According to a Statista report, by 2029, the global data generation is projected to reach 527.5 zettabytes, roughly triple the 2025 levels. At the same time, Kubernetes underpins a large share of modern cloud-native stacks and Gartner projects that by 2028, 80% of GenAI business apps will be developed on existing data management platforms.
Object storage is becoming the default solution for modern application data scaling because of its near-linear scalability, strong cost-efficiency at large scale and simple flat architecture. It is built to handle the sheer volume and variety of data generated by cloud-native applications, AI/ML workflows and global data lakes.
Together, these forces favor an API-first, globally replicated, versioned object layer that scales through parallelism, contains risk with immutability and keeps your unit economics more predictable at a global scale.
What is Object Storage?
Object storage, sometimes called object-based storage, is a data storage architecture that you can use to handle very large volumes of unstructured data. Instead of organizing information as files in folders or fixed-size blocks, it stores data as independent objects. Each object includes the data itself, descriptive metadata and a unique identifier that makes it easy to find and access.
These objects can live in on-premises systems, but are most often stored in the cloud, where they can be accessed from virtually anywhere. Because object storage uses a scale-out design, it can grow to accommodate massive datasets with minimal complexity. This often makes it more cost-effective for large, mostly read-heavy or archival data volumes than block storage.
What are the Key Reasons to Adopt Object Storage for Modern Apps?
Object storage is becoming the default for scaling data in modern applications due to its practical advantages across scalability, cost, resilience, security and cloud readiness.
Increased Scalability
Scalability is arguably the biggest advantage of object-based data storage. It uses a flat address space (storage pools or “buckets”) within a storage system like a server. When you need more capacity or throughput, you simply add more devices or servers in parallel to the object storage cluster. This makes it easier to handle large files like videos or images and the higher bandwidth they require.
Reduced complexity
Removing hierarchical folders and directories reduces lookup overhead and administrative burden for large data estates. Because there are no nested paths to traverse, there is less risk of performance bottlenecks and data retrieval becomes more efficient. This simplicity is especially valuable when you are managing very large volumes of data.
High availability and durability
Object storage can replicate or erasure-code data across disks, nodes and clusters to survive failures. Operations continue during a disk or node loss, while redundancy prevents data loss in most scenarios. Replication can span a single site or multiple regions to provide availability of targets and off-site disaster recovery.
Searchability
Each object carries rich metadata that records descriptive attributes, lifecycle tags and custom context for governance. Metadata supports precise search, filtering and analytics by exposing consistent keys for business and technical queries. These capabilities improve discovery and inform data protection, retention and market insight initiatives across teams.
Cost efficiency
Most providers use a pay-as-you-go pricing model that removes the need for large upfront capital investments. Costs are tied to actual usage, including storage volume, retrieval activity, bandwidth and API calls. Tiered classes reduce the cost for infrequently accessed data while keeping hot objects on higher performance media. Solutions often run on standard hardware, which preserves existing investments and supports an incremental, vendor-neutral scale.
Security
Object storage offers a comprehensive set of security features, including encryption at rest and in transit and strong access controls through IAM policies. Many platforms also support multifactor authentication, data loss prevention capabilities and integration with enterprise security tools for centralized monitoring and threat detection.
Cloud compatibility
Object storage is tightly aligned with cloud and hosted environments that deliver multitenant storage as a service. Multiple organizations or departments can share a common platform while retaining segregated space, improving scalability and cost efficiency. Using low-cost cloud-based object storage lets you shrink on-premises infrastructure yet keep data readily accessible. For example, your enterprise can capture and store large volumes of unstructured IoT and mobile data that power device applications.
How is Object Storage Different from Block and File Storage?
To understand why object storage is becoming the default for modern apps, it helps to compare it directly with block and file storage.
| Aspect | Block storage | File storage | Object storage |
|---|---|---|---|
| Data model | Fixed-size blocks presented as volumes | Files in directories with a hierarchical namespace | Metadata-rich objects in a flat namespace of buckets and keys |
| Access method | Attached to OS via iSCSI or NVMe | Shared via NFS or SMB with POSIX semantics | Accessed over HTTP(S) using S3-compatible APIs and other vendor-specific or RESTful APIs |
| Typical latency | Very low for transactional I/O | Low for shared file workloads | Higher than block or file, optimized for throughput and concurrency |
| Scaling pattern | Add and manage more volumes, snapshots and replicas | Scale up appliances or deploy distributed file systems | Scale out by adding nodes, virtually unlimited object count |
| Durability features | RAID at host or array level | Snapshots and replicas at filesystem or array level | Replication or erasure coding across disks, nodes and regions |
| Operational complexity | Per-volume lifecycle and tight host coupling | Directory, inode and metadata limits at very large scale | Policy-driven lifecycle with less hierarchy to manage |
| Cost profile | Higher cost per GB for large cold datasets | Moderate cost with growing overhead at scale | Lower cost per GB at scale with tiering and lifecycle rules |
| Best fit | OLTP databases, VM boot volumes, low-latency logs | Shared home directories, legacy apps that require POSIX | Unstructured and semi-structured data at large scale, such as media, logs, backups, analytics datasets and data lakes |
| Example protocols/services | iSCSI, NVMe, Fibre Channel | NFS, SMB | S3 API and compatible services |
Key Takeaways:
- Use block storage for low-latency transactional I/O (databases, boot volumes).
