Nearly half of enterprise storage spend can be tied to scaling surprises —and that reality changes how organisations in Singapore pick a platform.

We set a clear frame: this piece compares two top storage approaches so you can match technical trade-offs to business goals. We focus on performance, resilience, and predictable cost across multiple environments.

vSAN shines when tight virtualization integration and policy-driven management matter. The open-source alternative brings unified block, file, and object capabilities and strong self-healing for large clusters.

Our aim is practical: show which features and system choices reduce risk and speed outcomes — from network needs to team skills and local connectivity in Singapore. We guide your next step with confident, usable advice.

Key Takeaways

• We compare Ceph vs vSAN to align technical trade-offs with business outcomes.
• Expect different capacity and cost profiles—licensing and consumed storage matter.
• Performance depends on network and media choices — SSD/NVMe and 10GbE+ help.
• vSAN fits virtualization-first shops; the open system fits diverse platforms.
• Operational skills and local connectivity affect total impact and risk.

Overview: Software-Defined Storage choices in Singapore today

Singapore organisations are shifting to software-led storage to escape hardware lock-in. We see a clear move from fixed arrays to platforms that decouple features from proprietary devices. This lets teams iterate faster and reuse commodity infrastructure.

Why enterprises are moving beyond traditional SAN/NAS

Traditional SAN/NAS struggle with scale and vendor lock. Organisations want portability across hardware and predictable costs.

Software-defined storage places data control in software, not a siloed appliance. That reduces procurement cycles and lets you pick the best devices for a given workload.

Present-day priorities: performance, resilience, scalability, and cost

Buyers in Singapore prioritise measurable performance, resilient operations, and straightforward scale inside compact data centres. Fast networking—25/100GbE and dark fibre—has accelerated adoption.

We note two practical alignments: one solution fits VMware-first virtualization, while the other favours heterogeneous stacks and multi-protocol needs. Governance, staffing, and network build-outs remain real cost drivers.

What is vSAN? Native VMware virtual SAN for vSphere

vSphere includes a native storage layer that places policy control directly inside the hypervisor. This design removes an external array and shortens I/O paths, delivering lower latency for VMs and predictable performance for business workloads in Singapore data centres.

Hypervisor-level integration means the storage plane runs alongside compute. Administrators set storage policies that define availability (FTT), RAID choices, and QoS. The hypervisor enforces these rules automatically and exposes them through the vSphere Client for unified management.

How it pools local NVMe/SSD/HDD across ESXi hosts

Local NVMe or SSD often serve as cache while SSD/HDD handle capacity. The hypervisor aggregates those disks and drives into a shared datastore across the cluster.

• Low-latency I/O: fewer hops from VM to block storage improves response times.
• Policy-driven management: set availability and performance targets once—vSphere enforces them.
• Linear scaling: add ESXi hosts to grow capacity and compute in step with procurement cycles.
• Native tooling: monitoring, lifecycle, and alerts via vSphere Client reduce third-party overhead.

Design choices—dedupe, compression, RAID-1 vs RAID-5/6, and cache sizing—impact usable capacity and throughput. A stable network backplane, balanced CPU/memory per host, and firmware consistency are prerequisites for predictable behaviour at scale.

What is Ceph? Open-source, unified block, file, and object storage

Open, scale-out storage brings VM disks, shared files, and S3 APIs together under one control plane. We describe the core components and practical requirements for production deployments in Singapore.

Core components that run the cluster

Monitors (MON) maintain cluster maps and health. They prevent a single point of failure.

OSD daemons store and replicate data, handle recovery, and rebalancing. Each osd typically consumes ~4GB of ram and needs CPU and local drives.

MDS serves metadata for shared file systems. The Manager provides telemetry and operational tools.

Interfaces, scale, and tuning

RBD offers block devices for VMs, CephFS exposes file shares, and RGW supplies S3-compatible object APIs.

Performance improves with SSD/NVMe for journals/WAL/DB. We recommend 10GbE+ networks and at least three nodes for production scalability.

Configuration levers—replication factor, erasure coding profiles, and CRUSH maps—control placement, fault domains, and capacity. The system rewards expertise with flexibility and cost control at scale.

Architectural differences that impact your environment

Architectural choices shape how storage behaves under load and how your team manages risk.

Tight hypervisor coupling versus independent scale-out

vSAN follows a hypervisor-native path. That reduces I/O hops and helps latency‑sensitive VMs. But scale ties to ESXi cluster boundaries and homogeneous host profiles.

Ceph separates compute and storage. You can grow storage clusters independently and mix hardware generations. This supports multi‑protocol access and diverse infrastructure needs.

Failure domains and placement control

vSAN uses storage policies (FTT, RAID choices) to set fault tolerance per VM. Policies are simple to apply but map to cluster limits.

CRUSH maps provide rack, room, and site placement. They give granular control of data placement and recovery at scale.

Operational model and system complexity

vSAN delivers a turnkey configuration and single-pane management inside vSphere. Teams familiar with VMware get fast time-to-deploy.

The alternative demands distributed systems expertise for configuration and day‑2 operations. That extra complexity buys flexibility and broader scaling options.

• Decision case: choose coupling for speed and predictable performance.
• Choose decoupling for independent scalability and multi-protocol coverage.

Performance and latency: tuning for virtual machines and beyond

Real-world performance demands force teams to balance CPU, network, and disk choices. We focus on concrete tuning levers that reduce latency for VMs and raise throughput for analytics and object cases.

vSAN advantages for VM I/O paths and latency-sensitive workloads

Hypervisor-level data paths shorten I/O routes and cut context switches. That reduces tail latency for small random I/O—critical for databases and VDI.

Policy-driven controls also let admins set QoS per VM, which keeps latency predictable under mixed workloads.

Performance knobs: NVMe OSDs, SSD journals, and MTU

Use NVMe for OSDs to lower queue depth stalls and unlock higher IOPS. Move journals/WAL/DB to SSD to remove write amplification on capacity drives.

Enable jumbo frames and align MTU across switches and hosts. A consistent MTU and flow-control tuning keep the network pipe full for multi-node writes.

Throughput at scale: when distributed storage wins

For AI/ML and big data, the distributed model scales throughput by adding nodes and parallel disks. Replication and erasure coding improve resilience—at a CPU cost—so size cores accordingly.

• Right-size cpu for encoding and recovery tasks.
• Run synthetic tests (fio) and real traces to set queue depths and read/write ratios.
• Keep firmware and driver baselines consistent across disks and NICs to avoid unpredictable stalls.

Observability matters: use Prometheus/Grafana for cluster metrics and vSphere counters for hypervisor paths. Early hotspot detection preserves performance and keeps operations predictable in Singapore data centres.

Scalability and growth across clusters and nodes

Scaling choices affect cost, operations, and long-term performance.

Linear growth with ESXi hosts lets you add capacity and compute in step. Each host contributes storage and CPU, so capacity and performance increase together. Procurement stays predictable and lifecycle alignment is simpler for maintenance windows in Singapore environments.

Independent scale-out across many nodes

Alternatively, you can grow storage independently by adding OSD-style nodes for capacity or IOPS. This model suits uneven demand and phased budgets. CRUSH-like placement maps keep data balanced and reduce hotspots during expansion.

Planning, rebalancing, and node profiles

Plan fault domains, rack awareness, and failure tolerance before adding nodes. Rebalancing behaviour differs: one approach redistributes data as hosts join, while the other reweights placement to keep performance steady.

• Right-size cache-to-capacity ratios for host-based setups.
• Choose OSD density and NVMe for high-throughput clusters.
• Enforce MTU, network reservations, and CPU headroom to avoid bottlenecks.

We advise forecasting growth and documenting expansion procedures. This governance cuts risk and keeps your storage solution predictable as clusters grow.

Fault tolerance, self-healing, and data protection

Faults happen — the key is how your storage stack detects and recovers without service loss.

vSAN policies, RAID options, and stretched clusters

vSAN implements fault tolerance through FTT settings and RAID-1/5/6 choices. Administrators set an FTT value to require copies or parity across hosts and disk groups.

Stretched cluster options add site-level resilience for metro deployments. That keeps data available during a site outage while enforcing synchronous writes where required.

Replication versus erasure coding

Open storage typically defaults to three-way replication for straightforward durability and fast rebuilds.

Erasure coding reduces capacity overhead but increases CPU and network load during writes and recovery. Choose it for archival or capacity-sensitive pools — not for the most latency-sensitive VMs.

Designing failure domains and practical runbooks

Define racks, rooms, and sites in placement maps to avoid correlated risk. The system will re-replicate or re-encode data automatically to restore policy compliance.

Watch for configuration pitfalls: insufficient bandwidth, misaligned MTU, or uneven device profiles can slow rebuilds and extend the fault window.

• Run periodic fault drills — simulate host and disk failures to validate recovery time.
• Align protection levels to application tiers — strict tolerance for databases, efficient profiles for archival storage.

Operational complexity, management, and ecosystem fit

Operational fit often decides which storage path an organisation picks. Management style, team skills, and the broader ecosystem shape day‑2 work and long‑term risk.

Single-pane vSphere Client operations

vSphere offers a unified console where provisioning, policy changes, and monitoring live together. That central view speeds capacity adds and maintenance windows for VMware-first environments.

The result: familiar tooling, fewer context switches, and faster routine tasks for admins who already manage ESXi hosts.

Dashboards, telemetry, and day‑2 toolchains

The open alternative provides a native dashboard plus Prometheus/Grafana for metrics-driven SLOs. Automation via Ansible or Terraform is common and supports CI/CD workflows.

Telemetry gives clear insight into data latency, rebuild rates, and capacity thresholds — essential for setting alerts before production cutover.

Skills, complexity, and staffing impact

Distributed systems tuning demands expertise — CRUSH maps, replication profiles, and rolling upgrades add operational complexity. Teams may need dedicated specialists or managed support.

Conversely, VMware admins can operate the hypervisor-integrated option with minimal retraining, lowering people costs and shortening time to value.

• Integration paths: RBD/driver compatibility, CSI for Kubernetes, and API-first workflows matter for multi-platform deployments.
• Hardware hygiene: consistent firmware and matched devices reduce surprises during rebuilds and upgrades.

Recommendation: pilot in a controlled environment, define SLOs and alerts, and build runbooks. This phased approach lowers risk and lets you validate management, performance, and network behaviour before scale.

Cost and TCO: licensing, hardware, and people

Licensing, hardware, and people costs together determine which storage approach wins in practice. We break down where budget is spent and how that maps to operations in Singapore environments.

vSAN licensing models and feature tiers

vSAN charges for features and capacity—dedupe, compression, and stretched clusters add to headline licence spend. Recent changes tie licence to consumed storage, so forecasting effective capacity is vital.

Open-source economics: hardware, networking, and expertise

The open model has no software licence cost but shifts spend to NVMe drives, 25/100GbE switching, and skilled engineers. Expect higher initial hardware and training expenses—then lower ongoing software fees at scale.

When each is more cost-effective

Small VMware-centric teams often find the hypervisor-integrated path cheaper in practice—management time and predictable support reduce hidden costs.

Large, multi-workload estates can lower total software spend by using an open platform and independent storage nodes.

Practical advice: pilot with clear metrics, size CPU and RAM headroom for rebuilds, and account for firmware and optics in TCO. This staged approach avoids surprises and protects service levels.

Deployment best practices and network considerations

A robust deployment starts with a clear network boundary that keeps storage traffic predictable. We recommend a dedicated 10/25/100GbE fabric and VLAN separation to isolate control and data paths.

Network design: 10/25/100GbE, jumbo frames, and traffic separation

Use a separate physical or logical network for storage traffic. Enable jumbo frames and align MTU across switches and hosts. This reduces CPU load and improves throughput.

Document flow control and QoS so storage packets get priority during contention. Test end-to-end connectivity before production cutover.

Recommended node specs: CPU, RAM, and NVMe for cache/metadata

Size nodes with CPU headroom for recovery and encoding tasks. Aim for a balanced CPU-to-RAM ratio and follow a 1GB RAM per TB guideline where applicable.

Place NVMe devices for cache, metadata, or WAL to lower latency for both block and file workloads. Match drive firmware and drivers across nodes to avoid surprises.

Policy design: storage policies vs pools and CRUSH tuning

Align policies to outcomes. Use FTT and RAID choices for simple availability targets with hypervisor-integrated stacks. For scale-out pools, use placement maps and rules to define failure domains and replication.

Keep policy templates and configuration artifacts in version control so teams can reproduce settings across environments.

Backup and DR patterns with Veeam, file, and S3-compatible object storage

Common DR patterns: Veeam backups to NAS (for example TrueNAS), and object-target snapshots to S3-compatible endpoints for offsite retention. Mirror critical backups over dark fiber for regional resilience.

Define number-driven acceptance tests: latency ceilings, throughput floors, and rebuild windows to validate readiness before go-live.

• Automate repeatable configuration with Ansible or Terraform.
• Run fault drills and measure rebuild time under load.
• Keep a published runbook for network and node failures.

Use cases: choosing the right solution for your workloads

Different workloads demand different trade-offs—latency, throughput, or simple management—and the right fit saves time and cost.

VMware‑centric private clouds, VDI, and HA VM clusters

vSAN maps to VMware-first estates where predictable latency and fast provisioning for virtual machines matter most.

It delivers policy-driven availability (FTT/RAID) and simple lifecycle ops — ideal for VDI, HA clusters, and private clouds in Singapore.

