Together AI is a cloud-based platform specializing in generative AI technologies, providing the fastest cloud services for AI model inference, fine-tuning, and training. They allow customers to build and train generative AI models using private data, offering dedicated GPU clusters for training with custom models. Leveraging leading-edge AI research and breakthroughs like Cocktail SGD, FlashAttention 2, and sub-quadratic model architectures, Together AI enables the efficient creation of state-of-the-art models. They also support open-source initiatives with the RedPajama project and offer a range of AI language and image models fine-tuned for specialized tasks.

Together AI

Cloud platform offering API, training, and hosting for generative AI.

AI model fine-tuning

Scale specializes in data annotation, curation, and collection specifically tailored for AI applications, including autonomous vehicles and defense. Their offerings span from generative AI to reinforcement learning (RLHF), creating custom large language models (LLMs) to adapt AI initiatives to specific enterprise data. Collaborating with leading AI models from OpenAI, Google PaLM, and others, Scale facilitates the integration of enterprise data with these AI technologies. They deliver a full-stack generative AI platform, powering applications for federal, defense, and commercial sectors, emphasizing quality, safety, and strategic differentiation in AI deployment and management.

Scale

Data annotation, AI model training, generative platform, enterprise integration.

Labelbox offers a data-centric platform for building AI applications. It includes data labeling, curation, model evaluation, and workflows tailored for AI data set preparation. Users can pre-label data with existing models, compare model predictions with ground truth, and utilize reinforcement learning with human feedback. The platform also features analytics dashboards and tools for evaluating large language models and vision tools. With Labelbox, companies in industries like agriculture, retail, and healthcare can improve their AI models with high-quality training data, optimize model performance, and automate tasks, significantly reducing the time spent on data management and curation.

Labelbox

Platform for data labeling, AI model training, and evaluation.

Brev offers a cloud-based development environment optimized for machine learning, providing easy access to powerful GPUs. Customers can fine-tune their own language models or utilize pre-configured notebooks, including ControlNet for Stable Diffusion. Brev enables users to generate creative visuals such as logos and QR codes, as well as interact with PDFs through OCR. It simplifies the process by connecting to cloud providers to source the best-priced GPUs, configuring them for users' needs, and offering SSH wrapped connections for seamless code editor integration. From individuals to engineers of esteemed companies, Brev is trusted for their robust infrastructure and user-friendly setup.

Brev

Brev streamlines AI model training, tuning on cloud GPUs.

Automorphic specializes in making language models more secure and self-improving. They offer a platform called Conduit which allows users to infuse language models with knowledge using as few as 10 samples, overcoming the inefficiencies of prompt stuffing and context-window limitations. Conduit permits fine-tuning and combining behavioral and knowledge adapters. It incorporates human-in-the-loop feedback for rapid model iteration and deployment. The system also improves models continuously by augmenting datasets. Conduit supports rapid adapter loading, is OpenAI API compatible, and offers on-premises deployment to ensure data security. Automorphic focuses on tuning, feedback, and performance, simplifying model deployment.

Automorphic

Enhances language models with efficient knowledge infusion and adaptability.

OpenPipe is a startup offering a platform for training and deploying machine learning models with a focus on lowering costs and shortened deployment loops. Users can automatically capture production data, train models in a few clicks, and deploy them on managed endpoints ready for scaling. They highlight significant cost savings over GPT-4, reduced latency, and simpler model management. With various use cases from CEOs and CTOs, OpenPipe has saved customers $7M this year, boasting features like autoscaling, analytics, and support for hundreds of thousands of training rows and fine-tuning of open-source models like Mistral and Llama 2.

OpenPipe

Train, deploy fine-tuned AI models quickly and cost-effectively.

AI model infrastructure

Replicate allows users to execute and refine open-source AI models, as well as deploy custom models, with a single code line. It supports a variety of generative AI models, including image, language, and music generation, with a significant emphasis on scalability and simple integration. Developers can personalize models with their data for specific use cases. Replicate facilitates deployment using Cog, their tool for containerizing ML models, automating API server creation, and handling cloud deployment. The platform scales with demand, charging only for the computational time used, and caters to businesses looking to integrate AI into their products rapidly.

Replicate

Run, manage, and scale machine learning models in the cloud.

Banana offers a serverless GPU infrastructure tailored for AI teams needing scalable, high-throughput inference hosting. Unlike others, it autoscales GPU resources to optimize costs and performance. Banana provides a full platform experience with DevOps features like GitHub integration, CI/CD pipelines, CLI tools, and real-time monitoring with built-in analytics for decision-making. It promotes cost transparency with pass-through pricing, charging only for compute without extra fees. Additionally, its open API and developer tools support customization and automation. Banana combines simplicity in scaling with an open-source HTTP framework, Potassium, suitable for startups and enterprises demanding efficient growth.