- Use file storage when you need shared POSIX semantics or legacy apps.
- Use object storage for unstructured data at scale, especially logs, media, backups, analytics and data lakes.
For a deeper comparison of architectures and use cases, read our guide: Block Storage vs Object Storage
What are the Tiers of Object Storage and When Should You Use Them?
Most object storage platforms offer multiple storage tiers, so you can match cost, performance and durability to how “hot” your data really is. While names differ by provider, they generally fall into four categories:
1. Hot/ Standard tier
Designed for frequently accessed data.
- Lowest latency and highest throughput.
- Ideal for active application data, recent logs, user uploads and ML training datasets.
- Highest cost per GB but minimal or no retrieval penalties.
2. Warm/ Infrequent Access tier
Optimized for data that is read occasionally but must remain quickly available.
- Lower cost per GB than hot tiers.
- Slightly higher access and retrieval charges.
- Good fit for monthly reports, recently closed projects, older but still relevant analytics datasets.
3. Cold/ Archive tier
Meant for long-term retention where access is rare and retrieval can tolerate minutes or hours.
- Very low cost per GB.
- Higher retrieval fees and possible minimum storage duration.
- Suited for compliance archives, historical logs, old backups and media that must be retained but is rarely used.
4. Deep archive/ Vault tier
For data you keep “just in case”.
- Lowest cost per GB with strict retrieval and minimum-retention constraints.
- Retrieval can take many hours and is often batch-oriented.
- Appropriate for e-discovery archives, long-tail regulatory records and raw telemetry retained for future analysis.
Across these tiers, lifecycle policies let you automatically move objects from hot to cold as they age or delete them entirely after a retention period. That way, you avoid paying hot-storage pricing for cold data, while still keeping a single, consistent object namespace and API for your applications.
How to Scale Data Using Object Storage?
Scaling data with object storage requires structural design, policies and replication that anticipate future growth.
Design buckets and keys for growth
Start by modeling buckets and key prefixes for scale, not convenience. Instead of one bucket per app, segment by domain (for example logs/, analytics/ and ml-artifacts/) and use structured prefixes like tenant/app/year/month/day or region/workload/type. Good prefix design lets the object store distribute load evenly and prevents hot partitions as you grow to billions of objects.
Use lifecycle policies and storage tiers
Combine that layout with lifecycle rules and tiered storage. Keep fresh high-traffic objects in a standard tier then automatically move older or less-accessed data into infrequent-access or archive tiers. This keeps a single namespace and API surface while aligning spend with actual access patterns.
Replicate and version across failure domains
Scale safely by turning on versioning and replication. Cross-zone or cross-region replication protects against local failures and outages while versioning shields you from accidental deletes, corruption and ransomware. You gain global durability without managing complex volume-level replication.
Make object storage the system of record
For analytics and ML, treat object storage as the authoritative data layer. Store raw and curated datasets in open formats such as Parquet or ORC and let engines like Spark, Trino or other lakehouse platforms read directly from buckets. Wrap it all with observability: monitor request rates, errors, bucket growth and replication lag and enforce IAM boundaries so teams can scale independently without constant re-architecture.
Scale Object Storage with Confidence on AceCloud
As data volumes accelerate toward the zettabyte era, you can’t afford storage that tops out or blows up your TCO. Object storage gives you the elasticity, durability and cloud-native alignment for the modern apps demand.
AceCloud turns that into a production-ready reality with GPU-first infrastructure, S3-compatible object storage and a 99.99%* uptime SLA.
If you’re running Kubernetes, AI/ML or analytics at scale, now is the time to make object storage your default tier. Talk to AceCloud solution architects to review your current estate, map quick-win migrations and launch a resilient, cost-efficient object storage foundation tailored to your workloads.
Start your proof of concept today and see how your data layer can scale.
