Object storage is a type of data storage architecture. It manages and organizes data as distinct objects, as opposed to the traditional file system hierarchy. Each object typically consists of data, metadata, and a unique identifier. Unlike traditional file systems, object storage does not organize data in a file-folder structure but instead stores data as objects in a flat address space.
Objects can be stored either on-premises, but more commonly they’re stored in the cloud. Some examples of object storage services include Amazon S3, Google Cloud Storage, and Azure Blob Storage. These services provide scalable and reliable object storage infrastructure for businesses and developers. It allows them to store, retrieve, and manage vast amounts of data efficiently.
As the amount of unstructured data continues to grow, object storage holds strong as a scalable and cost-effective option for organizing data. Let’s dive deeper into what it is and how it’s different from traditional filing systems.
How does object storage work?
Object storage wraps each piece of data in a container called an object, assigns it a globally unique identifier, and places it in a flat pool of storage that you address by key rather than by path. There are no nested directories to traverse. You ask for an object by its ID (often a bucket name plus a key), and the system returns it.
Three components make this possible.
Objects, metadata, and unique identifiers
Every object carries the raw data (a log file, an image, a compressed batch of events), system metadata such as size and timestamp, and custom metadata you define, like source type, host, or retention class. That metadata travels with the object and becomes the main way you organize and find things later.
A flat address space
Instead of a hierarchy, object stores use a single flat namespace per bucket or container. You can simulate folders with prefixes in the key name, such as 2026/06/11/firewall/, but under the hood there is no tree to rebalance. That is why object storage scales without the performance cliffs that hit file systems at billions of files.
API-driven access and distributed durability
You read and write objects over HTTP-based APIs, most commonly the S3 API, which has become a de facto standard even for on-premises and other cloud providers. Behind the API, the storage system distributes copies or erasure-coded fragments of each object across multiple nodes, racks, or availability zones. Lose a disk or a node and your data is still there.
Put it together and a typical write looks like this:
Your application or pipeline sends an object and its metadata to a bucket via the API.
The object store assigns or accepts a unique key and records the metadata.
The data is replicated or erasure-coded across nodes for durability.
The store acknowledges the write, and the object becomes retrievable by key from anywhere with the right credentials.
What are the benefits of object storage?
Object storage offers near-limitless scale, low cost, built-in durability, and a simple access model, which is why it is common for cloud data lakes, backups, archives, and telemetry retention. Here's what each benefit means in practice.
Scalability without restructuring
Object storage scales horizontally. As volumes grow, the provider adds nodes and the namespace just keeps absorbing objects. You never hit a volume size limit or reformat a file system. When a new security tool suddenly produces a terabyte a day you didn't plan for, the bucket does not care.
Cost-effective retention
Most cloud object storage runs on a pay-as-you-go model with tiered pricing for hot, infrequent-access, and archive classes. Compare that to a log analytics platform, where indexing and replication can multiply the footprint of raw data many times over, as explained in Cribl's breakdown of why log systems require so much infrastructure. Keeping years of full-fidelity telemetry becomes a rounding error instead of a budget fight.
Durability and availability by default
Major providers design object storage for eleven nines of durability by distributing data across zones. You get resiliency without building a replication strategy yourself.
Simplicity and flexibility
One API, one flat namespace, and metadata you control. Object storage does not force a schema on your data, so you can land raw logs today and decide how to parse them later. That schema-on-read flexibility is a key reason object storage underpins modern data lakes and security data lakes.
Object vs. block vs. file storage: what's the difference?
Object, block, and file storage differ in how they organize data, how you access it, and what workloads they suit. Block storage is for performance, file storage is for shared hierarchical access, and object storage is for scale and cost. Most organizations use all three.
The practical takeaway: keep your databases on block storage, keep shared documents on file storage, and send the flood of logs, metrics, and traces to object storage where it is cheap to keep and easy to grow.
What are the most common object storage challenges?
Object storage is good at holding data and weaker at helping you find, govern, and reuse it. Three challenges come up again and again.
Consistency and latency
Because objects are distributed across many nodes, some systems have historically offered eventual rather than immediate consistency, and retrieval latency is higher than local disk. That is fine for archives and batch analytics. It is less fine for workloads that expect sub-millisecond reads, which is why object storage rarely replaces block storage for databases.
Security and access control at scale
A single bucket can hold billions of objects that mix sensitive and harmless data. Getting authentication, authorization, and encryption right for every object, across multiple accounts and regions, is difficult. Misconfigured buckets remain one of the most common causes of cloud data exposure, and compliance regimes like GDPR, HIPAA, and PCI expect you to know exactly what sensitive fields landed where.
