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
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.
Have a massive production dataset you want to play with? You don't need to wait for a full copy.
Just fork your source bucket, experiment freely, throw it away, and spin up a new one— instantly.
3/10
Just fork your source bucket, experiment freely, throw it away, and spin up a new one— instantly.
3/10
October 21, 2025 at 4:03 PM
Have a massive production dataset you want to play with? You don't need to wait for a full copy.
Just fork your source bucket, experiment freely, throw it away, and spin up a new one— instantly.
3/10
Just fork your source bucket, experiment freely, throw it away, and spin up a new one— instantly.
3/10
Big news! @tigrisdata.com raised $25M Series A led by Natalie Vais of Spark Capital w/ Martin Casado of a16z + Basis Set.
Tigris is a faster, freer, AI-native storage cloud– running on our own hardware.
Already powering fal.ai, krea.ai & Hedra.
Let’s build the open cloud for AI.
Read more on TC
Tigris is a faster, freer, AI-native storage cloud– running on our own hardware.
Already powering fal.ai, krea.ai & Hedra.
Let’s build the open cloud for AI.
Read more on TC
October 9, 2025 at 5:16 PM
Big news! @tigrisdata.com raised $25M Series A led by Natalie Vais of Spark Capital w/ Martin Casado of a16z + Basis Set.
Tigris is a faster, freer, AI-native storage cloud– running on our own hardware.
Already powering fal.ai, krea.ai & Hedra.
Let’s build the open cloud for AI.
Read more on TC
Tigris is a faster, freer, AI-native storage cloud– running on our own hardware.
Already powering fal.ai, krea.ai & Hedra.
Let’s build the open cloud for AI.
Read more on TC
We compared Flux Kontext, Nano Banana, and GPT-Image-1 for image gen. Result:
◆ Flux is the best for most use cases and also the fastest & cheapest.
◆ Nano Banana is very close behind.
◆ GPT-Image-1 is the best at detail.
Compare the models yourself 👉 www.image-models.dev
◆ Flux is the best for most use cases and also the fastest & cheapest.
◆ Nano Banana is very close behind.
◆ GPT-Image-1 is the best at detail.
Compare the models yourself 👉 www.image-models.dev
October 1, 2025 at 3:32 PM
We compared Flux Kontext, Nano Banana, and GPT-Image-1 for image gen. Result:
◆ Flux is the best for most use cases and also the fastest & cheapest.
◆ Nano Banana is very close behind.
◆ GPT-Image-1 is the best at detail.
Compare the models yourself 👉 www.image-models.dev
◆ Flux is the best for most use cases and also the fastest & cheapest.
◆ Nano Banana is very close behind.
◆ GPT-Image-1 is the best at detail.
Compare the models yourself 👉 www.image-models.dev
Check out our new changelog, a balance of feature announcements, blog posts, and API changes.
September 10, 2025 at 4:57 PM
Check out our new changelog, a balance of feature announcements, blog posts, and API changes.
We were shocked watching LogSeam query 10TB of logs in seconds.
How?
- Refactored to Parquet & compressed 100x
- Data moved close to compute w/ @TigrisData
- 100s of nodes spin up per query
👉 500M+ logs/sec, 1.5TB+/sec search
💸 40–80% lower cost
🔗 link to the blog in reply
How?
- Refactored to Parquet & compressed 100x
- Data moved close to compute w/ @TigrisData
- 100s of nodes spin up per query
👉 500M+ logs/sec, 1.5TB+/sec search
💸 40–80% lower cost
🔗 link to the blog in reply
September 4, 2025 at 5:40 PM
We were shocked watching LogSeam query 10TB of logs in seconds.
How?
- Refactored to Parquet & compressed 100x
- Data moved close to compute w/ @TigrisData
- 100s of nodes spin up per query
👉 500M+ logs/sec, 1.5TB+/sec search
💸 40–80% lower cost
🔗 link to the blog in reply
How?
- Refactored to Parquet & compressed 100x
- Data moved close to compute w/ @TigrisData
- 100s of nodes spin up per query
👉 500M+ logs/sec, 1.5TB+/sec search
💸 40–80% lower cost
🔗 link to the blog in reply
Did you miss us at DevOpsDays Halifax last week? Xe had a talk on globally distributed systems design and we have a copy of it on our blog! Here's a sneak peek:
September 3, 2025 at 6:49 PM
Did you miss us at DevOpsDays Halifax last week? Xe had a talk on globally distributed systems design and we have a copy of it on our blog! Here's a sneak peek: