Best AI tools for< Deploy Serverless Functions >

Modal is a high-performance cloud platform designed for developers, AI data, and ML teams. It offers a serverless environment for running generative AI models, large-scale batch jobs, job queues, and more. With Modal, users can bring their own code and leverage the platform's optimized container file system for fast cold boots and seamless autoscaling. The platform is engineered for large-scale workloads, allowing users to scale to hundreds of GPUs, pay only for what they use, and deploy functions to the cloud in seconds without the need for YAML or Dockerfiles. Modal also provides features for job scheduling, web endpoints, observability, and security compliance.

Amazon Bedrock is a cloud-based platform that enables developers to build, deploy, and manage serverless applications. It provides a fully managed environment that takes care of the infrastructure and operations, so developers can focus on writing code. Bedrock also offers a variety of tools and services to help developers build and deploy their applications, including a code editor, a debugger, and a deployment pipeline.

Mystic.ai is an AI tool designed to deploy and scale Machine Learning models with ease. It offers a fully managed Kubernetes platform that runs in your own cloud, allowing users to deploy ML models in their own Azure/AWS/GCP account or in a shared GPU cluster. Mystic.ai provides cost optimizations, fast inference, simpler developer experience, and performance optimizations to ensure high-performance AI model serving. With features like pay-as-you-go API, cloud integration with AWS/Azure/GCP, and a beautiful dashboard, Mystic.ai simplifies the deployment and management of ML models for data scientists and AI engineers.

Azure Static Web Apps is a platform provided by Microsoft Azure for building and deploying modern web applications. It allows developers to easily host static web content and serverless APIs with seamless integration to popular frameworks like React, Angular, and Vue. With Azure Static Web Apps, developers can quickly set up continuous integration and deployment workflows, enabling them to focus on building great user experiences without worrying about infrastructure management.

Empower is a serverless fine-tuned LLM hosting platform that offers a developer platform for fine-tuned LLMs. It provides prebuilt task-specific base models with GPT4 level response quality, enabling users to save up to 80% on LLM bills with just 5 lines of code change. Empower allows users to own their models, offers cost-effective serving with no compromise on performance, and charges on a per-token basis. The platform is designed to be user-friendly, efficient, and cost-effective for deploying and serving fine-tuned LLMs.

Cerebium is a serverless AI infrastructure platform that allows teams to build, test, and deploy AI applications quickly and efficiently. With a focus on speed, performance, and cost optimization, Cerebium offers a range of features and tools to simplify the development and deployment of AI projects. The platform ensures high reliability, security, and compliance while providing real-time logging, cost tracking, and observability tools. Cerebium also offers GPU variety and effortless autoscaling to meet the diverse needs of developers and businesses.

Fleak AI Workflows is a low-code serverless API Builder designed for data teams to effortlessly integrate, consolidate, and scale their data workflows. It simplifies the process of creating, connecting, and deploying workflows in minutes, offering intuitive tools to handle data transformations and integrate AI models seamlessly. Fleak enables users to publish, manage, and monitor APIs effortlessly, without the need for infrastructure requirements. It supports various data types like JSON, SQL, CSV, and Plain Text, and allows integration with large language models, databases, and modern storage technologies.

Anyscale is a company that provides a scalable compute platform for AI and Python applications. Their platform includes a serverless API for serving and fine-tuning open LLMs, a private cloud solution for data privacy and governance, and an open source framework for training, batch, and real-time workloads. Anyscale's platform is used by companies such as OpenAI, Uber, and Spotify to power their AI workloads.

Novita AI is an AI cloud platform that offers Model APIs, Serverless, and GPU Instance solutions integrated into one cost-effective platform. It provides tools for building AI products, scaling with serverless architecture, and deploying with GPU instances. Novita AI caters to startups and businesses looking to leverage AI technologies without the need for extensive machine learning expertise. The platform also offers a Startup Program, 24/7 service support, and has received positive feedback for its reasonable pricing and stable API services.

Novita AI is an AI cloud platform offering Model APIs, Serverless, and GPU Instance services in a cost-effective and integrated manner to accelerate AI businesses. It provides optimized models for high-quality dialogue use cases, full spectrum AI APIs for image, video, audio, and LLM applications, serverless auto-scaling based on demand, and customizable GPU solutions for complex AI tasks. The platform also includes a Startup Program, 24/7 service support, and has received positive feedback for its reasonable pricing and stable services.

Eventual is a platform that simplifies the process of building and operating resilient event-driven applications. It offers code-first APIs, Events, and Workflows to create durable, scalable, and event-driven systems with end-to-end type safety. The platform supports composable microservices that are fully serverless, evolve naturally, and have minimal operational complexity. Eventual runs in your cloud environment, adhering to your security and privacy policies, and integrates with your preferred Infrastructure as Code (IaC) framework.

