EdgeFlow

Deploy AI anywhere.
Run efficiently everywhere.

Cut inference cost. Keep performance and control.
Deploy across edge and cloud with one interface.

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Our Platform

EdgeFlow provides a complete end-to-end solution for efficient AI inference deployment

🎛️ Unified Control

One console for every deployment

Track CPU and GPU workloads, manage access policies, and promote releases from staging to production without leaving the dashboard.

⚙️ Ops Automation

Close the loop on reliability

Streaming metrics, proactive SLO alerts, and guided runbooks help your operators resolve incidents before customers notice.

🛡️ Enterprise Ready

Security & compliance baked in

Audit trails, role-based controls, and SOC2-ready policies make it easy to bring EdgeFlow into regulated environments.

⚡

InferX

Core Inference Engine

Executes LLMs and VLMs efficiently across CPU, GPU, and edge hardware. Ensures optimal performance independent of environment. Acts as the universal runtime layer for AI workloads.

🚀

ModelRun

Deployment & Management

Handles model packaging, integration, and rollout. Automates deployment pipelines for enterprises. Adapts to evolving infrastructure and business needs.

⚙️

CoreShift

Dynamic Hardware Optimizer

Monitors and reallocates compute resources in real time. Balances load across CPUs, GPUs, and edge nodes. Cuts inference cost by 40% through efficient utilization.

🔗

Unified Stack

End-to-End Platform

Model Abstraction Layer, Compiler Optimization, Unified Runtime, and Monitoring & CI/CD Integration. Deploy seamlessly across edge and cloud.

Why EdgeFlow

Many enterprise models run on CPUs today. EdgeFlow gives you strong throughput using quantization and optimized kernels. Keep data on-prem or at the edge. Use one runtime for LLM and VLM.

1
Package your model into an EdgeFlow bundle.
2
Expose REST and streaming endpoints.
3
Ship to edge or cloud with your CI pipeline.
4
Track latency and cost with built-in metrics.

Example request

{
  "model": "edgeflow-qwen-3-vl",
  "input": {
    "prompt": "Summarize this image",
    "image": "data:image/png;base64,..."
  },
  "params": {
    "max_tokens": 2048,
    "temperature": 0.2
  }
}

cURL example

curl -X POST \
  https://api.edgeflow.local/v1/generate \
  -H "Authorization: Bearer <token>" \
  -d @request.json

Key Features

CPU-first inference

Run LLM and VLM on any hardware with low memory footprint and strong throughput.

Consistent deploys

One runtime across laptop, data center, and edge. Same APIs, same behavior.

Lower TCO

Reduce GPU spend using CPU pools without loss of quality for many workloads.

Enterprise ready

FastAPI endpoints, auth, observability, and CI/CD hooks built in.

Simple Pricing

Choose the plan that fits your needs. Scale as you grow.

Starter

Forever free

Local development
Single model
Community support

Popular

Team

$499

/month, billed monthly

CPU clusters
REST and streaming
Metrics and dashboards
Email support

Enterprise

Custom

Custom pricing for your scale

SLA and SSO
Air-gapped deploy
On-site support
Custom integrations

Frequently Asked Questions

Find answers to common questions about EdgeFlow

Do I need GPUs?

No. EdgeFlow targets CPUs first. You add GPUs later if needed for additional performance.

Which models are supported?

LLM and VLM families that support quantization and CPU kernels. Qwen-VL, Gemma, Llama, and others.

Can I deploy on-prem?

Yes. EdgeFlow supports on-premise deployment with air-gapped options for enterprise customers.

What's the typical TCO reduction?

CoreShift can reduce infrastructure costs by 40% through efficient CPU utilization compared to GPU-only approaches.

Ready to optimize your AI inference?

Join our waitlist to get early access and be notified when EdgeFlow is ready for your use case.

Deploy AI anywhere.Run efficiently everywhere.