Which models can I fine-tune?

Open-weight models: Kimi K2.6, DeepSeek V3.1, Qwen 3.5, Nemotron, GPT-OSS, Thinking Machines' multimodal Inkling, Llama, and more, from 3B models up to 1T-parameter MoEs. Every model trains as the exact weights you pick, no substitute base.

Where does training actually run?

On dedicated GPUs, per job: a machine is provisioned just for your run and destroyed afterward, so idle compute never hits your bill and your data isn't pooled with anyone else's. The largest models train on Tinker by Thinking Machines Lab. In-house contracts run entirely inside your own environment.

Can I delete my training data after training?

Yes. Turn on delete-after-training and your uploaded files are permanently erased from our servers the moment training completes. Your trained model keeps working; retraining later just means uploading again.

Are open-source models actually good enough?

In 2026, yes. Open-weight models now match top closed models on coding and reasoning benchmarks, and fine-tuned on your data, an open model usually beats a general closed model on your specific task. Plus you own it instead of renting it.

Why open-weight instead of a closed API?

Ownership. The weights are yours to export and run anywhere, your data never trains anyone else's model, and you're not locked into one vendor's pricing or roadmap.

Who owns the trained model weights?

You do. Request an export of your adapter and run it in your own environment whenever you like. We never use your data to train shared or other customers' models.

How hard is it for my dev team to integrate?

About one line. Point the OpenAI SDK's base_url at your private endpoint, pass your API key and model id, done, LangChain, LlamaIndex, OpenCode, Cursor, or any HTTP client. New teams ship in minutes; existing teams change one line.

Can my model use my own tools (MCP)?

Yes, at the model level. Connect each model to MCP servers, knowledge bases, ticketing, internal APIs. Credentials are stored encrypted, tools are discovered automatically, and there's no MCP infrastructure to run.

Can we run InfoPlatform in-house instead of hosted?

Yes. Use the hosted platform self-serve, or email team@infoplatform.ai and we'll stand up the full training and inference stack inside your own infrastructure. Either way, the weights are yours.

How is my data kept secure?

Encrypted in transit (TLS) and at rest. Each training job runs on its own machine and is destroyed afterward. MCP credentials use AES-256-GCM. We never train shared models on your data, and you can delete everything at any time.

Fine-tune open-weight models · You own the result

Your own AI model, trained on your data

Generic models guess; they've never seen your tickets, contracts, codebase, or determinations. Upload your documents, fine-tune a top open-weight model, and get a private endpoint that answers like your team : hosted or inside your own walls.

Some features call third-party APIs : Privacy 3a says exactly what goes where, and in-house deployment keeps everything on your side.

No spam, and no email until we have something to tell you.

Never leaves your control

You own data + weights

Drop-in OpenAI SDK

Model-level MCP

InfoPlatform.ai dashboard: your models, datasets, and usage at a glance

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 jurisdiction.

A hosted API can log, cache, train on, or staff-review everything you send it. Here, the model is trained on your data and answers only you.

Private end to end

Encrypted uploads that never train a shared model, delete-after-training, and weights you can export and run anywhere.

Drop-in for any dev team

Every model ships an OpenAI-compatible endpoint. Point base_url at it and ship, LangChain, Cursor, OpenCode, anything.

Model-level MCP

Connect knowledge bases, ticketing, or internal APIs per model. Credentials encrypted once, scoped to that model, zero infra.

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"}],
)

Open

Thinking Machines · Apache 2.0

Open-weight 975B multimodal MoE: text, image & audio. Fine-tune via Tinker.

Open · you own the weights

Every model fine-tunes as the exact weights you pick, no substitute base, from 3B models up to 1T-parameter open MoEs. 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

Every tab in the app, doing real work

Real screenshots from the live product, not designed mockups.

Overview

Everything at a glance

Active models, datasets, requests, and feedback score in one view.

Your Data

Upload once, ready to train

Drag-and-drop files, each scored for quality before you train on it.

Agents

Workflows with human control points

Chain your models and drop a review step wherever you want one.

Approvals

Nothing ships without a person

Runs pause here until someone approves, corrects, or rejects, fully audited.

Connections

Give your agents real tools

MCP servers and external services, keys encrypted, scoped per model.

