Services /// Cost /// Premium
Storage costs for increasingly large datasets and parametrized models are significantly lower in distributed systems.
Filecoin
$0.018
-
Amazon S3 Standard
$0.276
1433%
Amazon S3 Glacier Deep Archive
$0.012
(33%)
Dropbox Business Standard
$0.030
67%
Dropbox Individual Professional
$0.066
267%
Google One (100GB)
$0.194
978%
Google One (1TB)
$0.062
244%
Microsoft OneDrive Personal (6TB)
$0.070
289%
Sia
$0.037
107%
Sia (with 1x Upload & Download)
$0.059
225%
Storj
$0.120
567%
Storj (with 1x Upload & Download)
$0.660
3567%
Decentralized compute platforms like Render, io.net are also displaying that it is more cost effective to access compute power of idle GPUs than paying large sums to cloud based centralized service providers.
H100
$4.28
$4.0
6.5%
A100 (80GB)
$2.14
$0.89
58.4%
A100 (40GB)
$1.81
$0.76
58.0%
A40
$1.33
$0.75
43.6%
RTX A6000
$1.23
$0.75
39.0%
RTX 8000
$0.3
$0.66
-120%
Decentralized AI networks provide significant cost advantages by leveraging economies of scale in energy consumption. GPUs located in regions with lower electricity costs can handle larger computational loads efficiently, making operations more cost-effective. Furthermore, existing idle infrastructure—such as personal computers and underutilized servers—can be integrated into the network, drastically reducing setup and maintenance expenses.
The use of peer-to-peer (P2P) layers, inspired by blockchain and file-sharing networks, allows the network to allocate tasks efficiently across available GPUs, maximizing resource utilization while minimizing waste. This ensures that computational power is optimized without requiring centralized, expensive data centers.
In addition to cost savings, a distributed architecture mitigates the risks associated with relying on traditional Web2 service providers. Centralized platforms often impose pricing inelasticity and strict compliance measures that can increase costs and limit flexibility. By contrast, a distributed system allows redundant nodes to step in automatically if others experience downtime, ensuring continuous operation and higher reliability.
We believe that the combination of economic efficiency, operational resilience, and user-driven incentives creates a sustainable ecosystem. Users are likely to pay equivalent or even premium amounts to access high-quality AI models free from moderation and censorship—starting with LLMs—making the distributed model not only technically effective but also economically viable.
// QUANTWARE //Last updated