Entry Point AI - Fine-tuning Platform for Large Language Models

Entry Point AI is a web platform for people who want a model tuned to one job instead of a general chatbot. It works as a no-code fine-tuning platform with three tools: Transforms, which adds an AI column to a spreadsheet and fills every row with a tag, an extraction, or a short summary; Synthetic Data, which creates extra labeled examples when a dataset is too small; and Fine-tuning, which uses the training data you approve to fine-tune an LLM.

The pitch is that this work usually falls to engineers. Preparing JSONL files, wiring provider APIs, and guessing which hyperparameters matter takes time. Entry Point puts the same steps in a browser with templates and cost estimates, and connects to your own provider accounts, so inference charges go straight to OpenAI, Anthropic, Google AI, or Groq. That means you can fine-tune OpenAI models and models from other providers from one screen, without touching the raw training data files.

The main limits sit in the business model. The row limit is shared across every project in the workspace, and provider charges are billed separately from the subscription. Small datasets keep the price low. Large production workloads push a team toward the Pro plan or a custom enterprise quote.

Getting Started

1. Create an account at app.entrypointai.com. No credit card turns up in the sign-up flow, and the free tier covers 300 rows for testing.
2. Connect a model provider with your own API key, then choose which model should handle prompts or training.
3. Import a dataset from CSV or write prompts and completions in the editor, using a template with variables for each column.
4. Run a transform to fill the table, or generate synthetic rows until each label has enough examples.
5. Check quality with exact match or a scoring prompt, then launch a fine-tune and point your own API at the result.