Pricing plans

Flexible options to match your workflow and goals

$7 USD
Basic plan
Billed annually.

5,000 credits per month

  • 100 document store requests
  • 1,250 prompt requests
  • PDFs
  • Github repos
  • Websites
Get started
$7 USD
Basic plan
Billed annually.

5,000 credits per month

  • 100 document store requests
  • 1,250 prompt requests
  • PDFs
  • Github repos
  • Websites
  • Youtube videos
Get started
$71 USD
Startup plan
Billed annually.

100,000 credits per month

  • 2,000 document store requests
  • 25,000 prompt requests
  • PDFs
  • Github repos
  • Websites
Get started
$71 USD
Startup plan
Billed annually.

100,000 credits per month

  • 2,000 document store requests
  • 25,000 prompt requests
  • PDFs
  • Github repos
  • Websites
  • Youtube videos
Get started
$464 USD
Enterprise plan
Billed annually.

1,000,000 credits per month

  • 20,000 document store requests
  • 250,000 prompt requests
  • PDFs
  • Github repos
  • Websites
Get started
$464 USD
Enterprise plan
Billed annually.

1,000,000 credits per month

  • 20,000 document store requests
  • 250,000 prompt requests
  • PDFs
  • Github repos
  • Websites
  • Youtube videos
Get started

Frequently asked questions

Everything you need to know

What is Ragapi?
Ragapi is a super-simple API for setting up scalable Retrieval-Augmented Generation (RAG) pipelines. With just two API calls, you can store and query documents like PDFs, YouTube videos, GitHub repositories, and websites—all while ensuring your data stays private.
How does Ragapi protect my data?
Privacy is our top priority. Ragapi only processes your documents; nothing is stored. Your chat history and data are never saved or used to train models.
What kind of documents can I use with Ragapi?
You can store and query a wide variety of document types, including PDFs, YouTube videos, GitHub repositories, and websites. Just upload your data, and Ragapi takes care of the rest.
How scalable is Ragapi?
Extremely scalable! With Ragapi, you get a fully functional RAG pipeline ready to handle any scale, from small projects to enterprise-grade workloads—all with just two API calls.
What are some use cases for Ragapi?

Ragapi unlocks powerful AI capabilities for a variety of applications. Here are some examples to inspire you:

  • ChatGPT, but Focused Only on Your Data: Build a chatbot that provides accurate answers based solely on the documents you upload—perfect for internal knowledge bases or customer-specific AI solutions.
  • Smart Documentation Search: Enhance your app with intelligent search functionality that retrieves the most relevant answers from manuals, legal documents, or technical guides.
  • Personalized Learning Assistants: Create AI tutors that pull context-aware responses directly from textbooks, courses, or training material you provide.
  • Codebase Navigation: Allow developers to query massive codebases, like GitHub repositories, for quick and accurate answers about functions, classes, or dependencies.
  • Medical Records Query Tool: Develop an app that retrieves relevant medical information or insights from patient records, clinical studies, or drug references.
  • Academic Research Assistant: Help researchers quickly find answers or insights from research papers, journals, or study datasets.
  • Customer Support Automation: Build a chatbot that answers customer queries using your knowledge base, FAQs, or product manuals.

These are just a few examples—in the end, the possibilities are limitless. It’s all about your imagination!

Why do I need RAG? Why can’t I just pass everything to the LLM in a prompt?
  • Context Limitations: LLMs have a fixed context window, meaning they can only process a limited amount of information at once. With RAG, you can retrieve only the most relevant data, bypassing this limitation.
  • Cost Efficiency: Passing all your data in a prompt can be extremely expensive since LLMs charge per token (input and output). RAG minimizes the tokens sent to the LLM, saving you money.
  • Precision: Without RAG, there’s no guarantee the LLM will generate answers based solely on your documents. RAG ensures the output is grounded in your specific data.
Can I trust Ragapi with sensitive data?
Yes. Ragapi doesn’t store any of your documents or chat history, and it never trains models on your data. Your data is processed securely and stays completely private.
Why do I need to use my own OpenAI and Pinecone API keys?

Using your own API keys ensures you have full control over your data, privacy, and costs. This approach means:

  • Maximum Privacy: Your data is processed directly through your own accounts, so Ragapi never accesses or stores it.
  • Transparent Costs: You only pay for what you use with OpenAI and Pinecone, giving you better visibility and control over your expenses.
What is Pinecone, and why do I need it?

Pinecone is a vector database that stores and retrieves embeddings—AI-optimized numerical representations of your documents. It’s essential for creating efficient Retrieval-Augmented Generation (RAG) pipelines because:

  • Efficient Search: Pinecone enables fast and accurate retrieval of the most relevant information from large datasets.
  • Scalability: It seamlessly handles growing data sizes, keeping your AI applications performant.
  • Essential for RAG: Without Pinecone, you’d have to reprocess your documents for AI with every request, which would be painfully slow and inefficient. Pinecone ensures your documents are pre-optimized and ready for instant querying.

In short, Pinecone makes storing and retrieving your data efficient and practical—without it, scalable RAG pipelines wouldn’t be possible.

Still have questions?

Can’t find the answer you’re looking for? Please chat to our friendly team.

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