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DIY is fine when
You have a small team, a handful of buckets, and one person who knows where everything is. Scripts and conventions can carry you a surprisingly long way.
Quilt vs DIY S3 + scripts
Quilt vs DIY S3 + scripts: when does homegrown stop scaling?
TL;DR
S3 plus homegrown scripts works at first. The cost shows up later: versioning, search, audit trails, and permissions each become engineering projects your team has to build and maintain. Quilt delivers them out of the box, in your AWS account, on the buckets you already own — with an open-source core you can inspect.
Side by side
| Capability | Quilt | DIY S3 + scripts |
|---|---|---|
| Versioning | Built-in package versions with complete history and rollback | Naming conventions and copies you design and enforce yourself |
| Search | AI-powered search and deep indexing across millions of files | aws s3 ls, grep, and asking on Slack |
| Audit trails | CloudTrail-integrated logging plus full version history | Custom logging you have to build and retain |
| Access control | IAM- and SSO-governed, package-level access | Hand-rolled bucket policies per team |
| Metadata | Structured metadata attached to every package | Spreadsheets and README files that drift out of date |
| Lineage | Instrument-to-analysis lineage, preserved over time | Tribal knowledge |
| Maintenance | Supported product with an actively developed open-source core | Your engineers own every script, forever |
| Time to value | POC in your AWS account in about a week (CloudFormation) | Months of internal build before scientists see benefits |
When to use which
Add Quilt when
Multiple teams share data, regulated workflows demand audit trails, scientists wait on data engineers to find files, or the person who wrote the scripts is about to leave.
Keep your buckets. Keep your scripts.
Quilt is not a migration. It deploys into your own AWS account and works with the S3 buckets you already have — no data movement, no lock-in. Scripts you have already written keep working alongside the quilt3 Python SDK, APIs, and SQL access. And the core is open source, so the layer your science depends on is inspectable.
- Works with your existing S3 buckets — no migration
- quilt3 Python SDK, REST APIs, and SQL via Athena
- Open-source core at github.com/quiltdata
- Cut data lookup time by 90% with search and AI
See it in action
Version history DIY S3 cannot give you
Every Quilt package revision is immutable and hash-verified — audit-ready lineage without building it yourself on raw buckets.
03 Versioning & lineage
Every change tracked by a content hash
A package is immutable and verifiable. Browse the full revision history, diff any two versions, and reproduce any prior state — audit-ready for GxP and 21 CFR Part 11.
- Cryptographic hash for every revision — tamper-evident
- Permanent URIs resolve to an exact dataset, forever
- Reproduce or revert to any point in history
Revision history
-
a1f3c9dlatestAdd QC report + 142 new samplesm.chen · 2h ago · +142 −3 -
7b2e0a4Re-run alignment with STAR 2.7.11j.okafor · 3d ago · +1,142 -
3c9d810Initial cohort import from instrumentingest-bot · 12d ago · +1,003
Measured impact
Outcomes teams see with Quilt
90%
faster data lookup
Resilience
3×
NGS analysis throughput
Tessera
Weeks → minutes
from instrument to AI-ready package
30+
biotech & pharma teams
incl. Allen Institute, Inari
Trusted by leading life-sciences organizations
- Allen Institute
- Inari
- Flagship Pioneering
- Cellarity
- Resilience
- Tessera
Data lookups that used to take our scientists days now take minutes — with a single, governed source of truth the whole team can trust.
Frequently asked questions
Do we have to move our data into Quilt?
No. Quilt deploys into your own AWS account and works with your existing S3 buckets. You keep control of the data, the security policies, and the costs.
Is Quilt open source?
Yes. Quilt's core is open source and actively developed at github.com/quiltdata — versioned data packages, the catalog, and the quilt3 Python SDK.
What happens to the scripts we already built?
Keep them. Quilt is accessible via the quilt3 Python SDK, APIs, and SQL, so existing automation keeps working — it just gains versioning, search, and audit trails underneath.
How long does setup take?
A first POC running in your own AWS account usually takes about a week. The deployment is a CloudFormation template against buckets you already own.
What happens if the person who set up Quilt leaves?
Quilt provides onboarding and account transition support for admin handoffs. You can request a walkthrough for a new admin and we will map your deployment, buckets, and integrations together.
Compare notes with a Quilt engineer
Bring your current setup. 30 minutes, no slides — just what would change and what would not.