Now training open-weight models: Qwen, Llama, Inkling & moreFine-Tune Any Model on Your Business Data
Your data stays private. It never trains anyone else's model. Fine-tune an open-weight model on it and own the whole stack: your data, your weights, your training, your inference. Then drop it into your app with a one-line OpenAI SDK swap. No ML team required.
Private + Owned
Your data, weights & endpoint
1-Line Swap
Drop into the OpenAI SDK
Model-Level MCP
Connect your tools in a click
Your data. Your model. Your endpoint.
Most AI tools rent you access and keep your data. We flip it: you keep the data, own the fine-tune, and get a private endpoint your team drops into in one line.
Private & yours, end to end
Your uploads are encrypted and never train a shared model. Flip on delete-after-training and the raw files are erased the moment training finishes. Open-weight fine-tunes are yours: request an export to run anywhere. You own the data, the weights, the training, and the inference.
Drop-in for any dev team
Every model exposes an OpenAI-compatible endpoint. Point the OpenAI SDK's base_url at it. That's the whole integration. Works with LangChain, LlamaIndex, OpenCode, Cursor, or any HTTP client. New teams ship in minutes; existing teams change one line.
Model-level MCP
Give each model its own tools with MCP: a knowledge base, ticketing system, or internal API it can call while it answers. Add credentials once (encrypted), scoped to that model. No servers to run, nothing to redeploy.
The entire integration
from openai import OpenAI
client = OpenAI(
base_url="https://app.infoplatform.ai/api/v1",
api_key="mf_sk_…",
)
client.chat.completions.create(
model="your-model-id",
messages=[{"role": "user", "content": "Hi"}],
)Built for Teams That Need AI That Actually Works
Generic AI falls short when you need domain expertise. InfoPlatform.ai fine-tunes the open-weight model of your choice, so it understands your business.
Post-Quantum Migration Copilot
The Problem
NIST/NSA deadlines are here and “harvest now, decrypt later” is active, but your cryptographic inventory is too sensitive to send to any SaaS or hosted API.
The Solution
Fine-tune an open-weight model on your own codebase to inventory cryptography (CBOM), risk-rank RSA/ECDH/ECDSA, and generate PQC migration PRs, running entirely inside your walls. You own the weights.
Real-world impact
A regional bank stood up a private migration agent in an afternoon and produced a CycloneDX CBOM across 400 repos without a single line of code leaving its network.
Customer Support AI
The Problem
Repetitive tickets drain your support team's time and morale.
The Solution
Fine-tune Qwen 3.5 or Llama 4 on your macros and past tickets. Answers like your best agent (your tone, your policies) for pennies per resolution.
Real-world impact
A SaaS company reduced first-response time from 4 hours to 30 seconds while maintaining 94% satisfaction.
Coding Agents That Know Your Repo
The Problem
Off-the-shelf agents don't know your patterns, libraries, or style.
The Solution
Fine-tune GLM 5.2 or Kimi K2.6 on your codebase, then drop it into OpenCode, Cursor, or any harness. Top-tier coding at a fifth of the cost.
Real-world impact
An engineering team of 30 saved 120 hours/month on review while improving defect detection by 60%, running a self-hosted model they own.
Legal Contract Drafting
The Problem
Associates spend hours on boilerplate that follows established patterns.
The Solution
Fine-tune an open-weight model on your clause library, with weights you own and can keep in your own environment. First drafts in your firm's style.
Real-world impact
A mid-size law firm cut contract drafting time by 70% and reduced revision cycles from 5 to 2.
One Platform, Every Model
Open-weight models are now genuinely competitive on quality. Fine-tune the best of them and own them outright, with no shared-model lock-in.
GLM 5.2
OpenZ.ai · MIT
Matches Opus 4.8 on coding at ~1/5 the cost. 1M context.
Open · you own the weights
DeepSeek V4
OpenDeepSeek · MIT
Long-context reasoning & coding specialist.
Open · you own the weights
Qwen 3.5
OpenAlibaba · Apache 2.0
Best fine-tuning ecosystem. Multilingual & agentic.
Open · you own the weights
Kimi K2.6
OpenMoonshot · Open
Elite tool use and long-horizon agent workflows.
