When ChatGPT is Not Enough

Shakudo's combination of a large language model (LLM) with a vector database steps in where ChatGPT, with its static knowledge base, falls short in providing real-time, tailored data solutions. This combination ensures not just personalized, up-to-the-minute information but also secures data within your own infrastructure, catering to the needs of the fast-paced world.

Building LLM + Vector DB

How to Do It on Shakudo

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Step 1

Choose Your Data Sources

Start by selecting where your data lives. Whether it's housed locally, in cloud storage, or scattered across different platforms, Shakudo brings it all together. Our integrations with tools like Airbyte make it simple — connect once, and you're ready to go.

Airbyte Data Sources

Step 2

Pick a Vector Database

Your data's next home is crucial. Opt for a vector database that suits your scale and speed requirements. With options like Milvus, Weaviate, and others at your fingertips in Shakudo's ecosystem, you'll find the perfect fit without the hassle of hopping between services.

Supported Vector Databases

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Step 3

Choose Your LLM

Deciding on the language and embedding model that best understands your data is key. With Shakudo, you can pick from a selection of top LLMs that are ready to deploy — straightforward choices without the technical runaround.

Supported LLMs

Step 4

Launch

It's go-time for your LLM + vector DB stack. Now you can engage with AI tailored to your domain expertise. Use the endpoints provided by Shakudo to integrate AI insights directly into your workflow or chat with your data with the built-in chatbot.

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Why on Shakudo

Simple, Fast, and Powerful

Shakudo offers the fastest way to self-serve the best-in-class LLM systems and vector databases. Set up and start using your chatbot in minutes.

Security and Production-Ready Endpoints

Shakudo keeps your data secure and sealed in your private zone — your data will not be used in any form. Scalable vector databases handle expansion of data and users.

Efficient Data Processing Pipeline and Tools for Real-Time Data Ingestion

Shakudo pipeline jobs use best-in-class data orchestration and ingestion tools to keep your vector databases refreshed.

More Than Just LLMs and Vector DBs

Growth-Ready Infrastructure

Scale

Ensures compatibility with expanding operations through Kubernetes-based scalability.
Maintains performance stability during high demand as your company grows.
Accommodates increasing workload volumes through flexible resource management.

Comprehensive Tooling

Complete

Integrates with over 116 pre-configured data stack components for diverse business needs.
Automates cloud infrastructure setup and maintenance, simplifying data scientists' workflow.
Delivers pre-built patterns for common ML tasks, streamlining development and deployment processes.

Streamlined Operations

Efficiency

Enables quick access to GPUs for faster ML model training.
Provides an intuitive single pane UI, making advanced data science tasks more accessible.
Facilitates seamless collaboration and consistent environments, cutting down on project timelines.

START FREE TRIAL

Start in our fully managed environment to experiment with Vector DBs and LLMs. Then transition to self-hosting anytime without changes to your data or code.

Get $300 worth of free usage credits when you subscribe

1

Usage Unit

≈

$0.01

of AWS/GCP/Azure spend

Dedicated instance of the Milvus Vector DB

Selection of the best LLM endpoints

Airbyte ingestion pipeline

Chat interface

Easily change or cancel your plan anytime through our Customer Portal.

Shakudo RAG Stack FAQs

Frequently Asked Questions

What is a RAG?

Retrieval-augmented generation (RAG) architectures are a popular way to address issues in LLMs, such as their inability to answer questions about fresh or private content (not in their training data) and data accuracy in generated response. Learn more about RAGs and their application in our blog post "Vector Databases: Build an Enterprise Knowledge Base with LLMs and Vector Stores."

How is cost determined for using Shakudo's RAG Stack?

Pricing is usage-based for the RAG stack, calculated from the compute, memory, GPU and storage consumption. For comprehensive access to the Shakudo data platform's full capabilities, please engage with our Sales team here.

What range of models are available on Shakudo?

Shakudo supports an extensive array of models, including top commercial options like those from OpenAI and Cohere, as well as a variety of open source alternatives. For a detailed list of supported models, visit our integrations page.

Are there any free offerings on Shakudo?

Yes! You get $300 worth of usage credits for the Shakudo platform when you sign up through this page.

Can Shakudo integrate with my existing data systems?

Yes, Shakudo is designed to be highly compatible with existing data systems. With its numerous pre-configured data stack components, it can easily be integrated into your current workflows to enhance data processing and machine learning capabilities.

How can I ensure my data remains secure while using Shakudo?

Security is a top priority at Shakudo. Your data belongs on your cloud — that’s why Shakudo can be easily deployed on any cloud or your on-prem infrastructure.

What kind of support can I expect from Shakudo?

Shakudo offers robust support options including a comprehensive knowledge base, 24/7/365 live support for critical issues, and dedicated account management to ensure you get the most out of the platform.

How quickly can I get started with deploying models on Shakudo?

You can get started almost immediately. Shakudo's user-friendly interface and extensive documentation mean that setting up and deploying your models can be done with minimal setup time.

Can Shakudo handle large-scale machine learning projects?

Yes, Shakudo is built for scale. Thanks to its underlying Kubernetes architecture and scalable components, it can handle large-scale machine learning projects with ease, providing the necessary resources as your project grows.

Production-Ready
LLM + Vector DB