Next stop: #PlatformCon
๐ London โ June 23 meet @victorchiea
๐ New York โ June 25 join with @braoinese
We're bringing:
โข Live demos
โข AI infrastructure
โข Bare metal
โข Platform engineering
See you there. ๐
How much time have you spent reconfiguring storage after a deployment?
Custom Disk Layouts let you define partitions and RAID configurations before the server is provisioned.
Less time setting up infrastructure, more time running workloads.
๐ขCustom disk layout is live
Define RAID per group at deploy time: os, storage or raw, each with independent RAID-0 or RAID-1
โ https://t.co/aThPFkMqxS
๐ข New on https://t.co/Ks0XGEW6Ox VMs
๐ฅ๐ฒ๐ฎ๐น-๐๐ถ๐บ๐ฒ ๐บ๐ฒ๐๐ฟ๐ถ๐ฐ๐: CPU, memory, network, disk. Query windows from 5m to 24h.
๐จ๐๐ฒ๐ฟ ๐๐ฎ๐๐ฎ: attach cloud-config scripts that run on first boot, before you ever SSH in.
Check out VM metrics and set up user data โ https://t.co/yWhZ3Pmg7a
@RNR_0@ichimikichiki@Teraswitch Hey @RNR_0 latency to major exchanges is solid depending on your region and you can pay with crypto. We're also totally fine with crypto/trading workloads, actually we've a lot of clients doing that ๐
Big news.
Together with @megaportnetwork, we're helping build the next generation of AI infrastructure
- A$827M raised
- A$459M in new contract TCV
- https://t.co/Ks0XGEW6Ox grew from A$60M ARR to A$385M ARR in just 6 months following the acquisition.
Over the last 60 days alone, we've closed A$748M in combined TCV.
We're building a Global AI Inference Cloud: compute, network, and storage connected across 1,000+ data centers worldwide.
And we're just getting started.
More regions. More capacity. More infrastructure for builders.
We just shipped ๐ข๐ฏ๐ท๐ฒ๐ฐ๐ ๐ฆ๐๐ผ๐ฟ๐ฎ๐ด๐ฒ ๐ชฃ
S3-compatible, no request, retrieval or egress fees and no minimum terms.
Two classes: high-performance for AI workloads, standard for cold storage. Point your existing S3 tooling at it and go!
โ For locations and pricing: https://t.co/HZzjFjWPPu
โ Create your bucket: https://t.co/BH3vWaY0Xg
If you want to understand how modern infra teams are running K8s closer to the hardware layer, this one is worth the read.
Read the full post:
https://t.co/g80V89WUP2
Most Kubernetes workloads belong on VMs.
but if you care about deterministic latency, direct hardware access, predictable networking or running inference close to the metal, virtualization becomes overhead.
Our Software Engineer wrote about running Kubernetes on bare metal without the setup hell ๐งต
Bare metal Kubernetes makes the most sense for workloads like:
โ validator infrastructure
โ inference serving
โ HFT / real-time APIs
โ multiplayer backends
โ platform engineering
โ compute-heavy batch pipelines
Anywhere p99 latency matters more than averages.