Skip to main content
SynapsAI Cloud hosts your models on shared, multi-tenant GPU infrastructure. You choose how models are kept warm, how they scale, and how you pay. This page explains the concepts that affect latency, availability, and cost.

​
How SynapsAI Cloud works

  1. You connect a Hugging Face repository and deploy it on the platform.
  2. SynapsAI loads your model onto shared GPU capacity and exposes an API endpoint scoped to your deployment.
  3. You send inference requests with an API key scoped to that deployment.
  4. The platform autoscales instances based on traffic and tears down idle capacity according to your settings.

Deploy your first model

Follow the quickstart to go from account creation to your first API call.

​
Readiness levels

When you deploy a model, you choose a readiness level. This controls how quickly instances can serve traffic and how much you pay when idle.
Always ready includes storage for prepped artifacts at no extra charge. Super fast adds a storage charge for those artifacts. Cold start does not use prepped storage.
See Deploy a model for configuration details and Optimizing costs for tuning spend.

​
Memory-based compute pricing

You are billed for the GPU memory (VRAM) consumed by active model instances. Example A: One instance using 50 GB of VRAM costs approximately $5.50/hour for that instance. Example B: Two active instances, each using 50 GB, cost approximately $11.00/hour in total. Factors that affect memory usage:
  • Model size and architecture
  • Precision (for example, Float32 uses more memory than BFloat16)
  • Quantization (INT4, INT8, FP8, EETQ)
  • Context length for LLMs
All estimated costs are shown in the deployment UI before you confirm.

​
Storage for Super fast deployments

When you choose Super fast, an additional storage charge applies for prepped model artifacts that enable rapid load times.
  • Price: $0.55 per GB per month
  • Example: 20 GB of prepared artifacts → $11.00/month
  • Example: 60 GB of prepared artifacts → $33.00/month
Always ready deployments include this storage at no extra charge.

​
Credits and billing

SynapsAI Cloud uses a credit system. Credits map to US dollars and pay for compute, storage, and inference usage.
  • Purchase credits from the Billing page.
  • Credits do not expire but are non-transferable and non-refundable.
  • Enable auto-pay to avoid interruptions when your balance runs low.
See Billing for purchasing credits, transaction history, and invoices.

​
Model lifecycle

Every deployment moves through defined states. Track them on the model page in the dashboard or via the API. For operational details, see Manage models.

​
Autoscaling and worker timeout

Each model has its own autoscaling policy:
  • Scale up threshold — average concurrent requests per instance before a new instance launches
  • Scale down threshold — load level at which idle instances may terminate
  • Cooldown period — minimum time between scaling events
  • Worker timeout — idle time before the last instance shuts down (Super fast and Cold start only)
See Autoscaling for parameter guidance.

​
Data handling

Inference requests are encrypted in transit (HTTPS), processed on shared GPU infrastructure running your model, and not sold to third parties. SynapsAI does not intentionally retain request payloads beyond what is needed to complete processing. For details on data location, retention, and video artifacts, see Data.

​
Supported tasks

SynapsAI Cloud supports a wide range of Hugging Face pipeline tasks — not only LLMs. Text generation, embeddings, image generation, speech, video, classification, and more are available depending on the model you deploy. See the full Supported tasks table for pipeline names, streaming support, and API endpoints.