Best AI tools for< Manage Inference Endpoints >

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.

Wallaroo.AI is an AI inference platform that offers production-grade AI inference microservices optimized on OpenVINO for cloud and Edge AI application deployments on CPUs and GPUs. It provides hassle-free AI inferencing for any model, any hardware, anywhere, with ultrafast turnkey inference microservices. The platform enables users to deploy, manage, observe, and scale AI models effortlessly, reducing deployment costs and time-to-value significantly.

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.

NeuReality is an AI-centric solution designed to democratize AI adoption by providing purpose-built tools for deploying and scaling inference workflows. Their innovative AI-centric architecture combines hardware and software components to optimize performance and scalability. The platform offers a one-stop shop for AI inference, addressing barriers to AI adoption and streamlining computational processes. NeuReality's tools enable users to deploy, afford, use, and manage AI more efficiently, making AI easy and accessible for a wide range of applications.

Substratus.AI is a fully managed private LLMs platform that allows users to serve LLMs (Llama and Mistral) in their own cloud account. It enables users to keep control of their data while reducing OpenAI costs by up to 10x. With Substratus.AI, users can utilize LLMs in production in hours instead of weeks, making it a convenient and efficient solution for AI model deployment.

BuildAi is an AI tool designed to provide the lowest cost GPU cloud for AI training on the market. The platform is powered with renewable energy, enabling companies to train AI models at a significantly reduced cost. BuildAi offers interruptible pricing, short term reserved capacity, and high uptime pricing options. The application focuses on optimizing infrastructure for training and fine-tuning machine learning models, not inference, and aims to decrease the impact of computing on the planet. With features like data transfer support, SSH access, and monitoring tools, BuildAi offers a comprehensive solution for ML teams.

Cirrascale Cloud Services is an AI tool that offers cloud solutions for Artificial Intelligence applications. The platform provides a range of cloud services and products tailored for AI innovation, including NVIDIA GPU Cloud, AMD Instinct Series Cloud, Qualcomm Cloud, Graphcore, Cerebras, and SambaNova. Cirrascale's AI Innovation Cloud enables users to test and deploy on leading AI accelerators in one cloud, democratizing AI by delivering high-performance AI compute and scalable deep learning solutions. The platform also offers professional and managed services, tailored multi-GPU server options, and high-throughput storage and networking solutions to accelerate development, training, and inference workloads.

BentoML is a platform for software engineers to build, ship, and scale AI products. It provides a unified AI application framework that makes it easy to manage and version models, create service APIs, and build and run AI applications anywhere. BentoML is used by over 1000 organizations and has a global community of over 3000 members.

Salad is a distributed GPU cloud platform that offers fully managed and massively scalable services for AI applications. It provides the lowest priced AI transcription in the market, with features like image generation, voice AI, computer vision, data collection, and batch processing. Salad democratizes cloud computing by leveraging consumer GPUs to deliver cost-effective AI/ML inference at scale. The platform is trusted by hundreds of machine learning and data science teams for its affordability, scalability, and ease of deployment.

TitanML is a platform that provides tools and services for deploying and scaling Generative AI applications. Their flagship product, the Titan Takeoff Inference Server, helps machine learning engineers build, deploy, and run Generative AI models in secure environments. TitanML's platform is designed to make it easy for businesses to adopt and use Generative AI, without having to worry about the underlying infrastructure. With TitanML, businesses can focus on building great products and solving real business problems.

Loops is an AI tool that empowers data analysts and product managers to make informed decisions based on deep, accurate causal insights. It leverages proprietary causal inference models to identify opportunities for maximizing key performance indicators (KPIs) without the need for traditional A/B testing. By analyzing user behaviors and business metrics, Loops helps companies prioritize efforts efficiently and proactively review impactful opportunities. The tool simplifies the process of understanding causality, providing actionable insights for product teams to drive growth and increase KPIs.

