Why Cloud Storage Is the Backbone of AI Workloads
Every AI workload starts and ends with data. Training datasets, model checkpoints, inference logs, embedding vectors, and fine-tuning corpora all live in storage. The quality of your storage infrastructure directly affects the speed, cost, and reliability of your AI pipeline.
Yet storage is often treated as an afterthought — a cloud bucket provisioned in 30 seconds with default settings. For AI teams, this is a mistake. Storage architecture decisions made at the start of a project compound as data volumes grow, and the cost of fixing them later is measured in migration time and downtime.
What Are the Storage Requirements for AI Workloads?
| Workload | Data Volume | Access Pattern | Throughput Needed | IOPS | Latency Sensitivity |
|---|---|---|---|---|---|
| Training dataset | 10 GB - 10 TB | Sequential read, repeated | 1-5 GB/s | Low | Moderate |
| Model checkpoints | 1-50 GB per checkpoint | Sequential write, periodic | 500 MB/s - 2 GB/s | Low | Low |
| Inference model weights | 1-30 GB | Read once at startup | 2-5 GB/s (startup) | Low | High (cold start) |
| Embedding vectors | 1-100 GB | Random read, high frequency | 100-500 MB/s | High (10K-100K) | Very high |
| Data preprocessing | Varies | Random read/write, many files | 500 MB/s - 2 GB/s | High (100K+) | Moderate |
| Experiment logs and metrics | 1-100 GB | Append-only, low frequency | Low | Low | Low |
The diversity of access patterns means no single storage configuration works for all AI workloads. Training datasets need high sequential throughput. Embedding vector databases need high random IOPS. Checkpoints need reliable write bandwidth. BHK Cloud's storage architecture handles all these patterns because the volumes are directly attached to the GPU infrastructure, eliminating the network bottleneck that plagues cloud object storage.
How Do Cloud Storage Options Compare for AI Workloads?
| Storage Type | Cost/TB | Throughput | Latency | Best For | Pitfalls |
|---|---|---|---|---|---|
| BHK Cloud Volumes | $2.49 | 1-5 GB/s | 1-5 ms | All AI workloads | Requires BHK Cloud GPU instances |
| AWS S3 Standard | $23.00 | Varies (limited by egress) | 10-50 ms | Archival, cold data | Egress fees, high latency for random reads |
| AWS EBS (gp3) | $80.00 | 1 GB/s (per volume) | 1-3 ms | Training scratch space | Expensive, attached to single instance |
| GCP Cloud Storage | $20.00 | Varies | 10-50 ms | Archival, cold data | Egress fees, complex tiering |
| Azure Blob | $20.80 | Varies | 10-50 ms | Archival, cold data | Egress fees, hot/cool tier complexity |
| Local NVMe (on-premise) | $200-500 (one-time) | 3-7 GB/s | 0.1-0.5 ms | High-performance training | No durability, no sharing, capacity limits |
How to Design a Storage Architecture for AI Pipelines
A well-designed AI storage architecture separates data by access pattern and lifecycle:
- Hot tier (active datasets). Data currently being used for training or preprocessing. Needs high throughput and low latency. BHK Cloud volumes: $2.49/TB/month.
- Warm tier (recent checkpoints). Checkpoints from the last 2-4 weeks. Accessed occasionally for model comparison or resuming training. BHK Cloud volumes (same tier, same cost).
- Cold tier (archived experiments). Datasets and checkpoints from completed projects. Accessed rarely, but too valuable to delete. Archive to low-cost object storage or compress and store on BHK Cloud volumes — at $2.49/TB, it is often cheaper to keep everything online than to build a separate archival pipeline.
With BHK Cloud's pricing, the cost difference between hot and cold storage is zero. At $2.49/TB, there is no financial incentive to archive data. Keep everything online, indexed, and accessible. This dramatically simplifies the storage architecture — no tiering policies, no retrieval delays, no lifecycle management scripts.
What Are Common Storage Mistakes in AI Projects?
Five storage mistakes that cost AI teams time and money:
- Using object storage for training data. Object storage (S3, GCS, Blob) is designed for infrequent access. Reading a 500 GB dataset from S3 for every training epoch adds latency and egress costs. Use directly attached volumes for active datasets.
- Not versioning datasets. "dataset_v2_final_real_final.csv" is not version control. Use tools like DVC or simply date-stamped directory names. Storage is cheap; reproducibility is priceless.
- Storing checkpoints on ephemeral instance storage. If your instance terminates, local data is gone. Always write checkpoints to persistent volumes. BHK Cloud volumes survive instance termination.
- Ignoring I/O bottlenecks. A GPU running at 50% utilization because it is waiting for data is a GPU running at 100% cost. Monitor storage throughput alongside GPU utilization.
- Paying for egress. If your storage provider charges for data access, your AI pipeline has a tax on every training epoch. BHK Cloud charges zero egress — data flows freely between storage and GPU instances.
Frequently Asked Questions
How much storage do I need for a typical AI project?
A small fine-tuning project: 50-200 GB (dataset + checkpoints + model weights). A medium-scale training project: 1-5 TB. A large-scale multi-model research project: 10-50 TB. BHK Cloud's storage pricing makes it practical to keep all of it online: 50 TB costs $124.50/month.
Can I share storage volumes between multiple GPU instances?
Yes. BHK Cloud volumes can be attached to multiple GPU instances simultaneously, enabling shared access to datasets and checkpoints across a team. This is particularly useful for multi-GPU training where all instances need access to the same dataset.
How do I back up my AI datasets and models?
BHK Cloud volumes include built-in redundancy. For off-site backups, use rclone or s3cmd to sync to an external object storage provider. Because BHK Cloud does not charge egress, backing up 10 TB to an external provider costs only the target storage fees — no data transfer charges from BHK Cloud.
What file systems are supported on BHK Cloud volumes?
BHK Cloud volumes appear as standard block devices. You can format them with ext4, XFS, ZFS, or any Linux file system. For shared access across multiple instances, NFS or a distributed file system like GlusterFS can be configured on top of BHK Cloud volumes.