The all-in-one platform for models and agents. On our cloud or yours.

Run LLMs, diffusion, classifiers, knowledge bases, and agents behind one runtime. Start on SynapsAI Cloud, then take the same stack to GPUs you already operate.

Everything under one hood

Stop stitching five platforms together. This is what running your own OpenAI API Platform would feel like, except every model on it is yours.

  • Wide model support — LLMs, diffusion, vision, speech, embeddings
  • Dedicated agents — tools and traces, no extra orchestration
  • Integrated knowledge bases — search your files from the same private API

Explore the platform

No procurement to start. No rebuild to leave.

Step 1 — Start on SynapsAI Cloud

Deploy from a Hugging Face repo, get a private endpoint, and skip provisioning infra.

Step 2 — Move to your hardware (or stay)

Point the same runtime at your own GPUs — on-prem, colo, or a major cloud.

Supported targets include on-prem, AWS, Azure, Google Cloud, Oracle, Nebius, and CoreWeave.

Deploy SynapsAI where you need it.

Access GPU infrastructure across multiple providers and regions through a single platform.

How a model actually reaches production

You point at the checkpoint. SynapsAI builds, serves, and monitors it.

  1. You: model source — Hugging Face, S3, or local
  2. SynapsAI runtime — build, package, schedule
  3. Endpoint — OpenAI-compatible API
  4. Dashboards — latency, cost, replay

SynapsAI in the wild

Vertical AI products

Serve customer-specific models in the cloud, then pin sensitive tenants to on-premises infrastructure without changing the API.

Evaluation platforms

Load and switch among many checkpoints with built-in scheduling, storage, and monitoring controls.

Enterprise AI teams

Deploy classification, vision, speech, and language workloads behind private APIs — on GPUs you already operate.

The platform

Everything you need to run models in production, not just serve them

Agents, knowledge, request replay, cost and latency, and the access model behind every private API.

Wide model range support

Deploy LLMs, diffusion models, classifiers, vision, audio, video, and embedding pipelines behind one runtime — using standard Hugging Face pipeline tasks.

  • Text & language
  • Vision
  • Image generation
  • Audio & speech
  • Embeddings & ranking
  • Classification

40+ pipeline tasks. Deploy any Hugging Face pipeline task on SynapsAI Cloud.

View full task list

Tool-using runs without an orchestration layer

Attach knowledge bases, web search, HTTP fetch, a calculator, Python execution, and sub-agents. Set a step limit. No orchestration code required.

Point it at where your knowledge already lives

  • OneDrive
  • Dropbox
  • Notion
  • Google Drive
  • S3 / object storage
  • Direct upload

Every request, replayable

Opt-in per-endpoint logging stores the raw input and output so you can inspect a failed call the same way you would a terminal capture. Data never leaves the customer's own project.

Cost, latency, and utilization in one place

Spend by model

See which checkpoints actually consume budget, not a blended average.

Percentiles, not averages

p50 / p95 / p99 so tail latency is visible before users feel it.

GPUs you already own

How much of the pool is busy versus sitting idle between bursts.

Role-based access, API keys, and audit logs

Roles, keys, audit logs, and network isolation on the same control plane. Owner, Admin, Developer, and Viewer permissions cover deploying models, managing billing, viewing analytics, managing the team, and creating API keys.

Taking a short development break

Leave your email and we'll let you know when this stack is ready for the next chapter.

Pricing

Match inference cost to your traffic

Estimate pricing for your exact Hugging Face model, then choose per-token or hourly compute based on how consistently it runs.

Three production paths

Readiness controls how quickly a model can load. Billing controls how compute is charged.

Serverless: variable demand

For experiments and intermittent traffic where avoiding continuously provisioned compute matters most.

Production: predictable throughput

For steadier traffic that benefits from hourly billing and workload-specific capacity planning.

Enterprise: governed deployment

On-prem and BYOC (bring your own cloud) deployments are available. Evaluate capacity, isolation, networking, support, and contractual requirements.

Model storage

Keep prepared artifacts close to compute when faster checkpoint loading is worth a recurring storage charge.

Super-Fast Readiness

$0.55 / GB / month

Applies only when you select Super-Fast Readiness. For example, 100 GB of prepared artifacts costs $55 per month before compute. Other readiness options may have different or no direct storage cost.

Choose how compute is billed

Rates depend on model size, precision, GPU memory, and target performance. Use the estimator for model-specific numbers.

Per-hour billing

Best for sustained demand or scheduled periods when predictable throughput matters more than scaling to zero.

Per-token billing

Best for variable text-generation traffic. Compute charges follow input and output volume instead of a continuously running endpoint.

You select one billing model (per-hour or per-token) for your compute needs. You do not pay for both simultaneously.

Estimate your costs

Enter your model details for a preliminary cost estimation. For precise quotes, please contact us.

Example LLM rates

BF16 · 128k context · per hour

  • 35B Parameters — $5.4/hr
  • 24B Parameters — $4.5/hr
  • 8B Parameters — $3.0/hr

Example transcription rates

per hour

  • 2B — $0.70/hr

Use the pricing calculator on the live site with your Hugging Face model path, optional Hugging Face token, and model precision (FP16, BF16, FP32, or INT8) to receive estimated storage, hourly compute, and per-token costs.