Data management and searchability
Object storage has no query engine. To find the events from one host during a three-hour window last March, you either download and scan enormous amounts of data or you rely on smart partitioning and metadata planned in advance. Without that plan, retrieval becomes slow, expensive, and dependent on tickets to another team.
Why object storage matters for telemetry
Object storage lets you separate your system of retention from your system of analysis. SIEM and observability tools are good at fast search over recent, high-value data. They are costly as a place to keep everything forever.
The pattern that works is data tiering: land a full-fidelity copy of every event in low-cost object storage, send a reduced, enriched subset to your analytics tools, and pull data back only when an investigation, audit, or AI project demands it. Security incidents are routinely discovered long after hot retention windows expire. With everything in object storage, you still have the full story.
One real-world example: a multinational enterprise that has run continuous operations since the 1890s stores petabytes of telemetry in Amazon S3 as its first destination, then sends a curated slice to Splunk. During one investigation, its team used path-based partitioning to target the right data, replayed 1.335 PB from S3 through Cribl Stream, and delivered just 3.69 GB of relevant events to Splunk, all within a single working day. The same approach applies in multi-cloud environments, where data can stay in the cloud where it was generated, avoiding egress charges, while you still search across all of it from one place.
How Cribl can help with object storage
Cribl provides tools to make object storage an active part of your data strategy, rather than a cold archive. Cribl Stream collects telemetry from any source, reduces and enriches it in flight, and writes a full-fidelity copy to Amazon S3, Azure Blob Storage, Google Cloud Storage, any S3-compatible store, or Cribl Lake. You define the partitioning scheme by time, host, source type, or index, so the data lands already organized for fast retrieval. Meanwhile, only the events your SIEM actually needs go to your SIEM.
When you need that data back, Stream's Replay feature addresses the searchability problem. Filter by time range and path metadata to skip objects that can't match, collect only the relevant slice, then run it through your pipelines and into any destination in the format that tool expects. Cribl's guide to replaying data from object storage for long-term investigations walks through the workflow step by step. Often you will not need to replay at all: Cribl Search runs federated queries across S3, Blob, and GCS without moving or rehydrating anything, so you can analyze first and forward only what matters.
If you prefer not to manage buckets, policies, and permissions yourself, Cribl Lake provides a telemetry-optimized data lake with unified retention, access controls, and native integration with Stream and Search. If you need to keep data in your own accounts for compliance, Cribl Lake's Bring Your Own Storage for Amazon S3 keeps data in your buckets, under your keys, while Cribl handles partitioning, retention, and search on top. Either approach keeps your data in open formats with no lock-in and no data loss.
This gives options for storage location, retention, and search: store everything affordably, keep only the right data hot, and retrieve the full story when needed. To try it, sign up for a free Cribl.Cloud account and process up to 1TB a day, no license required, or schedule a demo tailored to your object storage architecture.
Object Storage FAQs
What is object storage in simple terms?
Object storage is a way of saving data as individual objects rather than as files in folders or blocks on a disk. Each object contains the data, descriptive metadata, and a unique identifier, and is stored in a flat address space accessed through an API. Examples include Amazon S3, Azure Blob Storage, and Google Cloud Storage.
What is the difference between object storage and file storage?
File storage organizes data in a hierarchical tree of directories and files, which is intuitive for humans but limits scale. Object storage uses a flat namespace and rich metadata, so it can grow to petabytes and beyond without restructuring. File storage suits shared drives and traditional applications; object storage suits archives, backups, data lakes, and telemetry retention.
Is object storage cheaper than storing data in a SIEM or log analytics tool?
Yes. Log analytics tools index data for fast search, replicate it for resiliency, and run on fast block storage, so a single event can consume many times its raw size. Compressed raw data in object storage can cost a small fraction of that.
What are the main drawbacks of object storage?
Common challenges include consistency and latency in distributed systems, granular security and access control across huge volumes of objects, and searchability. Object storage is designed for durability and scale, not for fast interactive queries, so you need a way to find, filter, and retrieve specific data without downloading entire buckets.
How does Cribl work with object storage?
Cribl Stream writes full-fidelity telemetry to any S3-compatible store, Azure Blob, Google Cloud Storage, or Cribl Lake, and uses partitioning schemes to make later retrieval fast. Cribl Search runs federated queries directly against that data without moving it. When you need data back in a SIEM or analytics tool, Stream's Replay feature collects just the relevant slice and routes it through your pipelines in the right format.
Can I keep my data in my own buckets and still use Cribl Lake?
Yes. Cribl Lake supports bring your own storage for Amazon S3, so your data stays in your buckets under your policies and encryption keys, while Cribl Lake adds centralized dataset management, retention controls, search, and replay on top.
