Tigris Data
@tigrisdata.com
Tigris is a globally distributed S3-compatible object storage service that provides low latency anywhere in the world. | https://tigrisdata.com | Based in SFO
More teams are moving out of Big Cloud to neoclouds better built for AI workloads. How does the growth of neoclouds change the storage layer?
Our CEO talked with @thenewstack.io about the new cloud and how distributed computing is taking over and what that means for storage.
🔗👇
Our CEO talked with @thenewstack.io about the new cloud and how distributed computing is taking over and what that means for storage.
🔗👇
January 14, 2026 at 7:44 PM
More teams are moving out of Big Cloud to neoclouds better built for AI workloads. How does the growth of neoclouds change the storage layer?
Our CEO talked with @thenewstack.io about the new cloud and how distributed computing is taking over and what that means for storage.
🔗👇
Our CEO talked with @thenewstack.io about the new cloud and how distributed computing is taking over and what that means for storage.
🔗👇
What a year!
From $25M funding to Git-like bucket forks, our 2025 was all about powering modern AI workflows with fast, flexible object storage.
Catch the highlights! 👇
From $25M funding to Git-like bucket forks, our 2025 was all about powering modern AI workflows with fast, flexible object storage.
Catch the highlights! 👇
January 8, 2026 at 9:34 PM
What a year!
From $25M funding to Git-like bucket forks, our 2025 was all about powering modern AI workflows with fast, flexible object storage.
Catch the highlights! 👇
From $25M funding to Git-like bucket forks, our 2025 was all about powering modern AI workflows with fast, flexible object storage.
Catch the highlights! 👇
Chunking is a primary control knob for RAG quality. Compare chunking strategies using a reproducible pipeline:
• @qdrant.bsky.social for per-experiment indexing and retrieval diffs
• Tigris (Forked Buckets) for dataset versioning
• CrewAI to orchestrate ingest → chunk → embed → evaluate
Code 👇
• @qdrant.bsky.social for per-experiment indexing and retrieval diffs
• Tigris (Forked Buckets) for dataset versioning
• CrewAI to orchestrate ingest → chunk → embed → evaluate
Code 👇
January 6, 2026 at 5:20 PM
Chunking is a primary control knob for RAG quality. Compare chunking strategies using a reproducible pipeline:
• @qdrant.bsky.social for per-experiment indexing and retrieval diffs
• Tigris (Forked Buckets) for dataset versioning
• CrewAI to orchestrate ingest → chunk → embed → evaluate
Code 👇
• @qdrant.bsky.social for per-experiment indexing and retrieval diffs
• Tigris (Forked Buckets) for dataset versioning
• CrewAI to orchestrate ingest → chunk → embed → evaluate
Code 👇
Tigris delivers 4× higher throughput than S3 and 20× higher than R2 for small object workloads — 5.3× / 86.6× faster on reads. 🔥
Running training / inference on CoreWeave, Together AI, Lambda Labs, etc? You don’t need to stay locked into one big cloud to get performance.
Run the benchmarks👇
Running training / inference on CoreWeave, Together AI, Lambda Labs, etc? You don’t need to stay locked into one big cloud to get performance.
Run the benchmarks👇
December 23, 2025 at 4:55 PM
Tigris delivers 4× higher throughput than S3 and 20× higher than R2 for small object workloads — 5.3× / 86.6× faster on reads. 🔥
Running training / inference on CoreWeave, Together AI, Lambda Labs, etc? You don’t need to stay locked into one big cloud to get performance.
Run the benchmarks👇
Running training / inference on CoreWeave, Together AI, Lambda Labs, etc? You don’t need to stay locked into one big cloud to get performance.
Run the benchmarks👇
Tired of public registry limits, egress fees, or compliance headaches?
You can now spin up a private Docker registry on @northflank backed by Tigris in 1 click. Type in your keys, deploy, done
Easy scaling, multi-region, zero hassle.
Full guide in the replies.
You can now spin up a private Docker registry on @northflank backed by Tigris in 1 click. Type in your keys, deploy, done
Easy scaling, multi-region, zero hassle.
Full guide in the replies.
December 9, 2025 at 5:54 PM
Tired of public registry limits, egress fees, or compliance headaches?
You can now spin up a private Docker registry on @northflank backed by Tigris in 1 click. Type in your keys, deploy, done
Easy scaling, multi-region, zero hassle.
Full guide in the replies.
You can now spin up a private Docker registry on @northflank backed by Tigris in 1 click. Type in your keys, deploy, done
Easy scaling, multi-region, zero hassle.
