind questions for buyers
A bucket is one attached storage container in your AWS account. Buckets and data volume are metered independently. Bucket count reflects how many storage locations you attach. Data measures total indexed volume across all attached buckets. Both accrue at the same time and appear together on your invoice.
Bucket count and indexed data volume usually dominate, since the platform organizes storage around attached buckets and indexes their contents. User count, indexed objects, packages, and API calls add on top. Each meter bills independently and combines on one invoice, so your total scales with buckets, users, and data assets managed.
No. Feature_1 through Feature_12 are reserved for future use and carry no current charge. They exist as placeholders in the pricing structure. Your bill reflects only the active meters: deployments, buckets, regions, users, data, objects, packages, workflows, metadata facets, policies, roles, and API calls.
www.quilt.bio
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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 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.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.
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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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