Block Storage vs Object Storage

‍Quick Answer
‍
• Block storage delivers sub-millisecond, byte-level I/O and is ideal for databases, VMs, and Kubernetes persistent volumes.
• Object storage offers near-unlimited scale through an HTTP API and is built for backups, logs, media files, and ML datasets.
• Most production architectures use both. Choosing the wrong tier costs you in performance, budget, or both.

‍

5 Key Differences at a Glance

1. Access method: Block storage mounts like a local disk. Object storage is accessed via HTTP or S3 API.
2. Latency: Block storage is sub-millisecond. Object storage adds API round-trip overhead measured in milliseconds.
3. Mutability: Block storage supports in-place byte-level writes. Object storage is write-once and replace-to-update.
4. Scale model: Block storage is provisioned capacity. Object storage scales virtually without limits.
5. Cost model: Block storage is priced per GB provisioned. Object storage is priced per GB stored or used.

Introduction

Almost every infrastructure team hits the same fork in the road: block storage vs object storage. Which one does this workload actually need? It sounds like a simple question, but the difference between block storage and object storage is not just technical trivia. Choosing the wrong tier can mean performance degradation, unexpected budget overruns, or both.

In this blog, we explain precisely what each storage type does, where each performs best, how they compare on cost, and how to architect them together in real-world production environments.

What Is Block Storage?

Block storage divides data into fixed-size chunks, called blocks, each addressed by a numerical offset. Think of a physical hard drive at the OS level. When you attach a block volume to a virtual machine or bare-metal server, the operating system sees a raw mountable disk. It has no idea the storage is network-based.

That low-level abstraction is precisely the point. Because the OS has full, direct control over the device, block storage delivers the sub-millisecond latency and byte-level mutability that demanding applications require. You can format it with any filesystem such as ext4, XFS, or NTFS, run a relational or NoSQL database directly on it, or use it as the persistent volume backing a containerized workload.

When to Use Block Storage

• High-throughput databases: PostgreSQL, MySQL, MongoDB, Cassandra
• Boot volumes for virtual machines and bare-metal servers
• Persistent Volume Claims (PVCs) for Kubernetes workloads
• Transactional applications with frequent, small, random read and write patterns
• Any workload writing to specific byte offsets inside a file

Running latency-sensitive workloads?
‍‍
NetActuate block storage attaches high-performance network volumes directly to your VMs at $0.25/GB with no ingress charges and no over-provisioning penalties.
‍
Go to Explore NetActuate Block Storage
Go to Deploy VMs with Block Storage Today

 What Is Object Storage?

Object storage takes a fundamentally different approach. Instead of blocks and filesystems, it stores data as discrete objects. Each object is bundled with a unique key, arbitrary metadata, and the actual payload, and it is accessed through an HTTP API that is typically S3-compatible.

There is no directory hierarchy at the OS level, no in-place byte editing, and no mounting. You write an object, read an object, delete an object. What you gain in exchange is virtually unlimited scale, built-in geographic redundancy, and a cost model that makes storing terabytes or even petabytes of infrequently accessed data economically viable.

When to Use Object Storage

• Long-term backup archives and disaster recovery snapshots
• Centralized log retention for compliance, security, or troubleshooting
• Media assets including video files, images, and large binary packages
• Software release artifacts, container image layers, and build outputs
• Training datasets and inference outputs for ML and AI pipelines
• Any unstructured data written once and read occasionally

Storing backups, logs, or media at scale?
‍
NetActuate S3-compatible object storage scales automatically. You pay only for what you use with no upfront provisioning and no capacity planning headaches.
‍
Go to Explore NetActuate Object Storage‍

Block Storage vs Object Storage: Full Comparison

Core Architecture and Access Comparison

‍

‍
Use Case Matrix

‍

Not sure which storage tier fits your workload?
‍
NetActuate infrastructure architects can help you design the right storage architecture for your specific requirements, whether that is database-grade block volumes, S3-compatible object storage, or a custom configuration.
‍
Go to Explore NetActuate Custom Storage
Go to Speak with an Infrastructure Architect

Cost Model and TCO Breakdown

Storage costs are one of the most misunderstood areas of infrastructure budgeting. Block and object storage have fundamentally different pricing structures, and conflating them leads to either over-provisioning block volumes or misusing object storage for workloads that require I/O performance.

