Running Liquid AI as a service, AI Infrastructure

Liquid AI Agency Implementation, On-Device Model Deployment

Learn how to architect and deliver on-device AI solutions using Liquid Foundation Models, from selecting optimized variants to fine-tuning on client data and deploying across phones, laptops, and edge hardware. This course teaches agencies to build productized services around local inference, eliminating cloud dependencies and API costs for clients.

Open the decision record for Liquid AI

What does running Liquid AI for clients commit you to?

Published figures for this service. Blank fields are not published.

Monthly tool cost
$0 (free tier for companies under $10M revenue) plus team labor
Time to first value
high setup complexity; 2-4 weeks to first value
Payback
Break-even within first month with one client at $5,000 setup fee
Guided implementation
8 hours

Is Liquid AI worth running as a client service?

Liquid AI is a strong opportunity for ML-savvy agencies targeting edge AI applications. The free tier reduces upfront costs, but the high technical barrier limits the addressable market. Ideal for agencies with existing embedded systems expertise.

An agency-fit judgement for reselling this service. It is separate from the tool description on the decision record.

Before you start

What has to be in place before the first client engagement.

Tools and subscriptions

  • Liquid AI free/commercial account
  • LEAP SDK access
  • Target hardware for testing (e.g., laptop with GPU, phone with NPU)
  • Python environment for fine-tuning

People and inputs

  • ML engineering expertise (at least one team member experienced with model fine-tuning)
  • Custom dataset from client or synthetic data pipeline
  • Development time: 2-4 weeks for initial setup

Included with the course

7 working documents for delivering this service.

  • On-Device Deployment Architecture Checklistchecklist
  • Hardware Specification Assessment Worksheetworksheet
  • LEAP SDK Fine-Tuning SOP for Client Datasop
  • Model Selection Matrix (2700+ LFM Variants)template
  • Inference Latency Benchmarking Guideguide
  • Client Data Residency Compliance Checklistchecklist
  • Runtime Integration Decision Tree (llama.cpp, MLX, ONNX, CoreML, vLLM)template

Listed by name. These documents are not yet published as individual downloads.