Apify is a full-stack web scraping and data extraction platform that allows developers to build, deploy, and publish web scraping, data extraction, and web automation tools. The platform offers ready-made web scraping tools, serverless programs, integrations with various apps and services, storage for results, anti-blocking proxies, and an open-source web scraping and crawling library. Apify simplifies web scraping challenges by providing a full-stack platform that includes tools for developing, deploying, and running web scrapers efficiently.

Seldon is an MLOps platform that helps enterprises deploy, monitor, and manage machine learning models at scale. It provides a range of features to help organizations accelerate model deployment, optimize infrastructure resource allocation, and manage models and risk. Seldon is trusted by the world's leading MLOps teams and has been used to install and manage over 10 million ML models. With Seldon, organizations can reduce deployment time from months to minutes, increase efficiency, and reduce infrastructure and cloud costs.

PoplarML is a platform that enables the deployment of production-ready, scalable ML systems with minimal engineering effort. It offers one-click deploys, real-time inference, and framework agnostic support. With PoplarML, users can seamlessly deploy ML models using a CLI tool to a fleet of GPUs and invoke their models through a REST API endpoint. The platform supports Tensorflow, Pytorch, and JAX models.

Hanabi.rest is an AI-based API building platform that allows users to create REST APIs from natural language and screenshots using AI technology. Users can deploy the APIs on Cloudflare Workers and roll them out globally. The platform offers a live editor for testing database access and API endpoints, generates code compatible with various runtimes, and provides features like sharing APIs via URL, npm package integration, and CLI dump functionality. Hanabi.rest simplifies API design and deployment by leveraging natural language processing, image recognition, and v0.dev components.

Superflows is a tool that allows you to add an AI Copilot to your SaaS product. This AI Copilot can answer questions and perform tasks for users via chat. It is designed to be easy to set up and configure, and it can be integrated into your codebase with just a few lines of code. Superflows is a great way to improve the user experience of your SaaS product and help users get the most out of your software.

Outfit AI is an AI tool that enables users to design and deploy AI models or workflows as user-ready applications in minutes. It allows users to create custom user interfaces for their AI-powered apps by dropping in an API key from Replicate or Hugging Face. With Outfit AI, users can have creative control over the design of their apps, build complex workflows without any code, and optimize prompts for better performance. The tool aims to help users launch their models faster, save time, and enhance their AI applications with a built-in product copilot.

IBM Watsonx is an enterprise studio for AI builders. It provides a platform to train, validate, tune, and deploy AI models quickly and efficiently. With Watsonx, users can access a library of pre-trained AI models, build their own models, and deploy them to the cloud or on-premises. Watsonx also offers a range of tools and services to help users manage and monitor their AI models.

Lazy AI is a platform that enables users to build full stack web applications 10 times faster by utilizing AI technology. Users can create and modify web apps with prompts and deploy them to the cloud with just one click. The platform offers a variety of features including AI Component Builder, eCommerce store creation, Crypto Arbitrage Scraper, Text to Speech Converter, Lazy Image to Video generation, PDF Chatbot, and more. Lazy AI aims to streamline the app development process and empower users to leverage AI for various tasks.

PixieBrix is an AI engagement platform that allows users to build, deploy, and manage internal AI tools to drive team productivity. It unifies AI landscapes with oversight and governance for enterprise scale. The platform is enterprise-ready and fully customizable to meet unique needs, and can be deployed on any site, making it easy to integrate into existing systems. PixieBrix leverages the power of AI and automation to harness the latest technology to streamline workflows and take productivity to new heights.

General Framework is a cross-platform library designed to help create apps with a unified codebase using Flutter. It offers features such as cross-platform support, standardized style code, a CLI for easier usage, API integration for bot development, customizable extensions for faster development, and user-friendly information. The library is intended to streamline the app, server, bot, and userbot creation process by providing a comprehensive set of tools and functionalities.

YoMo is an open-source LLM Function Calling Framework for building Geo-distributed AI applications. It is built atop QUIC Transport Protocol and Stateful Serverless architecture, making AI applications low-latency, reliable, secure, and easy. The framework focuses on providing low-latency, secure, stateful serverless functions that can be distributed geographically to bring AI inference closer to end users. It offers features such as low-latency communication, security with TLS v1.3, stateful serverless functions for faster GPU processing, geo-distributed architecture, and a faster-than-real-time codec called Y3. YoMo enables developers to create and deploy stateful serverless functions for AI inference in a distributed manner, ensuring quick responses to user queries from various locations worldwide.

