The Generative AI Infrastructure Landscape by Segmind

Dive into the Generative AI Infrastructure landscape and explore what traditional tools have been customized to meet Generative AI's needs. Plus, get a look at emerging startups building infrastructure.

The Generative AI Infrastructure Landscape by Segmind

Generative AI Infrastructure is quietly powering application companies to reach greatness with Generative AI.

Similar to picks and shovels being sold to gold miners during the gold rush Generative AI Infrastructure companies are powering application companies to achieve rapid innovation and value unlock with Generative AI use cases.

As we see it, the Generative AI Landscape can be divided into five core areas: Compute, Data, Training, Inference, Recommender Systems, and Platforms. The landscape includes traditional tools that have been customized to meet the needs of Generative AI. In addition, there are emerging Generative AI Startups building Infrastructure specifically to meet the requirements of Generative AI.

From our perspective at Segmind, the Generative AI Infrastructure Landscape will experience the rapid creation of dozens of new startups being created. These startups will be built for the specific requirements and complexities of Generative AI use cases that existing infrastructure companies either do not service or do not service very well.

On the other hand, some of the companies currently focused on Generative AI Infrastructure will see rapid adoption and success because they are in the market first or near first and have the right market timing to address the existing pain points Generative AI teams are currently experiencing.

Generative AI Infrastructure has a parallel to the now mature MLOps space that over the past eight years witnessed the rapid creation of startups that became multi-billion dollar scale-ups like, Weights & Biases, and Dataiku. At the beginning of MLOps, only a handful of companies existed with minuscule amounts of revenue generated from the space as a whole. Currently, it is likely that most existing and new Generative AI Infrastructure startups are under $10m in revenue expect for a few outliers, but not for long. By addressing the specific needs of Generative AI application companies across the five core areas listed in our landscape, some of the startups identified in the Generative AI Infrastructure Landscape will experience explosive growth.

Segmind is seeing that Generative AI startups are choosing to buy rather than build their capabilities across all six categories in the Generative AI Infrastructure Landscape. Additionally, we are seeing that much of the growth in Generative AI use cases is still ahead of us, indicating that the implementation of Generative AI Infrastructure is still in its early stages.


Traditional Compute

  1. Google Compute Engine                         Compute Engine delivers configurable virtual machines running in Google's data centers with access to high-performance.
  2. Amazon EC2                              The broadest and deepest compute platform, with over 500 instances and choice of the latest processor, storage, networking, operating system, and purchase model to help you best match the needs of your workload.
  3. Microsoft Azure                          Invent with purpose, realize cost savings, and make your organization more efficient with Microsoft Azure's open and flexible cloud computing platform.
  4. NVIDIA DGX                          NVIDIA DGX Systems deliver the world's leading solutions for enterprise AI development at scale. Inspired by the demands of AI.

Cloud GPUs    

  1.                              The Airbnb of GPU Compute. Data centers at 5x better prices.
  2. CoreWeave                          CoreWeave is a specialized cloud provider, delivering a massive scale of GPUs on top of the industry’s fastest and most flexible infrastructure.
  3. Lambda Labs                            GPU cloud built for deep learning. Instant access to the best prices for cloud GPUs on the market. Save over 73% vs AWS, Azure, and GCP. Configured for deep learning with PyTorch®, TensorFlow, Jupyter
    The market leader in low-cost cloud GPU rental.Use one simple interface to save 5-6X on GPU compute.
  5.                             Rent GPU. Train and deploy AI, ML and DL models in few clicks trusted by over 10,000 ML practitioners.


Traditional Storage

  1. Amazon Simple Storage Service (Amazon S3)               An object storage service offering industry-leading scalability, data availability, security, and performance.
  2. Microsoft Azure Blob Storage                       helps you create data lakes for your analytics needs, and provides storage to build powerful cloud-native and mobile apps.
  3. Google Cloud Storage                           lets you store data with multiple redundancy options, virtually anywhere.

Generative AI Specific Storage  

  1.                               No-code tool to generate and maintain custom data integrations. Lume uses AI to automatically transform data between any start and end schema, and pipes the data directly to your desired destination.
  2.                             ChatGPT for your company's data. Let your users ask free form data questions through large language models embedded in your app
  3.                             Companies struggle to customize their generative AI models by taking advantage of their existing customer and enterprise data, in real-time. Chima solves for this through a sleek, interoperable layer before the standard generative AI models are applied.
  4.                                AI-Powered Automation for Enterprise.Fine-tune and compose Large Language Models to automate your business processes.