Segwise is an AI tool designed to help game developers increase their game's Lifetime Value (LTV) by providing insights into player behavior and metrics. The tool uses AI agents to detect causal LTV drivers, root causes of LTV drops, and opportunities for growth. Segwise offers features such as running causal inference models on player data, hyper-segmenting player data, and providing instant answers to questions about LTV metrics. It also promises seamless integrations with gaming data sources and warehouses, ensuring data ownership and transparent pricing. The tool aims to simplify the process of improving LTV for game developers.

Anote is a human-centered AI company that provides a suite of products and services to help businesses improve their data quality and build better AI models. Anote's products include a data labeler, a private chatbot, a model inference API, and a lead generation tool. Anote's services include data annotation, model training, and consulting.

AIMLAPI.com is an AI tool that provides access to over 200 AI models through a single AI API. It offers a wide range of AI features for tasks such as chat, code, image generation, music generation, video, voice embedding, language, genomic models, and 3D generation. The platform ensures fast inference, top-tier serverless infrastructure, high data security, 99% uptime, and 24/7 support. Users can integrate AI features easily into their products and test API models in a sandbox environment before deployment.

Tavus is an AI tool that offers digital twin APIs for video generation and conversational video interfaces. It allows users to create immersive AI-generated video experiences using cutting-edge AI technology. Tavus provides best-in-class models like Phoenix-2 for creating realistic digital replicas with natural face movements. The platform offers rapid training, instant inference, support for 30+ languages, and built-in security features to ensure user privacy and safety. Tavus is preferred by developers and product teams for its developer-first approach, ease of integration, and exceptional customer service.

Tavus is an AI tool that offers digital twin APIs for video generation and conversational video interfaces. It provides developers with cutting-edge AI technology to create immersive video experiences using AI-generated digital twins. Tavus' Phoenix model enables the generation of realistic digital replicas with natural face movements and expressions. The platform also supports rapid training, instant inference, and multi-language capabilities. With a developer-first approach, Tavus focuses on security, trust, and user experience, offering features like dubbing APIs and automated content moderation. The tool is praised for its speed of development cycles, high-quality AI video, and exceptional customer service.

Impulze.ai is an Influencer Analytics Platform designed to help users discover and manage influencers from a vast global database covering Instagram, TikTok, and YouTube. The platform offers AI algorithms that effectively identify the right influencers, allowing users to effortlessly filter influencers based on various criteria such as engagement rate, follower count, location, age, and gender. Additionally, Impulze.ai provides audience analytics, fraud detection, and data-driven matching to help users find the perfect match for their brand. With features like real-time performance metrics, audience filters, and campaign tracking, Impulze.ai streamlines influencer marketing strategies for agencies and marketers, saving time and enhancing brand awareness.

Kofluence is a disruptive AI-powered Ad-Tech influencer marketing platform that empowers both brands and influencers to capitalize on the value of their social influence. With features like data-driven performance, matchmaking through AI, end-to-end campaign management, real-time performance metrics, and integration with marketing tools, Kofluence offers a comprehensive solution for effective influencer marketing campaigns. The platform boasts a vast network of over 650,000 creators, deep profiling capabilities, AI/ML algorithms, and proprietary risk and fraud detection mechanisms to ensure successful and optimized campaigns.

vHub.ai is an AI-powered Influencer Marketing SaaS Platform that offers a comprehensive suite of tools to streamline influencer marketing campaigns. With features like in-depth influencer insights, authenticity assurance, instant campaign analytics, effortless influencer coordination, and tailor-made campaign strategies, vHub.ai aims to revolutionize the way brands collaborate with influencers. The platform boasts a database of 5 million influencers and 100's of influencer marketing agencies, providing users with the perfect influencer at optimal pricing. By leveraging AI search engine capabilities and VQS (vHub Quality Score), vHub.ai ensures genuine connections and successful influencer partnerships. Users can track campaign performance in real-time, manage all types of campaigns, and customize strategies with precision. With a focus on data-driven influencer discovery, authentic campaigns, custom success campaigns, ROI-driven marketing, and streamlined campaign management, vHub.ai empowers brands to maximize their influencer marketing ROI and reach.