Block Storage TCO Factors

• Priced per GB provisioned. You pay for the full volume even if utilization sits at 30 percent.
• IOPS tiers may incur additional cost on cloud platforms. NetActuate charges a flat rate of $0.25/GB.
• Snapshots and volume clones add cost on most providers.
• Over-provisioning is common. Teams typically provision two to three times actual usage to avoid emergencies.
• Best TCO profile: workloads with consistent, predictable I/O such as databases and application servers.

Object Storage TCO Factors

• Priced per GB stored. You pay only for what is actually written.
• API request costs (GET and PUT calls) can accumulate at high request volumes.
• Egress fees are the most common hidden cost on hyperscalers. NetActuate charges no ingress fees.
• Compression and tiering policies can dramatically reduce per-GB costs on large datasets.
• Best TCO profile: high-volume, rarely accessed data such as backups, logs, archival storage, and ML datasets.

‍See NetActuate storage pricing

Real-World Architecture Examples

Understanding the theory is one thing. Seeing how these patterns show up in actual production systems is another.

Example 1: SaaS Application Stack

Pattern: Block storage for the compute layer combined with object storage for the data-at-rest layer.

• PostgreSQL primary and replica nodes run on block volumes for high-IOPS, low-latency writes.
• Application server VMs mount block volumes for the OS and application binaries.
• User-uploaded files such as profile images and documents are written to S3-compatible object storage.
• Daily database backups are streamed to object storage through pg_dump or WAL archiving.
• Result: Transactional performance from block storage, with durable and low-cost archival handled by object storage.

Example 2: ML Training Infrastructure

Pattern: Object storage as the data lake, block storage for active compute.

• Raw training datasets in the hundreds of GBs to TBs range are stored in S3-compatible object storage.
• Training jobs pull data batches from object storage into local NVMe or attached block volumes.
• Model checkpoints are written back to object storage after each epoch.
• Inference endpoints run on VMs with block-backed local state.
• Result: Object storage serves as the cost-effective source of truth. Block storage handles active training compute.

Example 3: Global CDN and Media Platform

Pattern: Object storage as the origin, block storage for edge compute.

• Video and image assets are stored in object storage with geographic replication.
• Transcoding workers run on VMs with block volumes for scratch space during processing.
• Finished outputs are written back to object storage for CDN delivery.
• Access logs are written to object storage for analytics and compliance purposes.
• Result: Object storage as a scalable origin with block storage powering ephemeral compute tasks.

Deploy this architecture on NetActuate's Global Edge
‍
NetActuate storage deploys in every major market in 45+ global locations. You can place storage close to your users, anywhere in the world.

‍Go to NetActuate Global Data Centers

Migration Considerations

Switching storage tiers mid-lifecycle is more common than most teams expect. Growing data volumes, changing access patterns, and cloud cost optimization initiatives all create pressure to move data between storage types.

Migrating from Block to Object Storage

This is the most frequent migration scenario. Teams typically want to archive old database snapshots or log volumes from block storage to object storage to reduce cost.

• Use rclone, s5cmd, or any AWS CLI-compatible tool to copy data from block-mounted paths to S3-compatible endpoints.
• Implement lifecycle policies to automatically move data from block volumes to object storage after a defined retention period.
• Validate integrity with checksums before removing source data.
• Key risk: Applications that are hard-coded to file paths must be updated to use object storage SDK and API calls.

Migrating from Object to Block Storage

This scenario typically occurs when a team initially stored structured data in object storage for cost reasons but now requires low-latency random access.

