AI inference for what you are building
Start with a model from the catalog, test it in the playground, and connect through the API. Bring your own model when your application needs something specific.
We are inviting early-access interest while we develop the platform.
General notes
Run open-weight models and your own models through one platform. HIPS is being built for the moment an idea needs useful inference: a response, an image, a transcript, a classification, or the next step in an application.
From model choice to useful output
A good inference platform should make the next step obvious. Discover what a model accepts, what it returns, and how its usage is charged. Try a realistic input before integrating it. Move from the browser to your application with the same model in mind.
The HIPS Console will bring those steps together, alongside the usage, activity, and billing information needed to keep building.
Connect your client
export HIPS_BASE_URL="https://ai.hips.com/v1"export HIPS_API_KEY="<your-api-key>"export HIPS_MODEL="<model-id>"Keep the client patterns you know. For supported OpenAI-style integrations, the intended starting point is three settings: your HIPS API key, base URL, and model.
Compute matched to the work
HIPS will coordinate compatible GPUs across a distributed network. Shared, usage-based inference gives open-weight models access to available capacity. Always-on capacity provides another route when a model needs to stay loaded between requests.
Choose how your own models are executed, with pool selection and the relevant policies visible before deployment.
For work that does not need an answer right away, batch jobs will let you send many requests at once in one file, in the OpenAI-style batch format. HIPS will run them as capacity becomes free, within a window you choose from 24 hours to 7 days, and tell you when they are done. A batch will cost half the on-demand rate of the same model. No completion time is promised: requests still unfinished when the window closes will come back as expired and will not be charged.
A clearer view of cost
Different models do different work. Their prices should reflect that. Language models use separate input and output token rates; other models use units suited to the result they produce.
Compare the available market and stable-price options before sending work. Track the resulting usage in one account balance.
When requests begin with the same text, such as a long system prompt or a shared document, HIPS will reuse that work automatically from a cache kept for your workspace alone. Cached input will be billed at a lower rate than other input, and writing to the cache will cost nothing.
Built for useful applications
Use language models for assistants, combine vision and language for documents, generate media, work with speech, or apply specialist models to classification. Explore the use cases to find a starting point for your application.
Get closer to launch
Join early access and tell us what you want to run. Your use case will help us understand which models and workflows matter most.