Full guide in the replies.
Aaron Francis captured it perfectly on Database School: with Tigris, ‘the region is just Earth.’
No CDNs, no cross-region replication, no complex DR— Reliability is the default on Tigris.
Link to the full episode below 👇
No CDNs, no cross-region replication, no complex DR— Reliability is the default on Tigris.
Link to the full episode below 👇
November 20, 2025 at 8:40 PM
Aaron Francis captured it perfectly on Database School: with Tigris, ‘the region is just Earth.’
No CDNs, no cross-region replication, no complex DR— Reliability is the default on Tigris.
Link to the full episode below 👇
No CDNs, no cross-region replication, no complex DR— Reliability is the default on Tigris.
Link to the full episode below 👇
Classical clouds are obsolete.
The future belongs to specialized clouds built for AI and high-performance workloads — but they still need reliable, S3-grade storage.
That’s where Tigris comes in.
📷 Great chat between @ovaistariq.net and Martin Casado on the a16z podcast, link in reply
The future belongs to specialized clouds built for AI and high-performance workloads — but they still need reliable, S3-grade storage.
That’s where Tigris comes in.
📷 Great chat between @ovaistariq.net and Martin Casado on the a16z podcast, link in reply
November 11, 2025 at 2:39 PM
Classical clouds are obsolete.
The future belongs to specialized clouds built for AI and high-performance workloads — but they still need reliable, S3-grade storage.
That’s where Tigris comes in.
📷 Great chat between @ovaistariq.net and Martin Casado on the a16z podcast, link in reply
The future belongs to specialized clouds built for AI and high-performance workloads — but they still need reliable, S3-grade storage.
That’s where Tigris comes in.
📷 Great chat between @ovaistariq.net and Martin Casado on the a16z podcast, link in reply
Thanks to Martin Casado for having @ovaistariq.net on the a16z podcast to talk about the future of infrastructure for AI, and how Tigris is building the storage layer for AI workloads— from the metal up.
Learn why AI companies keep choosing Tigris for their storage!
🔗 in reply
Learn why AI companies keep choosing Tigris for their storage!
🔗 in reply
November 7, 2025 at 5:25 PM
Thanks to Martin Casado for having @ovaistariq.net on the a16z podcast to talk about the future of infrastructure for AI, and how Tigris is building the storage layer for AI workloads— from the metal up.
Learn why AI companies keep choosing Tigris for their storage!
🔗 in reply
Learn why AI companies keep choosing Tigris for their storage!
🔗 in reply
Big thanks to Aaron Francis for having Tigris CEO @ovaistariq.net on Database School!
As Aaron said, “It’s a bold undertaking to go against S3.”
We couldn’t agree more — and we’re here for the challenge.
🎧 Catch the full episode to see how Tigris is tackling vendor lock-in!
🔗 Link in reply
As Aaron said, “It’s a bold undertaking to go against S3.”
We couldn’t agree more — and we’re here for the challenge.
🎧 Catch the full episode to see how Tigris is tackling vendor lock-in!
🔗 Link in reply
November 6, 2025 at 5:03 PM
Big thanks to Aaron Francis for having Tigris CEO @ovaistariq.net on Database School!
As Aaron said, “It’s a bold undertaking to go against S3.”
We couldn’t agree more — and we’re here for the challenge.
🎧 Catch the full episode to see how Tigris is tackling vendor lock-in!
🔗 Link in reply
As Aaron said, “It’s a bold undertaking to go against S3.”
We couldn’t agree more — and we’re here for the challenge.
🎧 Catch the full episode to see how Tigris is tackling vendor lock-in!
🔗 Link in reply
Experiment without risk.
Have a big production dataset? Don’t copy—fork it.
Make alternate timelines for each model or transformation with zero copying.
We tried cleaning, transforms, and inference—no impact on source.
🔗 Code on the blog, link in reply.
Have a big production dataset? Don’t copy—fork it.
Make alternate timelines for each model or transformation with zero copying.
We tried cleaning, transforms, and inference—no impact on source.
🔗 Code on the blog, link in reply.
October 30, 2025 at 4:06 PM
Experiment without risk.
Have a big production dataset? Don’t copy—fork it.
Make alternate timelines for each model or transformation with zero copying.
We tried cleaning, transforms, and inference—no impact on source.
🔗 Code on the blog, link in reply.
Have a big production dataset? Don’t copy—fork it.
Make alternate timelines for each model or transformation with zero copying.
We tried cleaning, transforms, and inference—no impact on source.
🔗 Code on the blog, link in reply.