Cloud block storage is the better choice when application data needs a lifecycle separate from the VM. Local disk is the simpler choice when the data belongs to one server and can be rebuilt, replaced, or restored together with that server.
The distinction is primarily about lifecycle and operational ownership, not a universal claim that one storage type is always faster.
At Raff Technologies, a practical default is: keep the operating system, packages, caches, temporary files, and other replaceable runtime data on the VM disk; use a separate Volume when a database, container, or application needs persistent filesystem storage that should survive VM replacement or grow independently from compute.
In this guide, local disk means storage operationally coupled to one VM. It does not imply a specific physical disk topology underneath the cloud platform.
Cloud block storage vs local disk: quick answer
| Decision factor | Local / VM disk | Cloud block storage |
|---|---|---|
| Lifecycle | Coupled to one VM | Separate storage resource |
| Best fit | OS, packages, caches, temp files, rebuildable runtime | Databases, container state, persistent application filesystems |
| Capacity growth | Usually follows the VM/system-disk model | Can grow separately from compute |
| VM replacement | Data moves/restores with the VM process | Volume can remain a separate persistent layer |
| Application interface | Normal local filesystem | Normal filesystem after attach/mount |
| Multi-server shared access | No | Not automatically; ordinary block volumes are usually single-host storage |
| Backup replacement | No | No — persistence is not backup |
| Operational work | Lowest | Attach, mount, monitor, resize, protect |
A useful rule is:
If replacing the VM should not mean replacing the data, separate that data from the VM’s boot disk.
For the broader storage decision that also compares object storage, read Object Storage vs Block Storage vs VM Disk.
What cloud block storage actually is
Cloud block storage presents storage to a VM as a disk-like block device. The operating system can format it with a filesystem and mount it at a normal path.
A simplified Linux example looks like:
VM ↓ attached block device ↓ filesystem ↓ /mnt/data ↓ database / container / application
The important point is that the storage resource has a lifecycle distinct from the VM’s system disk.
That separation can make several operational tasks cleaner:
- storage capacity can grow without changing CPU or RAM;
- persistent application data can live outside the boot disk;
- a VM can be rebuilt without making the root disk the only copy of active state;
- storage monitoring can be separated from operating-system disk pressure;
- persistent container or service state can use a dedicated mount point;
- recovery procedures can distinguish compute replacement from data recovery.
Cloud block storage is still active primary storage. It is not automatically a backup, replica, or disaster-recovery system.
What local disk means in a cloud VM
“Local disk” is an overloaded term.
Some cloud providers use it to describe physically local ephemeral NVMe. Others use phrases such as local storage, VM disk, root disk, system disk, or boot disk for storage tied operationally to one virtual machine.
For architecture decisions, the useful question is not the marketing label. It is:
What happens to this data when the VM is rebuilt, replaced, resized, reimaged, or deleted?
Data that is naturally part of one VM’s runtime often belongs on the VM disk:
- operating-system files;
- packages and dependencies;
- application binaries;
- deployment files that can be recreated;
- package and container caches;
- temporary processing files;
- bounded local logs;
- generated artifacts that can be rebuilt safely.
This keeps the design simple. A separate volume is not automatically better just because it is more “cloud-native.”
Choose local disk for replaceable runtime
Local disk is usually the right default when losing the disk together with the VM would not create an unacceptable business-data loss event.
Good examples include:
Operating system and application runtime
The OS, packages, runtime binaries, service definitions, and deployment artifacts generally belong with the VM.
If the server can be recreated from automation, documentation, images, or deployment tooling, keeping these files on the system disk avoids unnecessary storage complexity.
Caches
Caches are normally rebuildable. Separating them onto persistent block storage can increase cost and operational work without adding meaningful resilience.
Temporary processing space
Image transformations, build workspaces, package extraction, temporary exports, and scratch files often fit local disk well when the process can safely restart.
Bounded logs
Local logs are reasonable when retention is controlled and important logs are shipped to an external monitoring or logging destination.
The key is bounded. An unbounded log directory on the boot disk can still fill the server and cause application failures.
Choose cloud block storage for persistent filesystem state
Use a block volume when software needs a mounted filesystem and the data should be managed separately from the VM root disk.
Common fits include:
- self-hosted database data directories;
- persistent Docker or container state;
- application data directories;
- search indexes;
- package or artifact repositories that require filesystem semantics;
- growing storage workloads where CPU/RAM demand is stable;
- service state that should not live inside the boot disk lifecycle.
A clean model is:
VM system disk └── OS + application runtime Attached block volume └── persistent service data
That boundary makes it easier to explain what belongs to the server and what belongs to the workload’s durable state.
Databases are a common block-storage use case
A self-hosted database expects filesystem and disk behavior, which makes block storage a natural fit when the platform’s performance and recovery characteristics match the workload.
A typical design is:
Application VM ↓ private network Database VM ↓ Block volume ↓ Database files
But moving the data directory to a volume does not make the database managed.
Your team still owns:
- database installation and patching;
- authentication;
- network exposure;
- filesystem and mount health;
- storage growth;
- database-aware backups;
- restore testing;
- replication or HA where required;
- upgrades and incident response.
A volume improves the storage boundary. It does not remove database operations.
If reducing that operational ownership matters more than root-level control, compare this architecture with Managed Database vs Self-Hosted.
Persistent container data should not depend on the container lifecycle
Containers are designed to be recreated. Important data therefore needs an explicit storage owner.
For filesystem-dependent state, a block volume can provide a clean persistent path:
VM / container host ↓ container ↓ mounted directory block volume
Examples include:
- self-hosted database containers;
- application state directories;
- indexes;
- plugin or extension data;
- queue/service state that genuinely needs a filesystem.
