Ocean Orchestrator
Run AI jobs from your IDE with a one-click workflow
295 followers
Run AI jobs from your IDE with a one-click workflow
295 followers
Access GPUs worldwide directly from your IDE. Ocean Orchestrator lets you run AI training and inference jobs while paying only for the compute you use. Jobs run on GPUs like NVIDIA H200s across the Ocean Network Escrow-based payments protect both users (data scientists, developers) and node operators, releasing funds only after successful execution, bringing reliable, decentralized GPU compute to real workloads with transparent pricing, global availability, and verifiable job execution at scale
Free Options
Launch Team / Built With
Ocean Orchestrator
@keshav_namdev Hey, congrats on the launch! Just a small question; how does the escrow system handle job failures or flaky nodes to keep workflows reliable?
Ocean Orchestrator
@swati_paliwal @michaelp_ai In Ocean, you don’t pay upfront to the node, you lock funds in escrow. The node only gets paid if the job completes successfully.
If a node fails or drops:
1) your funds stay safe
2) the job can be retried on another node
Plus, nodes are pre-qualified for reliability, so failures are already minimized. If you want to run it elsewhere, you can reroute it yourself, as compute resource selection stays fully in the user’s hands. If the failure comes from your algorithm, the job is simply marked unsuccessful, and you’re only charged for the compute time actually used, not the full run.
More details are covered in the Ocean Network & Orchestrator FAQ.
@keshav_namdev What happens when things break in a setup like this?
Feels like the “what could go wrong” layer is where things get interesting.
Embedding GPU access directly into the IDE where developers already work — Cursor, VS Code, Windsurf — rather than requiring a separate infrastructure dashboard is the right UX decision for making compute feel invisible rather than burdensome. The escrow-based payment system that only releases funds after verified job execution solves the trust problem that plagues most decentralized compute networks; how does Ocean Orchestrator handle job failures mid-execution on a node — does the escrow mechanism cover partial compute costs, or is the user only charged for successfully completed work?
Ocean Protocol
@svyat_dvoretski If a node fails mid-job, you don’t lose funds for unfinished work, and you stay in control of where jobs run.
Ocean Nodes handle failures locally. If a node goes down, the job can restart on the same node once it becomes available again. If you want to run it elsewhere, you can reroute it yourself, as compute resource selection stays fully in the user’s hands.
However, if the failure is caused by the algorithm itself, the job is marked unsuccessful and you’re only billed for the compute time that actually ran, not the full job window. You can read more details about the Ocean Network and Orchestrator in the FAQ here.
Congratulations on the launch! Can I also choose the hardware parameters? For example, if I need to process video in real time, not all hardware will be able to handle it.
Ocean Orchestrator
@mykyta_semenov_ Yeah, you can choose. In the Ocean Network Dashboard, You pick nodes based on their specs. So if you need something strong enough for real-time video, you can select machines that actually meet that requirement.
And since the nodes are pre-qualified, you’re choosing from hardware that’s already been vetted. Here is the dashboard link
Running GPU jobs directly from the IDE is the right abstraction shift. Most workflows break when you have to context-switch between dev, infra, and billing.
The escrow-based execution model is also interesting. Tying payment to successful job completion could solve a lot of trust issues in decentralized compute.
The hard part is reliability: how do you ensure consistent performance across heterogeneous nodes, especially for long-running training jobs?
Ocean Orchestrator
@behnam_sherafat Ocean Network (ON) is designed with that reality in mind from the start. You’re not running on random machines, nodes are pre-qualified, environments are standardized, and there are checks running while your job is live.
And if something does go wrong (which can happen in any distributed system), it’s handled gracefully, jobs can be retried or moved, and since payment is tied to success, nodes are naturally pushed to stay reliable.
So even for longer training runs, it ends up feeling a lot more stable and predictable than you’d expect from decentralized compute. More details about ON and Ocean Orchestrator here
Really interesting approach to keeping AI job execution close to the dev workflow. Curious how you handle credential delegation when a dev triggers a cloud job from their IDE, are they using their own cloud credentials or does Ocean abstract that with a service identity? That part of the UX seems like it'd drive a lot of the architecture decisions.
I really appreciate that this addresses both sides of the equation. The data scientist workflow gets a lot of attention, but the ability for node operators to monetize idle GPU capacity is genuinely underrated. A lot of people sitting on high-end rigs have no clean path to doing that. Interested to see how the node network scales.
Started experimenting with it yesterday, and I must say I really enjoy working with the workflow
Ocean Orchestrator
@jpegcolector We are glad you enjoyed it. Curious, what are you currently building with ON?