Hystax

04/14/2026

Moving thousands of VMs from VMware without disruption is rarely just a tooling challenge. In most cases, the real bottlenecks show up in architecture, data transfer, and replication behavior.

In our recent session, we broke down how teams are approaching large-scale workload migrations from VMware today — including:
• How parallel data transfer impacts throughput
• Why distributed architecture matters at scale
• Where security and replication tuning become critical

We also included a live technical demo to show how these concepts work in practice.

If you’re working on or planning a VMware migration, you might find this useful: https://youtu.be/1yqxJr23vKk

1 like. "Migration from VMware at scale: How to move thousands of VMs with minimal disruption"

03/24/2026

One thing we see more often now: companies don’t ask “Do we need disaster recovery?” — they ask “Should we build it ourselves or consume it as a service?”

That’s the real difference between Disaster Recovery and DRaaS.

Both aim for the same outcome — business continuity — but the operational model, responsibility boundaries, and cost structure differ significantly.

We put together a short, practical guide explaining where traditional DR makes sense, where DRaaS is a better fit, and how companies are approaching this decision today.
➡️ https://hystax.com/disaster-recovery-vs-draas/?utm_source=facebook&utm_campaign=24.03.26

03/04/2026

Open infrastructure is becoming a key part of modern cloud strategies.

More companies want flexibility in how they run and move workloads across platforms — without being tied to a single vendor or ecosystem.

That’s one of the reasons why Hystax recently joined the OpenInfra Foundation, a community focused on building open, interoperable cloud infrastructure.

In this article, we share why open infrastructure initiatives continue to shape the future of cloud platforms.
➡️ https://hystax.com/hystax-joins-openinfra-foundation/?utm_source=facebook&utm_campaign=04.03.26

02/25/2026

Having cost data is easy.
Making it influence engineering decisions is the hard part⬇️

Most organizations already have cost data.
The real challenge is turning that data into decisions across engineering, finance, and leadership.

In our latest article, we break down practical FinOps best practices that help teams move from reactive cost tracking to structured cost governance:
📍Building shared accountability between teams
📍Creating cost visibility without friction
📍Aligning optimization efforts with business goals
📍Embedding FinOps into engineering workflows
FinOps is not a reporting exercise.
It’s an operating model.
If you're refining your cloud cost strategy in 2026, this overview may be useful: ➡️ https://hystax.com/finops-best-practices/?utm_source=facebook&utm_campaign=25.02.26

02/16/2026

Moving thousands of VMs from VMware without disrupting production isn’t just about speed.
It’s about architecture, parallelism, and operational control.

📅 Tuesday, March 3, 2026
🕒 10:00 AM PST
⏱ 45 minutes + Q&A

In our upcoming webinar, “Migration from VMware at scale: How to move thousands of VMs with minimal disruption,” we’ll walk through recent updates in Hystax Acura that help teams migrate VMware-based workloads more predictably and at scale — followed by a live technical demo.

The session will be led by Edwin Alexander-Kuss, Director of Global Sales at Hystax, with a live demo and Q&A from our CTO, Max Bozhenko.

We’ll cover:

✅ Parallel data transfer and distributed architecture
✅ Migration security improvements
✅ Advanced tuning for VMware replication agents

If you're planning or executing a migration from VMware, this may be useful.

👉 Registration link: https://app.livestorm.co/hystax/migration-from-vmware-at-scale-webinar/?utm_source=social&utm_medium=facebook&utm_campaign=16.02.26

Can’t attend live? Register, and we’ll send you the recording.

02/11/2026

Ⓜ️ When you have multiple ML models, opinions aren’t enough — you need objective comparison. Without clear evaluation criteria, “the best model” becomes a debate, not a decision.
Model leaderboards bring structure to experimentation: consistent metrics, transparent rankings, and apples-to-apples comparison across runs and datasets.
This article explains how ML/AI model leaderboards improve collaboration, validation, and decision-making — especially in MLOps environments where reproducibility and traceability matter.
If your team runs many experiments, this might change how you evaluate results.
➡️ https://kiroframe.com/machine-learning-ai-model-leaderboards/?utm_source=facebook&utm_campaign=11.02.26

02/03/2026

Backups don’t fail because the tech is bad — they fail because the strategy is weak.
The 3-2-1 backup strategy is still relevant in 2026, but only if you understand why each rule exists and where teams usually break it.
A clear, practical breakdown with real-world caveats
🔖 https://hystax.com/3-2-1-backup-strategy/?utm_source=facebook&utm_campaign=03.02.26

01/29/2026

“Transparency is where FinOps actually starts — not reporting.”
Here’s how one FinOps team described the shift in practice 👇

FinOps in practice isn’t really about tools.
It’s about finally making cloud spend clear, explainable, and actionable.

That’s why real practitioner feedback matters.
“Hystax OptScale has been a game-changer for our FinOps practice. Its powerful capabilities, flexibility, and seamless integration have empowered us to deliver unprecedented transparency, control, and cost optimization for our clients. We truly value our partnership with Hystax and are excited to innovate further together.”
— Max Kuzkin, General Manager, SoftwareOne Platform

When transparency is in place, FinOps moves beyond reporting
and starts influencing real decisions across teams.
More practical FinOps insights here: https://hystax.com/optscale/finops-overview/?utm_source=facebook&utm_campaign=29.01.26

01/21/2026

Ⓜ️ High accuracy doesn’t mean a good ML model.
If training and test data aren’t separated correctly, your metrics are lying.
🔖 This article explains — in clear, practical language — the difference between training data and test data, why this distinction is critical for ML engineers and MLOps teams, and how mistakes here lead to fragile models that collapse in production.
✅ If you’re building, deploying, or overseeing ML systems, this is foundational knowledge that directly affects reliability and business outcomes.
➡️ https://kiroframe.com/training-data-vs-test-data-in-machine-learning/?utm_source=facebook&utm_campaign=21.01.26

01/13/2026

🌟 A new year is a good moment to pause — and look ahead.

In 2026, our focus remains the same: helping infrastructure teams keep their data safe, recover fast when things go wrong, move workloads between platforms without unnecessary risk, and keep cloud costs under control as environments grow.

🔖 Backup, disaster recovery, migration, and cost optimization aren’t about trends or hype. They’re about reliability, predictability, and informed decisions — especially as infrastructures become more complex and multi-cloud by default.

🚀 We’re starting this year with a clear direction, steady progress, and a strong belief that practical engineering and financial transparency matter more than ever.

12/24/2025

Ⓜ️ Most ML problems don’t come from bad models — they come from broken workflows. Understanding the full machine learning lifecycle is what separates experiments from production-ready systems.

This article outlines the key stages of the ML lifecycle and explains why understanding each step is critical — especially in an MLOps context, where reliability, repeatability, and long-term performance matter as much as accuracy.
If you’re working with ML (or planning to), this is a solid foundation for building systems that actually work in production. ➡️ https://kiroframe.com/exploring-the-crucial-stages-of-the-life-cycle-in-machine-learning/?utm_source=facebook&utm_campaign=24.12.25