Billing

Usage you can actually see

Live counts tracked against your plan limits. No surprise invoices.

Settings

Your account, your keys

Create, rotate, and revoke the API keys that call your models.

Before You Train

AI-Powered Feasibility Analysis

Before you spend a dollar on GPU time, our feasibility engine scores your data quality, task complexity, and expected performance, with a radar chart and a cost estimate.

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

Viability assessment: data volume, data quality, goal clarity, and domain match scored before training

Integrate tab: point the OpenAI SDK at your private endpoint with a one-line base_url swap

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

Your trained model serves from a private, OpenAI-compatible endpoint. Test it in the built-in chat, hand your team the one-line swap, connect its tools via MCP, 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

Test chat: a fine-tuned model answering in the business's own voice

Everything You Need to Train Custom AI

A complete platform from data ingestion to production inference.

Best-in-Class Open Models

Kimi K2.6, DeepSeek V3.1, Qwen 3.5, Nemotron 3, GPT-OSS, or Inkling, trained as the exact weights you pick.

You Own the Weights

Export your adapter and run it anywhere, or let us serve it. No shared-model lock-in.

Hosted or In-House

Start self-serve on our dedicated GPUs (or Tinker), or contract an in-house deployment with DPA, SLA, and a named engineer.

One-Line OpenAI SDK Swap

Change base_url and ship. Works with LangChain, LlamaIndex, OpenCode, Cursor, or any HTTP client.

Just Upload Your Data

Spreadsheets, documents, code, exports, cleaned, deduped, and prepared automatically.

Viability Check First

We analyze data quality and task complexity before you pay a dollar, so you know it'll work.

Learns From Your Corrections

Fix wrong answers; a one-click retrain improves the model exactly where it missed.

Agents With a Human Sign-Off

Put an approval gate anywhere in a workflow. Nothing ships until a person decides.

Model-Level MCP

Connect knowledge bases, ticketing, or internal APIs per model. Encrypted credentials, zero infra.

Transparent Costs

Clear dashboards for training runs, token usage, and inference volume. No surprises.

Delete Your Data After Training

Raw files are permanently erased the moment training finishes. Your model keeps working.

Pricing built for ownership

You're billed per determination: one completed answer, however many model calls it took to produce. Shape a contract on a call, or start self-serve below. No free tier, no trial.

Recommended

Enterprise

Custom

Tailored to your business

Determination volumes set by contract

A contract tailored to your needs and requirements
Hosted with us, or implemented in-house on your own infrastructure
Every open model, dedicated capacity, custom volumes
DPA, SLA, data-residency and security review support
Named engineer and priority everything
You own the data, the weights, the training, the inference

Starter

$500/month

Base platform fee, training included

1,000 determinations included, then $0.75 each

2 custom models, fine-tuned on your own data
1 configurable agent with human-approval steps
3 training runs per month
Knowledge Base: 50k indexed chunks with cited answers
Fine-tune the exact model, own the weights
Email support

Growth

$1,500/month

Base platform fee, training included

5,000 determinations included, then $0.50 each

5 custom models, fine-tuned on your own data
3 configurable agents with human-approval steps
12 training runs per month
Knowledge Base: 250k indexed chunks with cited answers
Priority support
Usage dashboards
Retrain from your feedback

Pro

$4,000/month

Base platform fee, training included

20,000 determinations included, then $0.35 each

20 custom models, fine-tuned on your own data
10 configurable agents with human-approval steps
30 training runs per month
Knowledge Base: 1M indexed chunks with cited answers
The largest models (100B–1T) unlocked
Dedicated support
Fastest processing

Calculate Your ROI

See how much time and money your team could save with a custom AI model.

What task do you want to automate?

Hours spent per month

Hourly cost ($)

Estimated monthly savings

$1,400

Assumes 70% of these hours are absorbed by the model, an estimate, not a measured result. Compliance work scores lowest because every answer still needs a human sign-off.

Frequently Asked Questions

Ready to Own Your AI?

Pick an open-weight model, fine-tune it on your data, and own the result. Use our hosted platform, or ask us to implement it in-house so inference never leaves your walls. Either way, the data and the weights are yours.