Launch faster
Use an API contract instead of building a complete worker fleet before validating your product.
Distributed AI for developers
AI capacity that can scale with your business.
Connect your product to a network of independently operated AI workers. Submit supported workloads through one API, choose the delivery pattern that fits your customer experience, and track credit usage for every completed request.
One integrationSubmit supported text, image and media workloads through the Spin-Up API.
Distributed capacityEligible jobs are matched with available workers that advertise the required model.
Measured consumptionUsage and credits are recorded against completed workloads for operational visibility.
The business case
Add AI capability without owning every GPU
The distributed network separates access to AI capacity from ownership of the underlying hardware. Your team integrates once, while participating workers contribute supported models and processing capacity.
Flexible capacity
Route eligible workloads to workers that are online and configured for the requested model.
Usage-based credits
Consume AI Credits according to measured work instead of purchasing dedicated hardware upfront.
Choose the experience
Wait briefly for interactive results or queue background work and collect it when complete.
How it works
From your application to an available worker
Your business remains the customer-facing application. Spin-Up handles request intake, matching, job state and usage reporting across the worker network.
Authenticate
Your server uses a Spin-Up API key. Keep it out of browsers and mobile applications.
Submit
Choose a supported model and send the workload with your preferred wait behavior.
Match
The network makes the job available to an eligible worker with that model and capacity.
Process
The assigned worker runs the model and securely reports the result and usage measurements.
Retrieve
Your application receives the live response or retrieves the queued result by request ID.
Choose a workflow and language
Use these examples from trusted server-side code. Never expose a Spin-Up API key in public browser JavaScript.
Live text · PowerShellWait briefly for a completed response, with a request ID fallback.
PowerShell
Commercial model
Credits connect usage, cost and worker rewards
Businesses fund AI Credits and spend them when workloads are processed. The charge can account for input usage, more expensive output generation and the model tier required to perform the work. A portion of qualifying settlement is allocated to the worker that supplied the capacity.
Measured input + weighted output × model-tier multiplier = workload credits
Designed for cost visibility
- Prompt, output and total usage are returned for completed text workloads.
- Larger model tiers consume credits at a higher rate than smaller models.
- Each request ID provides a traceable link between business activity and AI usage.
- Available balance and credit activity can be reviewed from the account dashboard.
Market comparison
Compare models by workload
Explore representative language, coding, audio and video models. Each view uses the billing unit appropriate to that workload.
Network earnings: the amount allocated to the worker that completes a qualifying request, calculated as 70% of the compressed customer charge. The remaining 20% platform fee and 10% reserve are not included.
Important: These are representative workload options, not claims of equal quality, speed, duration, resolution, context or capability. “Not offered” means no directly comparable first-party generation API was identified in the referenced provider catalogue.
Reference rates checked 5 August 2026: OpenAI Anthropic Google Mistral AI
Where it fits
Good workloads for a distributed network
The strongest early use cases can tolerate variable worker availability, have clear retry behavior and do not depend on a single proprietary model.
Document processing
Classification, extraction, tagging, rewriting and summaries that can run asynchronously.
Customer operations
Draft replies, conversation summaries and internal agent assistance with application-side review.
Developer workflows
Code explanation, structured transformations, test generation and queued repository tasks.
Creative generation
Supported image and media generation where completion can be retrieved as a background job.
Batch enrichment
Process records from business systems while controlling concurrency and tracking each request.
Fallback capacity
Add a distributed processing route for eligible work as part of a broader multi-provider strategy.
Plan for production
Build for asynchronous, variable-capacity compute
Keep request IDs
Persist each ID with your own customer or workflow record so jobs remain traceable.
Handle every state
Design for queued, assigned, completed, failed, cancelled and expired outcomes.
Use timeouts
Place a firm limit on live waits and move gracefully to queued retrieval when needed.
Protect data
Send only data appropriate for the selected processing route and your governance requirements.
Beta availability: Spin-Up AI Worker and the distributed processing network are currently in beta. Worker availability, supported models and completion times may vary. Evaluate the service with non-critical workloads and maintain appropriate retry, validation and fallback paths.
Start integrating
Turn your next AI workflow into a distributed job.
Create an account, fund AI Credits and issue an API key for your server-side integration.