AI is only as powerful as the data that fuels it. Training large models, fine-tuning small ones, or running inference at scale requires massive volumes of unstructured data—images, video, audio, text, sensor logs, and more. Traditional storage methods often fall short when workloads demand scalability, low latency, and cost efficiency.
This is where object storage steps in. But how exactly does object storage support AI workloads? And how can HorizonIQ help businesses deploy storage that is cost-predictable, globally available, and performance-optimized for AI?
Let’s dive in.
What is object storage and why is it important for AI?
Object storage organizes data as objects (with metadata and unique identifiers) instead of blocks or files. This structure makes it infinitely scalable and ideal for unstructured data—the lifeblood of modern AI.
For AI teams, this means:
- Storing petabytes of data in a flat namespace
- Efficient parallel access during training and inference
- Easier metadata tagging to support dataset management
- Seamless integration with APIs (most commonly Amazon’s S3 standard)
Learn more about the basics of HorizonIQ’s object storage service.
How does object storage differ from block or file storage in AI workloads?
- Block storage is optimized for low-latency, high-IOPS workloads such as databases or VMs.
- File storage works well for shared file systems, but doesn’t scale as easily.
- Object storage is built for massive scale and throughput, which makes it the best fit for AI training data, model checkpoints, and logs.
AI workloads focus on moving huge volumes of unstructured data efficiently, rather than handling small transactions.
Looking for a deeper breakdown? Read our storage guide to explore use cases, performance trade-offs, and best-fit scenarios.
Why do AI workloads need scalable object storage?
Training a single large language model (LLM) can involve hundreds of terabytes of text, images, or video. Even smaller, domain-specific models depend on massive, ever-growing datasets.
Object storage allows:
- Elastic scaling without performance drops
- Parallel access from distributed compute nodes
- Cost efficiency compared to traditional enterprise SAN/NAS
According to Goldman Sachs, AI workloads could account for around 28% of global data center capacity by 2027, with hyperscale operators expected to control about 70% of capacity by 2030 as demand and power usage surge due to AI.
How does HorizonIQ object storage support AI?
HorizonIQ was built with AI and high-performance computing in mind. Our object storage includes:
- Always-hot storage with no egress or operations fees
- 2,000 requests/sec per bucket (measured on Ceph clusters)
- 11 nines of durability (99.999999999%)
- 99.9% availability SLA
This makes it ideal for high-throughput pipelines like image preprocessing, model training, or inference at scale.
Explore our object storage features here.
Looking to migrate without overlap costs?
Migration shouldn’t drain your budget. With HorizonIQ’s 2 Months Free, you can move workloads, skip the overlap bills, and gain extra time to switch providers without double paying.
Get 2 Months FreeHow does HorizonIQ help control AI infrastructure costs?
Cloud costs are one of the biggest pain points for AI teams. 82% of enterprises cite managing cloud spending as their top challenge.
HorizonIQ solves this with:
- Flat, predictable pricing (no egress or API call fees)
- Up to 70% cost savings vs. hyperscalers
- 94% savings compared to VMware licensing for private environments
- Full S3 compatibility, so savings don’t come at the expense of rebuilding workflows or retraining teams
This means AI teams can run massive training or inference jobs without the “bill shock” that comes with variable cloud charges.
How does HorizonIQ compare to AWS, Azure, and Google Cloud for AI object storage?
Let’s look at a real-world AI training scenario:
- 50 TB hot storage
- 200M GET requests + 2M PUT requests
- Variant 1: All compute + storage in-cloud
- Variant 2: Same workload, plus 20 TB egress
| Provider | In-Cloud Cost | With 20 TB Egress | Notes |
| HorizonIQ Advanced Multi-tenant Object Storage | $1050 | $1050 | Flat, no egress or API fees |
| AWS S3 Standard | $1267 | $3037 | $80 per 200M GETs, $10 per 2M PUTs, egress charged |
| Azure Hot | $1032 | $2772 | Similar request and egress fees to AWS |
| Google Cloud Standard | $1936 | $3846 | Highest GET costs and egress charges |
Key takeaway: With HorizonIQ, costs are transparent and predictable. Whether you keep data in-cloud or move it out for distributed AI training, pricing doesn’t change. Hyperscalers, on the other hand, pile on fees for API calls and egress—costs that can triple your bill in high-usage scenarios.
Is HorizonIQ object storage secure and compliant for regulated AI use cases?
Yes. Many AI applications process sensitive data because industries like healthcare, finance, or government workloads require strict compliance.
HorizonIQ ensures:
- Dedicated, single-tenant architecture (no noisy neighbors)
- SOC 2, ISO 27001, and PCI DSS compliance
- Data sovereignty with 9 global regions to meet residency requirements
This makes it possible to train and deploy AI models while meeting regulatory demands.
How does HorizonIQ integrate object storage with GPUs and private cloud?
Storage is only one side of the AI equation. AI pipelines also need GPU acceleration and low-latency interconnects.
HorizonIQ provides:
- GPU-ready infrastructure colocated with object storage
- Private cloud and bare metal options for hybrid deployments
- Compass, a management platform to monitor, reboot, and control environments in just a few clicks
This creates an end-to-end AI stack: compute + storage + networking, optimized for both performance and predictability.
What are the real-world AI use cases for object storage?
Some common workloads HorizonIQ supports include:
- Machine learning training (vision, NLP, recommendation engines)
- Data lakes for generative AI pipelines
- Media & entertainment AI (video upscaling, captioning, recommendation)
- Scientific research (genomics, physics simulations, R&D)
- AdTech (real-time audience modeling)
Our customers include companies that are pushing boundaries with AI, such as Unity, ActiveCampaign, and IREX.
How can businesses get started with HorizonIQ object storage for AI?
The best way is through a proof of concept (POC) tailored to your workload. HorizonIQ offers:
- Custom POCs to benchmark AI training/inference
- Migration support to move datasets off hyperscalers
- Long-term discounts for predictable budgeting
Start exploring HorizonIQ object storage today.
Final Takeaway
AI runs on data, and object storage is the foundation for scaling it. Businesses need infrastructure that balances performance, cost predictability, compliance, and global reach. HorizonIQ delivers all four with up to 70% savings, GPU-ready infrastructure, and dedicated support.
HorizonIQ empowers your AI journey with storage built for innovation, without surprises.
