AI training doesn't read your dataset once. It reads the same data repeatedly across epochs, checkpoints, and distributed workers. Every stage of training pulls the same data across the network. That $0.09 per gigabyte fee isn't a one-time charge. It multiplies across everything you do.
And that's the egress fee trap.
Why $0.09/GB Looks Harmless—and Isn't
Major cloud providers charge $0.09 to $0.12 per gigabyte for data leaving their storage after your first 100 GB free.
Nine cents per gig sounds manageable. Your finance team thinks they can budget for this.
They can't—because those fees scale with movement, not with the size of the dataset.
Volume discounts look helpful. But they assume you're moving data once. Download a file, done.
AI workloads don't behave that way.
How AI Turns Pennies per Gigabyte into a Six-Figure Line Item
Consider documented training runs from recent years. GPT-3 required between 570 gigabytes and 45 terabytes of training data. GPT-4 used an estimated 13 to over 100 terabytes. Today's frontier models such as GPT-5.1, Claude Sonnet 4.5, and Gemini 3 train on datasets far larger than either of those ranges.
Those dataset sizes sound big, but they're small compared to total data movement.
Most large models train for one to three epochs: complete passes through the dataset. Distributed training splits data across multiple GPU nodes. Checkpoint storage saves model state periodically. Data augmentation creates variations on the fly. Inference pipelines pull model weights for every prediction.
Total data moved during AI training is often 10 to 100 times the raw dataset size.
A typical enterprise AI project uses 10 gigabytes to 10 terabytes of training data. With the 10x to 100x multiplier, that becomes 30 gigabytes to 30 terabytes of actual data movement. At $0.09 per gigabyte, the egress bill for a single model reaches $2,700. Larger projects hit tens of thousands per training run.
You're not training once; you're iterating—testing hyperparameters, retraining on new data, running experiments side by side.
A team moving 50 terabytes a month pays roughly $4,500 in egress alone, a recurring cost that easily compounds to six figures annually. You are not paying for storage or compute at that point; it is just the toll for moving data.
That's a major reason 95% of IT leaders report unexpected cloud storage costs.
Why Egress Fees Exist in the First Place
While infrastructure costs are real, the disparity between free ingress and expensive egress suggests a pricing model focused on retention rather than pure cost recovery.
Moving data in requires bandwidth and storage writes. Moving data out requires storage reads and outbound bandwidth. Similar operations. If anything, ingress should cost more: writes are more expensive than reads.
Instead, ingress is free and egress costs $0.09 or more.
Once your data is in, you're trapped. The industry calls this data gravity. Fifty-five percent of IT leaders say egress costs are the biggest barrier to switching providers—for most teams, the blocker isn't technology, it's cost.
Think Hotel California: checking in is effortless, leaving is what costs you.
Flat-Rate Storage That Kills the Egress Meter
The egress trap exists because of how storage is priced, not because it's technically necessary.
Akave Cloud charges $14.99 per terabyte per month. Zero egress fees. Zero API fees. Flat rate, unlimited retrieval.
This isn't a temporary discount on egress; it's a flat-rate pricing model by design.
Akave is engineered for high-throughput object workloads so your GPUs stay fed without paying an egress tax.
Zero egress enables AI workloads that would be cost-prohibitive elsewhere:
Train without the tax. Run as many epochs as you need. Iterate faster because iteration doesn't cost extra.
Checkpoint freely. Save model state as often as you want. Disaster recovery doesn't have a per-gigabyte cost.
Multi-region without penalty. Distribute training across regions. Geographic distribution becomes an architecture decision, not a cost decision.
Here's the math on 100 terabytes with 2x monthly data movement:
Annual savings: $225,612, driven entirely by eliminating egress fees.
Akave Cloud is S3-compatible. Your existing code works with one endpoint change. Your Terraform stays the same. The only thing that changes is the bill.
How to See (and Stop) Your Egress Bleed
Stop bleeding budget on data movement. Start with visibility: pull your current cloud bill and calculate what you're actually paying for egress.
Drop your latest bill into the Akave Cloud calculator to see flat-rate pricing for your storage and movement patterns. No email required.
Start with your AI workloads—the ones with highest data movement. See the savings. Decide what comes next.
Your data and your budget should move on your terms.
Sources
- Technologent, 2024 — 62% of organizations exceeded cloud storage budgets
- Backblaze / Campus Technology, 2024–2025 — 95% of IT leaders report unexpected cloud storage costs
- Backblaze survey, 2024 — 55% of IT leaders cite egress as biggest barrier to switching providers
- Epoch.ai, Stanford HAI AI Index 2025 — AI training data volume estimates (GPT-3, GPT-4)
- AWS, Azure, Google Cloud official pricing pages, 2024–2025 — Egress fee tiers
- Hokstad Consulting, 2024 — 50TB/month egress cost example (~$4,500 USD)