Open · you own the weights
Llama 4 Scout
OpenMeta · Community
Reliable all-rounder with a 10M-token context.
Open · you own the weights
Inkling
OpenThinking Machines · Apache 2.0
Open-weight 975B multimodal MoE: text, image & audio. Fine-tune via Tinker.
Open · you own the weights
Some very large models (e.g. GLM 5.2, DeepSeek V4, Kimi) train today on a mapped open base of similar strength; the wizard shows you exactly which. New open-weight releases are added within days.
From Data to Deployed in 6 Simple Steps
No ML expertise required. Our guided workflow handles the complexity so you can focus on results.
Upload Data
Spreadsheets, docs, or code
Pick a Model
Open-weight, yours to keep
Describe Goal
Tell us what you need
Viability Check
We check if it'll work
Train & Test
Chat, rate, get better
Go Live
OpenCode, Cursor, your app
Before You Train
AI-Powered Feasibility Analysis
Before spending a dollar on GPU time, our feasibility engine analyzes your data quality, task complexity, and expected model performance with a visual radar chart and cost estimation.
- Radar chart scoring across 6 dimensions
- Accurate cost and time estimates before training
- Actionable recommendations to improve data quality
- Go / No-go recommendation with confidence score
Fully Automated
Event-Driven Training Pipeline
When you hit "Train," we spin up a GPU instance, fine-tune your model, encrypt and store the weights, then spin down the infrastructure. Zero idle compute costs.
- Spin up on demand, no standing infrastructure
- Pay per training run, not per hour
- Optionally delete your uploaded data the moment training finishes
Ship & Improve
Test, Deploy, and Improve
When your model is ready to serve, you get a private, OpenAI-compatible endpoint. Test it in the built-in chat, hand your team a one-line base_url swap, connect its tools via MCP, and improve it with your feedback.
- One-line base_url swap into the OpenAI SDK, LangChain, OpenCode, or Cursor
- Give the model your tools with model-level MCP
- Built-in feedback + one-click retrain that learns from your corrections
Everything You Need to Train Custom AI
A complete platform from data ingestion to production inference.
Best-in-Class Open Models
Fine-tune GLM 5.2, DeepSeek V4, Qwen 3.5, Kimi, Llama 4, or Inkling. Pick the best open-weight model per project.
You Own the Weights
Open-weight fine-tunes are yours. Request an export of your adapter to run in your own cloud, or let us serve them. No shared-model lock-in.
Train Where You Choose
Run training on our fast serverless GPUs, on dedicated GPUs in our own cloud (no third-party training service touches your data), or on Tinker by Thinking Machines Lab. Same data, your call.
Trained for the Price of Dinner
Open-weight LoRA fine-tunes start around $8. On-demand GPUs spin up and down, so you never pay for idle compute.
One-Line OpenAI SDK Swap
Every model ships an OpenAI-compatible endpoint. Change base_url in the OpenAI SDK. That's it. Works with LangChain, LlamaIndex, OpenCode, Cursor, or any HTTP client.
Just Upload Your Data
Drag-and-drop spreadsheets, documents, code, or exports. We clean, dedupe, and prepare it automatically.
Viability Check First
We analyze your data quality and task complexity before you pay, so you know it'll work.
Learns From Your Corrections
Rate answers and fix the wrong ones. Your corrections become training data, so a one-click retrain makes the model better exactly where it missed.
Model-Level MCP
Connect each model to your tools with MCP: knowledge bases, ticketing, or internal APIs it can call while it answers. Credentials encrypted, scoped per model, no infra to run.
Transparent Costs
Clear dashboards for training runs, token usage, and inference volume. No per-GPU-hour surprises.
Delete Your Data After Training
Turn on automatic deletion and your uploaded files are permanently erased from our servers the moment training finishes. Your model keeps working; the training data doesn't stick around.
Simple, Transparent Pricing
Start free. Upgrade when you see results. Training included in every paid plan. Cancel anytime.
Calculate Your ROI
See how much time and money your team could save with a custom AI model.
Estimated monthly savings
$1,400
Based on 70% automation of the selected task