Klones is an AI application that allows content creators and influencers to create a digital clone of themselves using generative AI technology. This AI clone interacts with fans, replicating the creator's style and tone to manage fan engagement and generate income. The platform prioritizes data privacy and user safety, ensuring that influencer data is securely stored and used exclusively to train the AI clone. Klones revolutionizes the way creators monetize their brand and strengthen fan relationships, enabling them to focus on content creation while their AI clone engages with fans.

llm-swarm is a tool designed to manage scalable open LLM inference endpoints in Slurm clusters. It allows users to generate synthetic datasets for pretraining or fine-tuning using local LLMs or Inference Endpoints on the Hugging Face Hub. The tool integrates with huggingface/text-generation-inference and vLLM to generate text at scale. It manages inference endpoint lifetime by automatically spinning up instances via `sbatch`, checking if they are created or connected, performing the generation job, and auto-terminating the inference endpoints to prevent idling. Additionally, it provides load balancing between multiple endpoints using a simple nginx docker for scalability. Users can create slurm files based on default configurations and inspect logs for further analysis. For users without a Slurm cluster, hosted inference endpoints are available for testing with usage limits based on registration status.

This NVIDIA AI Workbench project is designed for developing a Retrieval Augmented Generation application with a customizable Gradio Chat app. It allows users to embed documents into a locally running vector database and run inference locally on a Hugging Face TGI server, in the cloud using NVIDIA inference endpoints, or using microservices via NVIDIA Inference Microservices (NIMs). The project supports various models with different quantization options and provides tutorials for using different inference modes. Users can troubleshoot issues, customize the Gradio app, and access advanced tutorials for specific tasks.

SAP Cloud SDK for AI is the official Software Development Kit (SDK) for SAP AI Core, SAP Generative AI Hub, and Orchestration Service. It allows users to integrate chat completion into business applications, leverage generative AI capabilities for templating, grounding, data masking, and content filtering. The SDK provides tools for managing scenarios, workflows, data preprocessing, model training pipelines, batch inference jobs, deploying inference endpoints, and orchestrating AI activities. Users can set up their SAP AI Core instance using the SDK, which includes packages for AI API, foundation models, LangChain model clients, and orchestration capabilities. The SDK also offers a sample project for demonstrating its usage in TypeScript/JavaScript applications, along with guidelines for local testing and contribution.

Embodied Agents is a toolkit for integrating large multi-modal models into existing robot stacks with just a few lines of code. It provides consistency, reliability, scalability, and is configurable to any observation and action space. The toolkit is designed to reduce complexities involved in setting up inference endpoints, converting between different model formats, and collecting/storing datasets. It aims to facilitate data collection and sharing among roboticists by providing Python-first abstractions that are modular, extensible, and applicable to a wide range of tasks. The toolkit supports asynchronous and remote thread-safe agent execution for maximal responsiveness and scalability, and is compatible with various APIs like HuggingFace Spaces, Datasets, Gymnasium Spaces, Ollama, and OpenAI. It also offers automatic dataset recording and optional uploads to the HuggingFace hub.

The main goal of llama.cpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware - locally and in the cloud. It provides a Plain C/C++ implementation without any dependencies, optimized for Apple silicon via ARM NEON, Accelerate and Metal frameworks, and supports various architectures like AVX, AVX2, AVX512, and AMX. It offers integer quantization for faster inference, custom CUDA kernels for NVIDIA GPUs, Vulkan and SYCL backend support, and CPU+GPU hybrid inference. llama.cpp is the main playground for developing new features for the ggml library, supporting various models and providing tools and infrastructure for LLM deployment.

