Arcfra AECP 6.3 Breaks the 11M IOPS Barrier, Delivering Tier-1 All-Flash Performance and RPO=0 Resilience for Enterprise Cloud

Arcfra launches AECP 6.3 with 11M+ IOPS (comparable to tier-1 all-flash storage) on a 3-node cluster. Native RPO=0 replication and built-in security deliver a fast, cost-efficient VMware alternative option.

Arcfra Recognized as A Sample Vendor in the 2025 Gartner® A Guide to Choosing a VMware Alternative in the Wake of Broadcom Acquisition

A recent Gartner® report, A Guide to Choosing a VMware Alternative in the Wake of Broadcom Acquisition, has identified Arcfra as one of the Sample Vendors for Hyperconverged Infrastructure (HCI).

The AI Threat Is No Longer Theoretical. Is Your Infrastructure Ready?

In a 13-day window, the IMF, Cambridge Centre for Alternative Finance, and Google all confirmed what security teams feared: AI-generated cyberattacks are here. Your cloud architecture is either part of the defense — or part of the problem.

Navigating VMware Migration: From Planning to Execution

Full VMware VM migration guide: compatibility check, planning, environment prep, pre-migration test, and smooth final migration.

Arcfra AECP 6.3 Deep Dive | Four Key Features to Simplify Large-scale VM Operations

Arcfra AECP 6.3 simplifies large-scale VM O&M with batch VMTools upgrades, SSH port modification, NTP sync, and multi-level VM grouping.

How does Arcfra's unified management model work from core to edge?

Arcfra provides a single, unified platform spanning core, edge, and colocation — enabling organizations to standardize infrastructure and operations across all locations through Arcfra Operation Cente

How does Arcfra provide enterprise-grade reliability for critical edge workloads?

As edge deployments move from pilot to production, enterprises need high availability, disaster recovery, and security. Arcfra delivers this through active-active stretched clusters across sites, micr

What problems do fragmented edge architectures create for enterprises?

Fragmented edge architectures — where compute, storage, networking, and orchestration are managed as separate layers — create integration complexity and limit scalability across distributed environmen

What capabilities does GigaOm evaluate for full-stack edge deployment platforms?

GigaOm's 2026 Radar evaluates full-stack edge platforms across eight key features and three business criteria. This Q&A explains the evaluation framework and Arcfra's positioning in each dimension.

Why are hyperscalers like AWS and Google Cloud losing ground in the full-stack edge deployment market?

GigaOm's 2026 Radar report reveals that hyperscalers have reduced investments in their edge portfolios despite strong initial strategies. Here is why specialized edge vendors are gaining ground.

What areas does GigaOm identify as Arcfra's priorities for future development?

GigaOm's 2026 Radar identifies three areas where Arcfra is focused on future development: cloud integrations, DevOps tooling, and marketplace expansion. Here is what each means for buyers.

How does Arcfra score compared to AWS, Cisco-Nutanix, Dell, VMware, Azion, and other full-stack edge vendors in the 2026 GigaOm Radar?

Detailed comparison of Arcfra's scores against all 16 vendors in the 2026 GigaOm Radar across key features, emerging features, and business criteria. Includes analysis of where Arcfra leads and where

How should enterprise buyers read the GigaOm Radar and apply it to edge infrastructure procurement decisions?

The GigaOm Radar plots vendors on two axes (Maturity vs Innovation, Feature Play vs Platform Play) with concentric rings. Learn how to read it correctly and apply it to real edge infrastructure procur

What is the difference between scale-up, scale-out, and scale-down in edge deployments, and why does it matter for platform selection?

GigaOm evaluates edge platforms across three scalability dimensions -- scale-up (bigger nodes), scale-out (more nodes), and scale-down (smaller devices). Understanding these helps buyers match platfor

Why is Edge AI inference becoming a key evaluation dimension in the 2026 GigaOm Radar?

Edge AI inference -- running machine learning inference at the edge rather than in the cloud -- is a new evaluation dimension in GigaOm's 2026 Radar. Here is why it matters and what it means for edge