Banana

AI inference hosting, autoscaling GPUs, low costs, DevOps included.

Hugging Face is an AI collaboration hub for researchers and developers focusing on machine learning models, datasets, and applications. It provides tools for easier model creation, discovery, and collaboration. Users can host unlimited models, utilize various data modalities, build their ML portfolios, and share their work. Hugging Face accelerates machine learning projects by offering paid compute solutions and enterprise-grade features such as security and support. They cater to a vast community with their open-source ML toolsets compatible with Pytorch, TensorFlow, and JAX, and they provide state-of-the-art models and tooling across several domains of AI.

Hugging Face

Collaborative AI platform for model sharing and machine learning development.

Fal is a startup offering a platform for building real-time AI applications with quick inference capabilities, averaging under 120ms. The service eliminates cold start delays and adopts a pay-as-you-go pricing model. Users can access a variety of pre-built models from the Model Gallery with simple API endpoints for seamless AI integration. The gallery includes tools like Stable Diffusion with customizable LoRA weights, SDXL for swift processing, Stable Cascade for efficient image generation, short video clip generation from images, state-of-the-art image upscaling, and SAM for AI model management. Fal allows customization of model endpoints including timeout settings, concurrency, and autoscaling.

Fal provides fast, real-time AI inference and customizable models.

Modular provides the MAX Platform, a suite of tools designed to enhance the deployment and performance of AI applications, supporting both generative and traditional AI workloads. It empowers AI developers by simplifying the complexity of deployment across various frameworks and hardware, without necessitating a rewrite of existing stacks. The platform offers compatibility with pre-existing production tools and promises high performance within latency and budget constraints. Their enterprise-ready solutions prioritize data protection, privacy, and compliance. Modular is committed to facilitating AI innovation by delivering scalable infrastructure that addresses the needs of large-scale enterprises while remaining accessible to smaller edge devices.

Modular

Modular provides integrated AI tools for efficient, multi-framework model deployment.

Modal is a serverless platform designed to serve AI and ML teams by running generative AI models, batch jobs, and job queues. It allows users to bring their own code while it manages the infrastructure, enabling efficient scaling to numerous GPUs. Modal's container system, built in Rust, ensures rapid scaling and low-latency starts. The platform supports custom container images and hardware requirements, facilitating rapid deployment to the cloud. Modal offers a pay-per-use model with competitive compute rates and a monthly free compute allowance. Customers like Substack and Ramp leverage Modal for efficient, parallel processing on demanding data-intensive applications.

Modal

Serverless AI and ML platform, scalable, fast, pay-per-use infrastructure.

Modelbit is an MLOps platform that helps machine learning teams deploy models rapidly with minimal configuration. It supports a variety of ML models, including computer vision and fraud detection, with seamless integration into Jupyter, Colab, and Hex notebooks. The platform offers on-demand compute that scales automatically and can deploy to private clouds or Snowpark ML. Modelbit simplifies deployment with tools that transform models into REST APIs and SQL functions within data warehouses. Integration with Git ensures version control and CI/CD compatibility. Customers have successfully reduced costs and sped up model iteration.

Modelbit

MLOps platform enabling quick machine learning deployments and scaling.

LM Studio specializes in allowing users to discover, download, and execute local large language models (LLMs) like LLaMa, Falcon, MPT, and StarCoder on their devices. The platform supports various models on Hugging Face, ensuring compatibility with popular formats. Their latest software version includes bug fixes and performance enhancements. It's necessary to have hardware supporting AVX2, with Linux support currently in beta. LM Studio's development is facilitated by the llama.cpp project. Furthermore, they offer support through various channels such as Twitter, Github, and Discord, and are actively expanding their team, inviting job applications through their careers page.

LM Studio

Provides tools to discover, download, and run local LLMs.

E2B, a startup, specializes in providing cloud runtime solutions for AI agents with secure, sandboxed cloud environments that require no infrastructure management. They offer internet access, long-running sessions, and customization options through Python and JavaScript SDKs. Their platform is designed to safely run AI agents, allowing developers to build and deploy AI applications without worrying about underlying infrastructure. With a focus on building LLM benchmarks for frontend applications, E2B enables efficient and scalable development of AI-driven technologies. The company is a part of FoundryLabs, Inc., dedicated to pioneering innovations in the AI space.

E2B creates secure sandboxed cloud environments for AI agents.

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