LLM

Productionizing large language models (LLMs) made seamless and simple.

Table of contents

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The Unified Solution for Large Language Models

The challenge of data interaction often stems from the use of disjointed platforms and tools. Shakudo offers a unified solution to your data operations, featuring advanced methods for deploying large language models (LLMs), managing vector databases, and establishing robust data pipelines. We streamline the entire journey — from dividing your data into context-fitting chunks, running it through an embedding LLM, storing it in a vector database, to finally productionizing it. This centralized process enhances operational efficiency and minimizes the potential for errors, freeing you to focus on strategic objectives.

From Jupyter Notebooks to Production-Grade LLM Services

Moving from a local demo to a fully operational production-grade system is simplified with Shakudo's unified platform, which seamlessly carries your project from development notebooks to resilient, auto-scaling pipelines with real-time monitoring and automated orchestration. Key operational tasks like resource provisioning and security audits are automated, significantly reducing operational overhead. This ensures a secure, efficient, and reliable production environment.

Flexible Generative AI Operations

With Shakudo, you can easily switch between different generative AI models — open source or proprietary — without having to deal with complex system migrations or code changes. Our platform also enables you to select embeddings that align well with your specific data types. Additionally, we have scalable vector databases that can handle high-volume and blazing-fast queries, and update in real-time to reflect any changes in your data. Shakudo also supports data ingestion pipelines like Airbyte and Prefect, orchestration frameworks like LangChain, and flexible deployment options, including cloud and on-premise GPUs.

Production Ready Vector Databases

Managing vector databases in a production environment requires high-throughput, low-latency, and scalability. Shakudo addresses these complexities with performant database stack components and real-time resource allocation to optimize performance and cost. The platform offers full database control without the need for manual synchronization efforts. Its serverless architecture simplifies scaling, and auto-sync ensures data consistency with your Delta Lake. Shakudo’s stack components allow you to create an adaptable and unified data stack with no vendor lock-in. You can freely use the same codebase whether you continue with Shakudo or choose a different path, providing you with ultimate flexibility.

No Vendor Lock-in and 100+ Stack Components

Shakudo stands out by offering a broad array of 20+ specialized LLMOps tools. Whether it's LLMs like PALM 2, GPT-4, or FALCON LLM, or vector databases like PINECONE, VESPA, WEAVIATE, our platform brings together best-of-breed open source and commercial data and AI tools into a unified ecosystem. This enhances the functional richness of your existing tech stack, allowing you to manage complex operations through a single, consolidated interface.

Large Language Model (LLM)

Stack Components on Shakudo

LlamaIndex
Data framework for your LLM applications
Llama 2
Open source large language model developed by Meta
Cohere
Advanced Large Language Models and NLP tools via an easy-to-use API
Flan-T5
Enhanced T5 LLM model, finetuned on a mixture of tasks
FastChat-T5
Open-source, fine-tuned chatbot model
MPT-7B
Open-source LLM model for short-form instruction following
Falcon LLM
Open-source LLM with 40B parameters trained on 1T tokens
LangChain
Powering up applications with composable large language models
GPT-3
LLM developed by OpenAI
GPT-3.5
LLM by OpenAI improved on GPT-3
GPT-4
Top Multimodal LLM developed by OpenAI
flan-ul2
Encoder decoder model based on the T5 architecture by Google
Stanford Alpaca
Strong, Replicable Instruction-Following LLM from Standford

Database

Stack Components on Shakudo

Pinecone
General-purpose vector database used for multiple data types
Vespa
Real-time serving engine for AI-powered applications
Weaviate
AI-native vector database with semantic search
Redis
Fast in-memory database
pgvector
Vector similarity search for Postgres
Elasticsearch
Search & analytics engine, excelling in real-time, distributed search
Milvus
Vector database for AI search and analytics, excelling in similarity search.
Chroma
The fastest way to build Python or JavaScript LLM apps with memory
Shakudo brings the best data and AI products into your VPC and operates them for you automatically achieving a more reliable, performant, and cost effective data stack than ever before.
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