Log10 is a one-line Python integration to manage your LLM data. It helps you log both closed and open-source LLM calls, compare and identify the best models and prompts, store feedback for fine-tuning, collect performance metrics such as latency and usage, and perform analytics and monitor compliance for LLM powered applications. Log10 offers various integration methods, including a python LLM library wrapper, the Log10 LLM abstraction, and callbacks, to facilitate its use in both existing production environments and new projects. Pick the one that works best for you. Log10 also provides a copilot that can help you with suggestions on how to optimize your prompt, and a feedback feature that allows you to add feedback to your completions. Additionally, Log10 provides prompt provenance, session tracking and call stack functionality to help debug prompt chains. With Log10, you can use your data and feedback from users to fine-tune custom models with RLHF, and build and deploy more reliable, accurate and efficient self-hosted models. Log10 also supports collaboration, allowing you to create flexible groups to share and collaborate over all of the above features.

aiaio (AI-AI-O) is a lightweight, privacy-focused web UI for interacting with AI models. It supports both local and remote LLM deployments through OpenAI-compatible APIs. The tool provides features such as dark/light mode support, local SQLite database for conversation storage, file upload and processing, configurable model parameters through UI, privacy-focused design, responsive design for mobile/desktop, syntax highlighting for code blocks, real-time conversation updates, automatic conversation summarization, customizable system prompts, WebSocket support for real-time updates, Docker support for deployment, multiple API endpoint support, and multiple system prompt support. Users can configure model parameters and API settings through the UI, handle file uploads, manage conversations, and use keyboard shortcuts for efficient interaction. The tool uses SQLite for storage with tables for conversations, messages, attachments, and settings. Contributions to the project are welcome under the Apache License 2.0.

llama.cpp is a C++ implementation of LLaMA, a large language model from Meta. It provides a command-line interface for inference and can be used for a variety of tasks, including text generation, translation, and question answering. llama.cpp is highly optimized for performance and can be run on a variety of hardware, including CPUs, GPUs, and TPUs.

Clipboard Conqueror is a multi-platform omnipresent copilot alternative. Currently requiring a kobold united or openAI compatible back end, this software brings powerful LLM based tools to any text field, the universal copilot you deserve. It simply works anywhere. No need to sign in, no required key. Provided you are using local AI, CC is a data secure alternative integration provided you trust whatever backend you use. *Special thank you to the creators of KoboldAi, KoboldCPP, llamma, openAi, and the communities that made all this possible to figure out.

This repository focuses on organizing resources for building synthetic datasets using large language models. It covers important datasets, libraries, tools, tutorials, and papers related to synthetic data generation. The goal is to provide pragmatic and practical resources for individuals interested in creating synthetic datasets for machine learning applications.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

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.

LLM App Stack, also known as Emerging Architectures for LLM Applications, is a comprehensive list of available tools, projects, and vendors at each layer of the LLM app stack. It covers various categories such as Data Pipelines, Embedding Models, Vector Databases, Playgrounds, Orchestrators, APIs/Plugins, LLM Caches, Logging/Monitoring/Eval, Validators, LLM APIs (proprietary and open source), App Hosting Platforms, Cloud Providers, and Opinionated Clouds. The repository aims to provide a detailed overview of tools and projects for building, deploying, and maintaining enterprise data solutions, AI models, and applications.

Edgen is a local GenAI API server that serves as a drop-in replacement for OpenAI's API. It provides multi-endpoint support for chat completions and speech-to-text, is model agnostic, offers optimized inference, and features model caching. Built in Rust, Edgen is natively compiled for Windows, MacOS, and Linux, eliminating the need for Docker. It allows users to utilize GenAI locally on their devices for free and with data privacy. With features like session caching, GPU support, and support for various endpoints, Edgen offers a scalable, reliable, and cost-effective solution for running GenAI applications locally.