Hybrid portfolios: Kubernetes, OpenStack, archival, and big data

Open storage supports block and file interfaces, S3-compatible archives, and persistent volumes for Kubernetes.

Use replication for fast rebuilds or erasure coding to lower capacity cost on large analytics or AI/ML datasets.

Home lab and SMB scenarios

Small teams can run Proxmox with built-in distributed storage or adopt lightweight projects for a compact cluster.

We recommend three or more nodes for quorum and resilience. Pick RBD/block for VM disks and databases, and file for shared POSIX workloads.

“Match policy choices to outcomes — FTT and RAID for VM SLAs; replication or erasure coding for capacity efficiency.”

• Outcome: faster provisioning, consistent performance, and straightforward recovery.

Ceph vs vSAN

We focus on how integration, protocol support, and failure domains alter operational risk and cost for Singapore teams.

Integration and management

vSAN wins on native vSphere integration and a single-pane experience inside the hypervisor. That reduces context switching and shortens time to provision VMs.

The open platform uses a dashboard plus external tooling. This model gives rich telemetry and automation, but it asks for distributed storage expertise.

Flexibility of storage types and protocols

The open option delivers block, file, and object in one control plane. It suits mixed workloads—Kubernetes, object archives, and shared files.

The hypervisor-integrated option focuses on VM block storage, which simplifies management for VMware‑centric estates.

Performance, scalability, and failure domain control

Performance favors the hypervisor path for low-latency VM I/O. The networked design can match throughput with NVMe-accelerated OSDs and tuned networks.

Scalability differs: one solution scales within cluster bounds; the other expands to hundreds of nodes and petabytes with CRUSH-like placement for granular fault tolerance.

• Management: turnkey vs specialist operations.
• Hardware and cpu: plan cores for erasure coding and recovery tasks.
• Fault tolerance: policy-driven protection or rack/site-aware placement.

Choose the solution that maps to your platform, growth plans, and appetite for operational complexity.

Regional notes for Singapore environments

Singapore’s dense data centres demand network choices that match low-latency storage needs. We focus on practical measures—uplinks, fibre, sourcing, and operational tests—that keep service levels predictable.

Uplinking 25/100GbE cores and dark fiber considerations

Leverage 25/100GbE uplinks for storage traffic. These links cut replication time and speed rebuilds during failures.

Dark fibre suits offsite backups, active‑active replication, and stretched cluster links if latency SLOs are met. Test latency and throughput before enabling synchronous writes.

Hardware sourcing, support, and compliance in APAC

Plan lead times and support SLAs carefully—APAC distributor windows vary. Buy with matched firmware and vendor compliance to avoid rebuild surprises.

• Allow CPU and link headroom for peak rebuilds and DR tests.
• Align MTU and routing across carriers to keep data paths consistent.
• Zone racks and rows to define clear failure domains for each cluster node.

Operational pattern: run Veeam backups to TrueNAS locally, then mirror copies over dark fibre for resilient restores. This keeps data available and helps meet performance and recovery targets.

Decision framework: mapping requirements to the right solution

A pragmatic decision framework ties use cases to measurable acceptance criteria and clear runbooks.

We map requirements across platform alignment, protocol use, and compliance to build a focused shortlist.

Next, we quantify constraints—cpu headroom, device mix, and network architecture—to define safe operating envelopes.

Configuration baselines include policy templates for virtual san and pool/CRUSH-style rules for open platforms. These baselines tie directly to availability and performance targets.

“Pilot with a small cluster, measure latency and throughput, then validate runbooks before wider adoption.”

• Set numeric acceptance criteria: latency ceilings, throughput floors, rebuild windows, and RPO/RTO.
• Assess team skills: VMware operations versus distributed storage engineering and managed support options.
• Score cases by budget, timeline, and operational risk to pick a practical solution fast.

Conclusion

A practical storage decision ties workload requirements to a realistic operations plan and budget.

There is no one-size-fits-all answer — choose the solution that matches your platform, team, and growth model.

For virtualization-led estates, the tight hypervisor path delivers low-latency I/O and streamlined operations. For broad protocol needs, an open, scale-out option gives unified block, file, and object access and cost control at scale.

Plan for performance by right-sizing cpu, network, and policies. Mix approaches where it makes sense — for example, use a virtual san for core VMs and an object-backed pool for backups and analytics over 25/100GbE or dark fibre links in Singapore.

We recommend pilot-first adoption: measure against clear success criteria, then scale with confidence. Our team can help define requirements, benchmark options, and implement the right path.