Generative AI video is exploding. Platforms can turn prompts into polished clips, and models churn through massive training sets of images and footage. Behind the magic, though, is the unavoidable reality of storing and moving petabytes of data.
Training runs require archiving colossal datasets, then pulling them back in full when it’s time to retrain. Once models go live, the output itself becomes another major workload to manage, whether that’s endless libraries of user-generated videos or fast-cycling streams of ephemeral content. These challenges are part of life for every GenAI company, but the costs of handling them vary widely depending on the provider.Those cloud storage costs can spiral quickly out of control. The big cloud providers lure teams in with low headline rates, but the fine print tells a different story. Pricing depends on which storage tier you pick, how often you move data between regions, and how many API requests your pipeline makes. Founders end up building workflows around cloud quirks instead of what’s fastest and simplest for their teams.
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Hidden cost #1: Storage tiers and complexity
AI video data doesn’t behave neatly. Training sets might sit untouched for long stretches before being needed again all at once. User-facing content might accumulate forever, or spike and crash depending on the latest trend. For lean engineering teams, predicting these swings is nearly impossible.
On major cloud providers, the stakes are high. Choose a hot tier and you’ll overpay when data goes cold. Pick an archive tier and you’ll face delays and penalties when you suddenly need that dataset tomorrow. Constantly shifting petabytes between tiers adds both operational overhead and surprise costs.
The numbers tell the story: a 5PB archive costs about $120K a month on AWS S3 Standard for storage alone, before any egress charges. The same capacity runs closer to $30K on Backblaze B2 Cloud Storage—a $90K delta that could fund another GPU cluster or extend a startup’s runway.
Backblaze B2 comes in at around one-fifth the cost of S3, with no tiering games to manage. And when workloads demand maximum throughput, B2 Overdrive scales while delivering a stronger price-to-performance ratio than others offer. That means less time modeling cost scenarios and more time iterating on product and model design.
Hidden cost #2: Egress fees
AI development thrives on iteration. Training and retraining cycles shuffle enormous datasets across clusters, often more than once a month. Each transfer can rival the cost of storage itself. And the faster a team wants to move, the more those bills stack up.
The big cloud providers introduce friction at every step. They charge not only when data exits their cloud but also when it crosses between their own regions. At petabyte scale, those tolls can reach five or even six figures in a single month, forcing founders into an impossible tradeoff: experiment less or drain the budget.
Consider that moving just 1PB once per month on AWS in the US East (N. Virginia) region racks up around $53.8K. Double that transfer frequency and you’re staring at over $100K in egress fees. That’s budget better spent on hiring, acquiring customers, or building better products.
Backblaze removes this bottleneck. Backblaze B2 already includes free egress to leading GPU and CDN partners. For companies operating at AI scale, B2 Overdrive goes further with unlimited free egress to any destination. That means models can be trained, tuned, and distributed globally without a single surprise charge standing in the way of progress.
Mirage, an AI video platform, experienced this firsthand. By eliminating egress costs, they cut storage-related expenses by up to 95% compared to their previous provider—freeing resources to reinvest in growth and product innovation.
Hidden cost #3: API requests and transaction fees
Not every AI video workflow interacts with storage the same way. Some stream large video files in big chunks, keeping the number of calls manageable. Others slice data into millions or billions of tiny objects—frames, embeddings, or metadata—and rely heavily on listing and indexing operations. In those cases, what looks like spare change per request quickly compounds into thousands of dollars in charges every month.
Major cloud storage providers are relentless here. Every PUT, GET, LIST, or HEAD operation comes with a fee, no matter how small. At scale, those fractions of a cent add up fast, leaving engineers designing around billing quirks instead of choosing the cleanest solution for their pipelines.
Picture a pipeline that generates one billion writes and two billion reads in a single month. On AWS, the tab for those transactions alone would run close to $5.8K. On Backblaze B2, writes are free and reads cost just $0.004 per 10,000 requests, bringing the same workload down to about $800. And the first 2,500 Class B and Class C transactions each day are free, further shrinking the bill. On B2 Overdrive, all API calls are included at no additional cost.
Whether your architecture leans toward billions of tiny objects or more efficient streaming, Backblaze keeps request charges predictable and manageable. That makes API calls something your team doesn’t need to obsess over, which is exactly how it should be.
Bringing it together: Simple, predictable economics
Taken together, these hidden costs show why storing AI video on “the big three” often feels like playing a rigged game. The pricing looks straightforward until the bills arrive, padded with charges for tiers, transfers, and transactions. Each one eats away at budget and slows the pace of innovation.
Backblaze offers a different path. By stripping out the fine print and focusing on price-to-performance, it makes storage a stable foundation instead of a moving target. Mirage proves what that means in practice: eliminating egress fees drove huge savings and freed resources to reinvest in their product.
For founders, that kind of predictability turns storage from a frustrating line item into the fuel for faster iteration, bolder experimentation, and sustainable growth.
