Entry Point AI
Entry Point AI is a fine-tuning platform designed for managing, training, and evaluating large language models (LLMs). This tool provides a means to optimize th...
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What is Entry Point AI?
Entry Point AI is the modern AI optimization platform for proprietary and open-source language models that enables users to manage prompts, fine-tune models, and evaluate their performance all from a single unified interface. The platform makes fine-tuning large language models fast and accessible to organizations of any size, removing the need for command lines, Python scripts, or custom API calls. When users reach the limits of what prompt engineering can achieve, Entry Point AI simplifies the transition to model fine-tuning by handling all the complexities involved.
Key features include import and management of structured data, prompt and completion templates with a templating engine for rapid iteration, cross-platform fine-tuning across multiple LLM providers (OpenAI, Replicate, Google AI), token counts and cost estimation, model validation with a playground, unlimited data synthesis for generating more training examples, CSV and JSONL exports, and the ability to deploy a frontend to fine-tuned models with a single click for testing. The platform also supports team collaboration with user seats, hyperparameter comparison, and performance evaluation tools.
Entry Point AI is designed for individuals working on personal projects, startups building AI-driven software features, businesses scaling high-volume applications, developers integrating AI into their applications, and teams needing to collaborate on training data and fine-tuning jobs. It serves customers across various use cases including content production, tagging and classification, data extraction, prioritization, recommendations, fraud detection, moderation, data enrichment, and scoring/ranking in RAG workflows.
Entry Point AI pricing
Pricing model: Paid
Start for free with free tier access. Paid monthly plans: Individuals Starter at $49/mo (5,000 training examples, 3 user seats), Startups Growth at $99/mo (25,000 training examples, 5 user seats), Businesses Pro at $249/mo (100,000 training examples, 10 user seats). All plans include import & manage structured data, prompt & completion templates, cross-platform fine-tuning, token counts & cost estimation, model validation & playground, unlimited data synthesis, onboarding call, and CSV/JSONL exports. Pro adds premium support. For higher limits or Enterprise plans, contact [email protected]. Custom quotes available for high volume requests.
Entry Point AI pros
- No-code AI training without command lines or Python scripts
- Cross-platform fine-tuning across OpenAI, Replicate, and Google AI
- Unlimited data synthesis for generating more training examples
- Token counts and cost estimation to avoid unexpected charges
- Model validation playground for testing performance
- Prompt and completion templating engine for rapid iteration
- Import and manage structured data easily from spreadsheets
- CSV and JSONL exports in syntax and structure of your choice
- Deploy frontend to fine-tuned model with single click for testing
- Team collaboration with multiple user seats across plans
- Compare hyperparameters to see what works best
- Start getting impressive results with as few as 50 examples
- Train lighter models to reduce latency and cost for simpler tasks
- More predictable outputs with controlled model behavior
- Handles nuances for different models from syntax to token limits
- Onboarding call included on all paid plans
- Premium support available on Pro plan
- Keep AI models after canceling - models become yours to keep
- Export data as JSONL or CSV at any time
Entry Point AI cons
- Additional costs on connected LLM platforms not included in plan price
- No free tier with training examples - only starter access
- Individual Starter plan limited to 5,000 training examples
- Maximum 10 user seats on Pro plan without enterprise contact
- Requires connecting to external LLM providers (OpenAI, Google, etc.)
- Fine-tuning and synthetic generation incur separate platform costs
- Enterprise plans require contacting sales for custom quotes
- High volume requests need direct contact for accommodation
Frequently asked questions about Entry Point AI
How many examples do I need to fine-tune a model?
You can start getting impressive results with as few as 50 examples. The more examples you add, the more accurate your models can become. Adding more examples helps optimize for edge cases or to allow a faster model to perform the task.
How does the training example limit work?
If your training example limit is 1,000, then you could create 1 project with 1,000 examples, 10 projects with 100 each, or anything in-between. When you delete a project or examples, it frees up your example limit to be used in another project.
Can I keep my AI models if I cancel the plan?
Yes! We train the model on the platform of your choice, such as OpenAI, and you can revoke our access anytime. These models become yours to keep through the platform of your choice. You can also export your data as JSONL or CSV at any time.
Are there other costs besides the plan price?
Fine-tuning models and generating synthetic examples will incur costs on the platform(s) you connect to Entry Point. These costs are not included, but we help estimate costs to avoid unexpected charges.
Do you offer volume or enterprise pricing?
We can accommodate high volume requests and custom quotes—please reach out to [email protected] so that we can learn more about your needs.
What LLM providers does Entry Point AI support?
Entry Point acts as a layer on top of language model providers including OpenAI, Replicate, and Google AI, enabling cross-platform fine-tuning through a unified interface so you don't get locked into a single API or model.
Is coding required to use Entry Point AI?
No code required. We implemented all the APIs from top LLM providers and built a user-interface to make them more accessible, with full access to underlying hyperparameters and key settings. Users can simply import a spreadsheet, design prompt and completion templates, and initiate training with one click.
What use cases can Entry Point AI help with?
Customers apply AI to solve real business problems including content production (reports, blog articles, social media, emails), tagging & classification, data extraction, prioritization (support issues, bug reports, leads), recommendations, fraud detection, moderation, data enrichment, and scoring & ranking in RAG workflows.
How does fine-tuning differ from prompt engineering?
Fine-tuning is showing a model how to behave, not telling it. It works together with prompt engineering and retrieval-augmented generation (RAG) to leverage the full potential from AI models. Fine-tuning can help get better quality from prompts like an upgrade to few-shot learning that bakes examples into the model itself.
Can I share and test my fine-tuned models?
Yes, you can deploy a frontend to your fine-tuned model with a single click and share it for testing. All the completions are saved so you can catch problems and augment your dataset.