Do not move every file to block storage just because a container writes it. User uploads, media, reports, and other file objects shared by multiple application instances may fit Object Storage better.
A separate volume helps when storage grows faster than compute
One of the strongest commercial and architectural reasons to separate storage is independent scaling.
Suppose an application VM has enough CPU and memory but its persistent dataset keeps growing.
With storage tightly coupled to the server:
more data → pressure on system disk → possible server resize or migration
With a separate volume:
compute remains adequate → volume capacity grows independently
This prevents a storage-capacity problem from automatically becoming a compute-purchasing decision.
The same logic applies in reverse. A CPU-heavy workload may need more compute without needing more persistent storage.
Separating the lifecycles lets each resource scale for the reason it actually needs to scale.
Block storage does not become shared storage automatically
A common mistake is assuming that a block volume is equivalent to a shared network filesystem.
It is not.
Ordinary filesystems such as ext4, XFS, or NTFS generally expect one host to control the block device. Several independent servers writing to the same block device without explicit multi-attach support and a cluster-aware filesystem can corrupt data.
If several application servers need the same files, consider whether the workload really needs:
- object storage for uploads, media, exports, and artifacts;
- a database for structured state;
- a purpose-built shared filesystem when normal filesystem semantics are mandatory.
Do not design multi-writer storage around assumptions that the platform or filesystem does not document.
Block storage is persistent storage, not backup
Separating data from the VM protects against some lifecycle problems. It does not protect against every data-loss scenario.
A block volume can still contain:
- accidentally deleted files;
- corrupted database pages;
- a destructive migration;
- ransomware-encrypted data;
- bad application writes;
- administrator mistakes.
If the bad change reaches the active volume, persistence faithfully preserves the bad change.
That is why production storage architecture usually needs a separate recovery layer.
Use Snapshots vs Backups for the recovery-control decision and Cloud Storage Architecture for Production Apps: Backup & Recovery for the wider architecture.
Performance depends on the workload, not the label
A simple “local disk is faster” rule is too broad for production architecture.
Actual storage performance depends on factors such as:
- latency;
- IOPS;
- throughput;
- queue depth;
- read/write size;
- sequential vs random access;
- synchronous database writes;
- filesystem configuration;
- caching;
- VM and storage contention;
- platform limits.
A database performing many small synchronous writes has very different requirements from a backup archive or media store.
For important workloads, benchmark representative operations on the actual environment rather than relying on one sequential throughput number.
Measure the metrics that affect the application: transaction latency, query latency, IOPS, throughput, queueing, and filesystem utilization.
Local disk can still be correct for small production workloads
Separating every stateful directory on day one can create more moving parts than value.
A small production application may reasonably begin with one VM when:
- the dataset is small;
- growth is predictable;
- backups are tested;
- downtime tolerance is understood;
- the team can restore the whole server quickly;
- independent storage scaling is not yet needed.
The trigger to add a volume should be a real operational requirement, such as:
- persistent state needs its own lifecycle;
- disk growth is outpacing compute needs;
- a service needs a dedicated filesystem;
- VM replacement should not also become a data migration;
- root-disk pressure is creating operational risk.
Complexity should solve a problem, not merely signal architectural maturity.
Mounting a volume creates operating-system responsibility
A separate block volume is not zero-operations storage.
The team should manage:
- filesystem creation where required;
- mount point selection;
- persistent mount configuration;
- permissions and ownership;
- filesystem utilization;
- monitoring and alerts;
- resize procedures;
- safe detach/reattach workflows;
- backup and recovery procedures.
On Linux, persistent mounting should use stable identifiers rather than assuming a device name will never change.
Always test a reboot. A volume that is attached but not mounted can cause an application to write into the underlying empty mount directory on the boot disk, silently splitting data across two locations.
For the procedural side, the cluster also contains Block Storage Volume Setup for Linux VMs.
Capacity planning should include operational headroom
Do not size a volume only for the current dataset.
Allow for:
- database and index growth;
- temporary migration space;
- compaction or vacuum operations;
- log retention;
- container image or artifact growth;
- backup staging;
- filesystem overhead;
- future application data.
Monitor both percentage used and rate of growth.
A filesystem at 70% with rapidly accelerating growth may be a bigger risk than one at 85% with stable usage.
Local disk, block storage, and object storage often work together
A common production architecture is not an either/or choice.
VM disk ├── OS ├── application runtime ├── cache └── temporary files Block volume └── persistent database / service filesystem Object storage └── uploads / media / reports / archives Backup layer └── recovery points + tested restore process
Each layer has a different job.
This separation can keep the server replaceable without forcing every piece of state into object storage or every file onto a block volume.
How Raff Volumes fit this decision
Raff Volumes are the current block-storage layer for Raff VMs.
The live Volumes page documents the current volume sizes, pricing, attachment behavior, online growth, snapshot support, and other product-specific limits. Because these details can change, the product page—not a dated guide—is the source of truth for current commercial values.
As reviewed on September 16, 2026, Raff documents that Volumes:
- are separate persistent block-storage resources;
- attach to Raff VMs as disk devices;
- can grow independently from VM compute;
- can remain after the VM is deleted and be attached again;
- support online growth while attached;
- integrate with the platform’s snapshot/data-protection workflow.
The architectural implication is more important than a fixed price quote: use a Volume when the workload needs persistent filesystem semantics and a lifecycle separate from the VM boot disk.
Use Raff VM for compute and runtime, Volumes for persistent block state, Object Storage for API-accessed durable files, and Data Protection for recovery workflows.
