Ocean Network Launches ‘Inference’ for Persistent, HTTP-Accessible AI Deployments on Dedicated GPUs

Ocean Network has launched Inference, a service letting users deploy AI models and applications as persistent, always-on services reachable over ordinary HTTP requests, running on dedicated GPU hardware for as long as a session stays booked. Ocean’s team is calling it the largest capability expansion the decentralized compute platform has shipped to date.

The pitch is aimed squarely at three frustrations developers routinely hit when renting AI compute: billing that’s unpredictable until the invoice arrives, little visibility into what hardware is actually running a given model, and no easy way to keep a model live and reachable as a standing service rather than a one-off job. Inference shows an estimated cost before a session even begins and bills by GPU time rather than by tokens consumed, a structural difference from how most AI API providers charge. Pricing starts at $2.16 an hour for access to an Nvidia H200 GPU. In one internal benchmark Ocean cited, generating a 30-second video took about 20 minutes of compute time at a cost of roughly $0.72.

Payments run on-chain through an escrow contract on the Base network, meaning funds are held against usage rather than pre-paid into an opaque balance, which is consistent with Ocean’s broader positioning around transparent, pay-per-use decentralized compute rather than traditional cloud billing.

Note: the embedded post above is Ocean’s own announcement of H200 GPU access at the $2.16/hour price point referenced in this story; it predates the specific “Inference” product branding but confirms the pricing and hardware details directly.

Whether Inference gains real traction will come down to something simple: can it reliably keep a model online and responsive for paying developers without the downtime or opacity that decentralized compute networks have historically struggled to shake. A persistent, HTTP-reachable AI service is a commodity product in centralized cloud computing already; Ocean’s bet is that transparent, escrow-backed, pay-per-use pricing on distributed GPU hardware is different enough to win developers away from incumbents.

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This article is for informational purposes only and is not financial advice. Pricing and performance figures reflect the provider’s own published benchmarks as of publication and may not represent all real-world use cases.