• Mount a block volume alongside existing object storage and use parallel transfer tools for bulk copying.
• Update application connection strings and storage endpoints.
• Run both in parallel during cut-over to validate correctness.
• Key risk: Object storage was write-once. Make sure the application handles the block storage mutability model correctly after the switch.

The Hybrid Migration (Most Common)

The most common migration pattern is not switching between types at all. It is architecting a proper split where none previously existed. Organizations migrating off hyperscalers often find that everything was stored in object storage such as S3 or GCS because it was the path of least resistance, even for databases. Repatriation means introducing block storage for stateful compute while keeping object storage for archival workloads. See NetActuate's Cloud Repatriation solutions for more on how this works in practice.

How NetActuate Delivers Storage at the Global Edge

NetActuate's Global Edge Storage Platform is built for demanding workloads across 45 or more locations worldwide, with storage placed close to compute and users to minimize latency in distributed architectures.

‍

‍

All storage options are self-service through the NetActuate portal with full API and CI/CD integration support.
‍
Key differentiators:

• No egress fees. Unlike AWS, GCP, and Azure, NetActuate does not charge for data transfer out.
• Global placement. Store data in 45 or more locations, from Ashburn and New York to Frankfurt, Singapore, and Johannesburg.
• Infrastructure-first approach. Storage is co-located with compute, not bolted on as a separate cloud service.
• Backed by 24/7 infrastructure support and consulting services for complex architectures.

Ready to add storage to your edge workloads?
‍
Talk to a NetActuate infrastructure expert to find the right storage configuration for your environment.
‍
Go to Explore All Storage Options
Go to Schedule a Call with Our Team

Frequently Asked Questions

What is the main difference between block storage and object storage?

Block storage divides data into fixed-size blocks, mounts like a physical drive, and supports in-place byte-level writes. Object storage keeps data as objects accessible through an HTTP API, scales virtually without limit, but does not support in-place edits. Block storage is designed for low-latency transactional workloads. Object storage is designed for durable, cost-effective storage of large unstructured datasets.

Can I run a database on object storage?

No. Relational databases such as PostgreSQL and MySQL, and most NoSQL databases such as MongoDB and Cassandra, require in-place write operations and sub-millisecond I/O. Those are capabilities that only block storage can provide. Some purpose-built query engines like Athena or DuckDB in read-only mode can query data that lives in object storage, but they are not transactional databases in the traditional sense.

Is object storage slower than block storage?

Yes, for transactional workloads. Object storage adds HTTP round-trip latency, typically somewhere between 5 and 50 milliseconds depending on location and object size. Block storage operates at sub-millisecond latency for random I/O. That said, for large sequential reads such as loading a video file or a model checkpoint, object storage throughput can match or exceed block storage, especially with parallel multipart downloads.

When should I use both block storage and object storage?

Most production architectures use both. The typical pattern looks like this: databases and application servers run on block volumes for low-latency I/O, while backups, logs, media files, and datasets live in object storage for durability and cost efficiency. The decision is less about choosing one over the other and more about assigning each workload to the tier it was designed for.

What does S3-compatible mean for object storage?

S3-compatible means the object storage service implements the Amazon S3 API, including the same PUT, GET, DELETE, and multipart upload commands. Any tool or SDK built for AWS S3 such as boto3, rclone, s5cmd, or the Terraform S3 provider works against S3-compatible storage without code changes. NetActuate object storage is fully S3-compatible, which means zero retooling is required.

How do I choose between block storage and object storage for Kubernetes?

For Kubernetes workloads, use block storage for StatefulSets that require Persistent Volume Claims with ReadWriteOnce access. This includes databases, message queues, and stateful microservices. Use object storage through S3-compatible SDKs or CSI drivers such as Ceph RGW or MinIO for storing large files, artifacts, and data that needs to be accessed by multiple pods simultaneously.

‍