OpenLLM is a platform that helps developers run any open-source Large Language Models (LLMs) as OpenAI-compatible API endpoints, locally and in the cloud. It supports a wide range of LLMs, provides state-of-the-art serving and inference performance, and simplifies cloud deployment via BentoML. Users can fine-tune, serve, deploy, and monitor any LLMs with ease using OpenLLM. The platform also supports various quantization techniques, serving fine-tuning layers, and multiple runtime implementations. OpenLLM seamlessly integrates with other tools like OpenAI Compatible Endpoints, LlamaIndex, LangChain, and Transformers Agents. It offers deployment options through Docker containers, BentoCloud, and provides a community for collaboration and contributions.

MCP-Bridge is a middleware tool designed to provide an openAI compatible endpoint for calling MCP tools. It acts as a bridge between the OpenAI API and MCP tools, allowing developers to leverage MCP tools through the OpenAI API interface. The tool facilitates the integration of MCP tools with the OpenAI API by providing endpoints for interaction. It supports non-streaming and streaming chat completions with MCP, as well as non-streaming completions without MCP. The tool is designed to work with inference engines that support tool call functionalities, such as vLLM and ollama. Installation can be done using Docker or manually, and the application can be run to interact with the OpenAI API. Configuration involves editing the config.json file to add new MCP servers. Contributions to the tool are welcome under the MIT License.

A Rust based tool to evaluate LLM models, prompts and model params. It automates the process of selecting the best model parameters, given an LLM model and a prompt, iterating over the possible combinations and letting the user visually inspect the results. The tool assumes the user has Ollama installed and serving endpoints, either in `localhost` or in a remote server. Key features include: * Automatically fetches models from local or remote Ollama servers * Iterates over different models and params to generate inferences * A/B test prompts on different models simultaneously * Allows multiple iterations for each combination of parameters * Makes synchronous inference calls to avoid spamming servers * Optionally outputs inference parameters and response metadata (inference time, tokens and tokens/s) * Refetching of individual inference calls * Model selection can be filtered by name * List experiments which can be downloaded in JSON format * Configurable inference timeout * Custom default parameters and system prompts can be defined in settings

BentoVLLM is an example project demonstrating how to serve and deploy open-source Large Language Models using vLLM, a high-throughput and memory-efficient inference engine. It provides a basis for advanced code customization, such as custom models, inference logic, or vLLM options. The project allows for simple LLM hosting with OpenAI compatible endpoints without the need to write any code. Users can interact with the server using Swagger UI or other methods, and the service can be deployed to BentoCloud for better management and scalability. Additionally, the repository includes integration examples for different LLM models and tools.

vnc-lm is a Discord bot designed for messaging with language models. Users can configure model parameters, branch conversations, and edit prompts to enhance responses. The bot supports various providers like OpenAI, Huggingface, and Cloudflare Workers AI. It integrates with ollama and LiteLLM, allowing users to access a wide range of language model APIs through a single interface. Users can manage models, switch between models, split long messages, and create conversation branches. LiteLLM integration enables support for OpenAI-compatible APIs and local LLM services. The bot requires Docker for installation and can be configured through environment variables. Troubleshooting tips are provided for common issues like context window problems, Discord API errors, and LiteLLM issues.

RayLLM (formerly known as Aviary) is an LLM serving solution that makes it easy to deploy and manage a variety of open source LLMs, built on Ray Serve. It provides an extensive suite of pre-configured open source LLMs, with defaults that work out of the box. RayLLM supports Transformer models hosted on Hugging Face Hub or present on local disk. It simplifies the deployment of multiple LLMs, the addition of new LLMs, and offers unique autoscaling support, including scale-to-zero. RayLLM fully supports multi-GPU & multi-node model deployments and offers high performance features like continuous batching, quantization and streaming. It provides a REST API that is similar to OpenAI's to make it easy to migrate and cross test them. RayLLM supports multiple LLM backends out of the box, including vLLM and TensorRT-LLM.

Demystifying Experimentation and Causal Inference with 1-Sided Tests Focus

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