The serverless-pdf-chat repository contains a sample application that allows users to ask natural language questions of any PDF document they upload. It leverages serverless services like Amazon Bedrock, AWS Lambda, and Amazon DynamoDB to provide text generation and analysis capabilities. The application architecture involves uploading a PDF document to an S3 bucket, extracting metadata, converting text to vectors, and using a LangChain to search for information related to user prompts. The application is not intended for production use and serves as a demonstration and educational tool.

ChatBook provides a one-stop AI service, including basic AI dialogue, AI role agents, AI customer service, AI knowledge base, AI mind map generation, and AI PPTX generation. Users can define AI workflows freely to handle more complex business scenarios. The backend uses serverless functions with data stored in the ./data directory. The tool allows administrators to manage knowledge bases, configure keys, and review user registrations. Normal users can directly use AI models and knowledge bases after registration. The technology stack includes LLM models like Langchain, Pinecone, OpenAi, Gemini, Baidu Wenxin, Node Express for backend, and React, NextJS, MUI for frontend.

Learn Cloud Applied Generative AI Engineering (GenEng) is a course focusing on the application of generative AI technologies in various industries. The course covers topics such as the economic impact of generative AI, the role of developers in adopting and integrating generative AI technologies, and the future trends in generative AI. Students will learn about tools like OpenAI API, LangChain, and Pinecone, and how to build and deploy Large Language Models (LLMs) for different applications. The course also explores the convergence of generative AI with Web 3.0 and its potential implications for decentralized intelligence.

Airbroke is an open-source error catcher tool designed for modern web applications. It provides a PostgreSQL-based backend with an Airbrake-compatible HTTP collector endpoint and a React-based frontend for error management. The tool focuses on simplicity, maintaining a small database footprint even under heavy data ingestion. Users can ask AI about issues, replay HTTP exceptions, and save/manage bookmarks for important occurrences. Airbroke supports multiple OAuth providers for secure user authentication and offers occurrence charts for better insights into error occurrences. The tool can be deployed in various ways, including building from source, using Docker images, deploying on Vercel, Render.com, Kubernetes with Helm, or Docker Compose. It requires Node.js, PostgreSQL, and specific system resources for deployment.

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AWS AI Stack is a full-stack boilerplate project designed for building serverless AI applications on AWS. It provides a trusted AWS foundation for AI apps with access to powerful LLM models via Bedrock. The architecture is serverless, ensuring cost-efficiency by only paying for usage. The project includes features like AI Chat & Streaming Responses, Multiple AI Models & Data Privacy, Custom Domain Names, API & Event-Driven architecture, Built-In Authentication, Multi-Environment support, and CI/CD with Github Actions. Users can easily create AI Chat bots, authentication services, business logic, and async workers using AWS Lambda, API Gateway, DynamoDB, and EventBridge.

llm-finetuning is a repository that provides a serverless twist to the popular axolotl fine-tuning library using Modal's serverless infrastructure. It allows users to quickly fine-tune any LLM model with state-of-the-art optimizations like Deepspeed ZeRO, LoRA adapters, Flash attention, and Gradient checkpointing. The repository simplifies the fine-tuning process by not exposing all CLI arguments, instead allowing users to specify options in a config file. It supports efficient training and scaling across multiple GPUs, making it suitable for production-ready fine-tuning jobs.

Beta9 is an open-source platform for running scalable serverless GPU workloads across cloud providers. It allows users to scale out workloads to thousands of GPU or CPU containers, achieve ultrafast cold-start for custom ML models, automatically scale to zero to pay for only what is used, utilize flexible distributed storage, distribute workloads across multiple cloud providers, and easily deploy task queues and functions using simple Python abstractions. The platform is designed for launching remote serverless containers quickly, featuring a custom, lazy loading image format backed by S3/FUSE, a fast redis-based container scheduling engine, content-addressed storage for caching images and files, and a custom runc container runtime.

This sample shows how to build a serverless chat experience with Retrieval-Augmented Generation using LangChain.js and Azure. The application is hosted on Azure Static Web Apps and Azure Functions, with Azure Cosmos DB for MongoDB vCore as the vector database. You can use it as a starting point for building more complex AI applications.

This repository showcases how to deploy generative AI models from Amazon SageMaker JumpStart using the AWS CDK. Generative AI is a type of AI that can create new content and ideas, such as conversations, stories, images, videos, and music. The repository provides a detailed guide on deploying image and text generative AI models, utilizing pre-trained models from SageMaker JumpStart. The web application is built on Streamlit and hosted on Amazon ECS with Fargate. It interacts with the SageMaker model endpoints through Lambda functions and Amazon API Gateway. The repository also includes instructions on setting up the AWS CDK application, deploying the stacks, using the models, and viewing the deployed resources on the AWS Management Console.

