Updates and recent posts about AIStor..

Posts
Description

Activity

@kala added a new tool GPT-5.2 , 4 months, 2 weeks ago.

Story

@laura_garcia shared a post, 4 months, 2 weeks ago

Software Developer, RELIANOID

RELIANOID at CII Delhi International Technology Summit 2025

16–17 December 2025 - New Delhi, India Our team continues a packed December schedule, and we’re excited to add another key event: the CII Delhi International Technology Summit 2025. Focused on “Accelerating the Techade”, this summit brings together industry, government, and research leaders to shape..

CII Delhi International Technology Summit relianoid

Link

@anjali shared a link, 4 months, 2 weeks ago

Customer Marketing Manager, Last9

OTel Updates: OpenTelemetry Proposes Changes to Stability, Releases, and Semantic Conventions

OpenTelemetry proposes stability changes: stable-by-default distributions, decoupled instrumentation, and epoch releases for production deployments.

Story

@laura_garcia shared a post, 4 months, 2 weeks ago

Software Developer, RELIANOID

deploy the RELIANOID Load Balancer Community Edition v7 on Azure using Terraform

🚀 New Technical Guide Available! You can now deploy the RELIANOID Load Balancer Community Edition v7 on Azure using Terraform in just a few minutes: ✔️ Install prerequisites (Terraform, Azure CLI, SSH keys) ✔️ Use the official Terraform module from the Registry ✔️ Automatically provision all Azure r..

terraform_relianoid_community_azure_img2

Activity

@tairascott gave 🐾 to Helm 4 or Nelm? What's the difference , 4 months, 2 weeks ago.

Activity

@tairascott gave 🐾 to Hidden Correlations Traditional Monitoring Misses , 4 months, 2 weeks ago.

Activity

@tairascott gave 🐾 to How to Track Down the Real Cause of Sudden Latency Spikes , 4 months, 2 weeks ago.

Link

@anjali shared a link, 4 months, 2 weeks ago

Customer Marketing Manager, Last9

How to Track Down the Real Cause of Sudden Latency Spikes

Sudden latency spikes rarely have a single cause. This blog shows how to uncover the real source using traces, histograms, and modern debugging signals.

Link

@anjali shared a link, 4 months, 2 weeks ago

Customer Marketing Manager, Last9

Hidden Correlations Traditional Monitoring Misses

Last9 is built to work with high-cardinality telemetry, and we’ve been covering it in detail through our series. This piece looks at a familiar pain: issues that only show up for a specific tenant or deployment. Why does that context disappear in most monitoring setups?

Story Trending

@shurup shared a post, 4 months, 3 weeks ago

@palark

Helm 4 or Nelm? What's the difference

#Helm #Cloud N... #werf #Nelm #kuberne...

Helm 4.0.0 brought several new features to its users, such as Server-Side Apply support and kstatus-based resource watching.Nelm, an alternative to Helm created in werf, a CNCF Sandbox project, has been offering these capabilities even before. Nelm has many more new features for Kubernetes deploymen..

AIStor is an enterprise-grade, high-performance object storage platform built for modern data workloads such as AI, machine learning, analytics, and large-scale data lakes. It is designed to handle massive datasets with predictable performance, operational simplicity, and hyperscale efficiency, while remaining fully compatible with the Amazon S3 API. AIStor is offered under a commercial license as a subscription-based product.

At its core, AIStor is a software-defined, distributed object store that runs on commodity hardware or in containerized environments like Kubernetes. Rather than being limited to traditional file or block interfaces, it exposes object storage semantics that scale from petabytes to exabytes within a single namespace, enabling consistent, flat addressing of vast datasets. It is engineered to sustain very high throughput and concurrency, with examples of multi-TiB/s read performance on optimized clusters.

AIStor is optimized specifically for AI and data-intensive workloads, where throughput, low latency, and horizontal scalability are critical. It integrates broadly with modern AI and analytics tools, including frameworks such as TensorFlow, PyTorch, Spark, and Iceberg-style table engines, making it suitable as the foundational storage layer for pipelines that demand both performance and consistency.

Security and enterprise readiness are central to AIStor’s design. It includes capabilities like encryption, replication, erasure coding, identity and access controls, immutability, lifecycle management, and operational observability, which are important for mission-critical deployments that must meet compliance and data protection requirements.

AIStor is positioned as a platform that unifies diverse data workloads — from unstructured storage for application data to structured table storage for analytics, as well as AI training and inference datasets — within a consistent object-native architecture. It supports multi-tenant environments and can be deployed across on-premises, cloud, and hybrid infrastructure.