Beta complete

Serve private models without paying to keep every GPU warm

Our private beta has wrapped. We are taking a short development break while we prepare the next release. Sign up to hear when we are back.

Checkpoint loading, measured separately

These results measure one part of a cold start: loading prepared BF16/FP16 weights. They are not presented as end-to-end request latency.

Model	Load Time (Seconds)
Qwen3 4B	0.741
Mistral 7B	1.296
Qwen3 8B	1.481
Meta Llama 3.1 8B	1.481
Mistral-Nemo 12B	2.222
Qwen3 14B	2.593
Mistral-Small 3.1 24B	4.444
Qwen3 32B	5.926
Meta Llama 3.1 70B	12.963

Measured: prepared checkpoint load time for the listed BF16/FP16 model.
Excluded: a typical ~3s allocation and warmup period, networking, queuing, and generation.
Evaluate in a proof of concept: end-to-end cold start, time to first token, throughput, concurrency, and total cost.

Results may vary by model architecture, precision, configuration, and capacity.

Built for the long tail of private models

The advantage is strongest when model flexibility, low utilization, and startup latency matter at the same time.

“Over the past year, I built this infrastructure focused on reducing model startup latency and making custom model deployment simpler. It started when I needed to deploy a fine-tuned Mistral 7B model and realized existing options were either expensive, difficult to operate, or too slow to start.”

Maxime Champagne Founder

Paying for idle GPUs

Customer-specific models and internal tools often sit quiet between bursts. Always-on endpoints keep billing even when no inference is running.

Slow model loading for on-demand tasks

Scaling from zero means waiting on provisioning, checkpoint loads, and warmup before the first request can run — exactly when users expect instant responses.

A serving stack to maintain

Schedulers, images, drivers, runtimes, autoscaling, logs, and capacity planning turn model deployment into an infrastructure project.

One platform, many intermittently used models

Start with a production model and representative traffic. Compare cold and warm latency, scaling behavior, and estimated monthly cost.

Vertical AI products

Serve customer-specific or fine-tuned models without maintaining an always-on endpoint for each customer.

Evaluation platforms

Load and switch among many checkpoints for experiments, benchmarks, and model selection workflows.

Enterprise AI teams

Deploy intermittent classification, vision, speech, and language workloads behind private APIs.

We run the hardware layer

You ship the model. We handle the GPU nodes, storage path, and private serving layer beneath it.

no image pulls
no container warmup
no kubernetes to manage
no VM boot cycles

Built for demanding inference workloads

Private endpoints for compatible Hugging Face models
Readiness options tuned to latency and idle-cost needs
OpenAI-compatible APIs for low-friction integration
Managed GPU allocation, storage, scaling, and observability

The SynapsAI Cloud difference

Managed infrastructure, explicit benchmark scope, and billing choices matched to actual model usage.

Blazing-Fast Deployment

Immediate provisioning on GPU clusters. Full setup handled automatically. A simple API experience on top of managed infrastructure.

Economics for variable traffic

Choose per-token billing for intermittent text-generation traffic or hourly billing when sustained throughput is the better fit.

Rapid Model Loading

Prepared model artifacts load from fast storage in seconds. Published results separate checkpoint load time from allocation and warmup.

Cost Monitoring

Real-time dashboards show token usage, user-level billing, and project costs.

More than LLMs

Deploy compatible Hugging Face pipeline tasks across text, image, speech, audio, and video.

Text Classification
Text-to-Image
Image-to-Text
Text-to-Speech
Speech-to-Text
Text-to-Video
Video-to-Text
Text-to-Audio
Audio-to-Text

See the full list of supported pipelines

Taking a short development break

Thanks to everyone who joined the beta. Leave your email and we will let you know when SynapsAI Cloud is ready for the next chapter.

Platform

Platform capabilities

Managed serving for teams deploying private, custom Hugging Face models without operating the underlying GPU stack.

Model load lifecycle

From first request to first token — see how SynapsAI Cloud eliminates the latency traps that slow traditional inference stacks.

Request to endpoint

Your client sends an inference request to the private API endpoint.

Rapid allocation

GPU capacity is allocated on our infrastructure in seconds — not minutes.

Right-sized resources

The model claims only the compute and memory it needs — not an entire GPU.

Rapid model loading

Checkpoints load from local NVMe at unprecedented speed.

Rapid inference

The model streams tokens back with minimal time-to-first-token.

Iterate with the platform

Track the metrics that determine user experience and inference economics, from latency percentiles to token volume and cost.

Performance

P50, P95, and P99 latency distributions

Volume

Traffic, concurrency, and usage over time

Economics

Input tokens, output tokens, and project-level cost

Analytics Dashboard

Interactive charts for monitoring response time, requests, and token usage.

Team Management

Easily invite and manage team members with role-based access control.

Invite team members via email
Assign roles and permissions
Real-time collaboration

Security controls

Review platform controls against your own security and compliance requirements.

Inference isolation
Encryption in transit and at rest
Access and activity records

Define “private” before production

A private API endpoint and isolated inference environment do not automatically mean dedicated hardware. On-prem and BYOC (bring your own cloud) deployments are available. Review model storage, tenancy, residency, retention, access, capacity, and support requirements before production.

Built for teams

Invite members, control access, share resources, and keep billing in one place.

Role-Based Access Control

Define granular permissions for different user roles and control access to resources.

Comprehensive Audit Logs

Track all platform activity for compliance and troubleshooting.

Shared Models & Resources

Collaborate on AI projects by sharing models and datasets within your team.

Secure API Key Management

Generate and manage API keys with specific permissions and usage limits.

Centralized Team Billing

Consolidate all usage and costs under a single billing address.

Deployment Review

Review on-prem, BYOC (bring your own cloud), isolation, data handling, networking, capacity, support, and contractual requirements before production.

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.