Beyond the Ratio: Smarter Compression for Object Data

AI growth puts new pressure on object storage

AI runs on data, and the volume keeps climbing. Training corpora, images, video, documents, checkpoints, logs, and machine-generated content are all growing quickly. For infrastructure and AI operations teams, the challenge isn't only finding room for more. It's keeping the right data accessible for analytics, training, retrieval, and inference without letting storage costs and operational complexity grow unchecked.

That's why storage efficiency has become part of AI readiness. When teams can store more usable data on the same physical capacity, they have more options for what they keep close to their AI pipelines. Getting there starts with understanding what efficiency figures actually measure.

Read every data-reduction ratio in context

Dell offers data-reduction technologies across its portfolio, but the ratios they produce aren't interchangeable. Compression, deduplication, and combined data reduction work on different kinds of data. Backup workloads often contain extensive redundancy, while primary and object storage workloads can have very different compressibility profiles. The large ratios associated with PowerProtect Data Domain describe a purpose-built backup environment where deduplication drives the result. PowerStore and PowerMax use their own primary storage methods and assumptions.

Each of these figures is valuable in its intended context. A more useful question than "Which product has the biggest ratio?" is "Which efficiency technology is designed for this data, this access pattern, and this operating model?"

ObjectScale offers up to 9:1 compression of object data¹. This figure reflects compression, not backup deduplication, and actual results vary with file type, data content, hardware configuration, compression policy and workload behavior. It's a helpful proof point. The larger opportunity is applying compression intelligently across large, diverse object environments and making the results visible to the people who manage capacity and cost.

Match compression to each object workload

No single compression setting fits every AI workload. ObjectScale provides five configurable modes: ZSTD, LZ4, Deflate, Snappy, and no compression. Administrators can balance capacity savings, speed and CPU utilization instead of applying one policy everywhere.

Compression also isn't all-or-nothing. A configurable threshold decides whether expected savings justify the processing. With a 50% threshold, for example, ObjectScale compresses data only when it expects at least 50% savings and otherwise stores the data as-is. That helps teams avoid spending CPU cycles for little return.

This matters because some object types, such as JPEG, MP4 and ZIP files, are already compressed. Text, logs, documents and structured data may still offer meaningful savings. Because ObjectScale supports compression globally or at the bucket level, teams can treat each data set according to its actual characteristics. A practical AI environment might begin with these starting points, then validate them against representative data:

• Document and log lakes: ZSTD, to favor capacity efficiency for retained data.

• Active training and feature processing: LZ4 or Deflate, to favor throughput and lower processing overhead.

• High-throughput ingestion: Snappy or LZ4, to favor speed and responsive ingest.

• Already-compressed content: No compression or a higher threshold, to avoid processing when added savings are limited.

These aren't universal prescriptions. They're a framework for testing. The right policy delivers meaningful savings while respecting each workload's performance and operating requirements.

See the savings and build on them

A ratio is hard to manage without visibility. ObjectScale provides insight into compression savings, compression ratio, and logical versus physical capacity across clusters, namespaces and buckets. Storage administrators can see which buckets deliver meaningful savings, which data types gain little, and whether a policy creates an unacceptable performance or CPU impact. With those answers, teams can tune policies as workloads change and plan capacity from observed usage rather than assumptions.

Compression is also one part of a broader efficiency strategy:

• Erasure coding can reduce protection overhead compared with maintaining multiple full copies, and selectable schemes help balance durability, capacity and workload requirements.
• Space reclamation recovers capacity after data is deleted or updated and can align more aggressive activity with lower-usage periods.
• Capacity-oriented media can support retained data, while all-flash resources stay reserved for performance-sensitive applications.
• S3-native access and support for modern data formats help applications use object data directly instead of creating avoidable copies.

When evaluating object storage efficiency, look at the full picture: compression savings by bucket, logical versus physical capacity, CPU and performance impact, protection overhead, capacity reclaimed after deletion, and the copies your AI pipeline requires. That broader view produces a more credible business case and fairer comparisons across technologies.

Keep the Right Data Ready for AI

Within the Dell AI Data Platform, ObjectScale provides an S3-native foundation for unstructured data, keeping it accessible to AI and analytics as environments scale. An Omdia economic validation found up to 80% lower total cost of ownership than leading public clouds in active-storage scenarios². Because the comparison is scenario-specific, the broader lesson is that storage economics depend on the full operating model and not compression alone.

The strongest efficiency strategy isn't the one with the biggest isolated ratio. It's the one that understands the data, adapts to each workload, makes savings measurable, and keeps delivering value as the environment grows. Smart compression in ObjectScale helps organizations keep more object data accessible for AI and analytics while managing capacity, performance and cost with greater precision.

To explore the details, read the white paper, Dell ObjectScale: Enhanced Storage Efficiency with Compression.

¹ Dell Technologies Solutions White Paper, "Dell ObjectScale: Enhanced Storage Efficiency with Compression," July 2026.

² Omdia Economic Validation, "Analyzing the Economic Benefits of Dell ObjectScale: Economic Benefit Analysis of On-Premises Object Storage vs. Public Cloud,” July 2026. Cost savings based on Omdia comparison of ObjectScale to leading public clouds in active storage scenarios. Actual results will vary.

FREQUENTLY ASKED QUESTIONS

What does the ObjectScale 9:1 compression figure mean?

It describes up to 9:1 compression of object data, based on Dell testing. It reflects compression, not backup deduplication. Actual results vary with file type, data content, hardware configuration, compression policy and workload behavior.

Which compression modes does ObjectScale support?

ObjectScale provides five configurable modes: ZSTD, LZ4, Deflate, Snappy, and no compression. These options help administrators balance capacity savings, speed and CPU utilization.

How does the compression threshold work?

A configurable threshold determines whether expected savings justify the processing. With a 50% threshold, for example, ObjectScale compresses data only when it expects at least 50% savings. Otherwise, it stores the data as-is.

Can teams apply different compression policies to different workloads?

Yes. ObjectScale supports compression globally or at the bucket level, so teams can treat document collections, logs, media, backups, checkpoints and structured data according to their characteristics.

How can teams tell whether compression is working?

ObjectScale provides insight into compression savings, compression ratio and logical versus physical capacity across clusters, namespaces and buckets. Teams can use this visibility to tune policies and plan capacity from observed usage.