Functionary is a language model that interprets and executes functions/plugins. It determines when to execute functions, whether in parallel or serially, and understands their outputs. Function definitions are given as JSON Schema Objects, similar to OpenAI GPT function calls. It offers documentation and examples on functionary.meetkai.com. The newest model, meetkai/functionary-medium-v3.1, is ranked 2nd in the Berkeley Function-Calling Leaderboard. Functionary supports models with different context lengths and capabilities for function calling and code interpretation. It also provides grammar sampling for accurate function and parameter names. Users can deploy Functionary models serverlessly using Modal.com.

Cube Studio is an open-source all-in-one cloud-native machine learning platform that provides various functionalities such as project group management, network configuration, user management, role management, billing functions, SSO single sign-on, support for multiple computing power types, support for multiple resource groups and clusters, edge cluster support, serverless cluster mode support, database storage support, machine resource management, storage disk management, internationalization capabilities, data map management, data calculation, ETL orchestration, data set management, data annotation, image/audio/text dataset support, feature processing, traditional machine learning algorithms, distributed deep learning frameworks, distributed acceleration frameworks, model evaluation, model format conversion, model registration, model deployment, distributed media processing, custom operators, automatic learning, custom training images, automatic parameter tuning, TensorBoard jobs, internal services, model management, inference services, monitoring, model application management, model marketplace, model development, model fine-tuning, web model deployment, automated annotation, dataset SDK, notebook SDK, pipeline training SDK, inference service SDK, large model distributed training, large model inference, large model fine-tuning, intelligent conversation, private knowledge base, model deployment for WeChat public accounts, enterprise WeChat group chatbot integration, DingTalk group chatbot integration, and more. Cube Studio offers template-based functionality for data import/export, data processing, feature processing, machine learning frameworks, machine learning algorithms, deep learning frameworks, model processing, model serving, monitoring, and more.

This project provides a proof of concept for deploying a scalable, serverless LLM Generative AI inference engine on AWS Lambda. It leverages the llama.cpp project to enable the usage of more accessible CPU and RAM configurations instead of limited and expensive GPU capabilities. By deploying a container with the llama.cpp converted models onto AWS Lambda, this project offers the advantages of scale, minimizing cost, and maximizing compute availability. The project includes AWS CDK code to create and deploy a Lambda function leveraging your model of choice, with a FastAPI frontend accessible from a Lambda URL. It is important to note that you will need ggml quantized versions of your model and model sizes under 6GB, as your inference RAM requirements cannot exceed 9GB or your Lambda function will fail.

LangServe helps developers deploy `LangChain` runnables and chains as a REST API. This library is integrated with FastAPI and uses pydantic for data validation. In addition, it provides a client that can be used to call into runnables deployed on a server. A JavaScript client is available in LangChain.js.

Pluto is a development tool dedicated to helping developers **build cloud and AI applications more conveniently** , resolving issues such as the challenging deployment of AI applications and open-source models. Developers are able to write applications in familiar programming languages like **Python and TypeScript** , **directly defining and utilizing the cloud resources necessary for the application within their code base** , such as AWS SageMaker, DynamoDB, and more. Pluto automatically deduces the infrastructure resource needs of the app through **static program analysis** and proceeds to create these resources on the specified cloud platform, **simplifying the resources creation and application deployment process**.

AI Town is a virtual town where AI characters live, chat, and socialize. This project provides a deployable starter kit for building and customizing your own version of AI Town. It features a game engine, database, vector search, auth, text model, deployment, pixel art generation, background music generation, and local inference. You can customize your own simulation by creating characters and stories, updating spritesheets, changing the background, and modifying the background music.

AI DIAL Core is an HTTP Proxy that provides a unified API to different chat completion and embedding models, assistants, and applications. It is written in Java 17 and built on Eclipse Vert.x. The core functionality includes handling static and dynamic settings, deployment on Kubernetes using Helm charts, and storing user data in Blob Storage and Redis. It supports various identity providers, storage providers like AWS S3, Google Cloud Storage, and Azure Blob Store, and features like AI DIAL Addons, Interceptors, Assistants, Applications, and Models with customizable parameters and configurations.

Gemini-OpenAI-Proxy is a proxy software designed to convert OpenAI API protocol calls into Google Gemini Pro protocol, allowing software using OpenAI protocol to utilize Gemini Pro models seamlessly. It provides an easy integration of Gemini Pro's powerful features without the need for complex development work.

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