  1.                               Unmatched speed and scale. Learn about the distributed techniques of Colossal-AI to maximize the runtime performance of your large neural networks.
  2.                              The MosaicML platform enables you to easily train large AI models on your data, in your secure environment.
  3. Rubbrband                               ML Training in 1 Line of Code. Rubbrband is a CLI that enables training of the latest ML models in a single line of code.
  4. Ivy                                Unified Machine Learning. Unify all ML frameworks pip install ivy-core.


  1. Pytorch Lighting                         PyTorch Lightning is the deep learning framework for professional AI researchers and machine learning engineers who need maximal flexibility without sacrificing performance at scale. Lightning evolves with you as your projects go from idea to paper/production.
  2. Cerebras                                The fastest AI accelerator, based on the largest processor in the industry, and made it easy to use. With Cerebras, blazing fast training, ultra low latency inference, and record-breaking time-to-solution enable you to achieve your most ambitious AI goals.
  3. Keras                                Consistent & simple APIs that minimizes the number of user actions required for common use cases with  clear & actionable error messages.
  4. PyTorch                                An open source machine learning framework that accelerates the path from research prototyping to production deployment.
  5. Tensorflow                             Open-source software library for machine learning and artificial intelligence. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks.
  6. Google Jax                               Machine learning framework for transforming numerical functions. It is described as bringing together a modified version of autograd and TensorFlow's XLA.



  1. Segmind                                Serverless Optimization Platform For Generative AI. The first serverless optimization platform that increases inference speed by up to 5x for Generative AI.
  2.                               Optimize AI compute cost and performance in one place
  3. OctoML                                 Optimize and package your trained model in minutes so you can deploy it to any hardware target for faster, more cost-efficient inference.
  4.                              Accelerate your cloud GPU workloads by optimizing scaling, deployment, and latency. You can run a generative AI model or encode a video on our service up to 10x faster than standard solutions while cutting costs by up to 90% and reducing engineering risk.

Serverless Deployment

  1. Replicate                               Run machine learning models with a few lines of code, without needing to understand how machine learning works. Use our Python library:
  2. Segmind                                Serverless Optimization Platform For Generative AI. The first serverless optimization platform that increases inference speed by up to 5x for Generative AI.
  3. Beam Cloud                           Develop on remote GPUs, train machine learning models, and rapidly prototype AI applications — without managing any infrastructure.
  4.                                GPU Cloud. Scalable infrastructure built for production. Rent Cloud GPUs from $0.2/hour.
  5. ONNX Runtime                             Speed up machine learning process. Built-in optimizations that deliver up to 17X faster inferencing and up to 1.4X faster training
  6. Deep Infra                              Run the top AI models using a simple API, pay per use. Low cost, scalable and production ready infrastructure.
  7. Inferless                            Serverless GPU for ML models is here The fastest way to deploy production-ready ML. No hassle of managing servers, DevOps cost, no favours required.
  8. Amazon Sagemaker                        Machine Learning for Every Developer and Data Scientist. Try Today with AWS. Get Machine Learning Models into Production Quickly with Amazon SageMaker.
  9. Banana.Dev                            Scale your machine learning inference and training on serverless GPUs.
  10. Kubernetes                          Kubernetes, also known as K8s, is an open-source system for automating deployment, scaling, and management of containerized applications.
  11. TensorFlow                           Extended  Build and manage end-to-end production ML pipelines. TFX components enable scalable, high-performance data processing, model training and deployment.
  12. TrueFoundry                              The fastest framework for Post model Pipeline. Instant monitored endpoints for models in 15 minutes with the best DevOps practices.

Severless Deployment Optimization

  1. Segmind                                Serverless Optimization Platform For Generative AI. The first serverless optimization platform that increases inference speed by up to 5x for Generative AI.


  1. PYQ                                Pyq is an easy and affordable way to integrate machine learning into your application. We help developers skip all the cloud infrastructure and setup, and get straight to the most important part: leveraging ML in their apps.
  2.                                Open-source unified compute framework that makes it easy to scale AI and Python workloads — from reinforcement learning to deep learning to tuning, and model serving. Learn more about Ray’s rich set of libraries and integrations.
  3.                              AGIs for your APIs: Infrastructure for customizing LLMs with agency

Recommender System

  1. Rubber Ducky Labs                         Better recommender systems with machine learning plus human expertise


  1. Rubber Ducky Labs                         Better recommender systems with machine learning plus human expertise
  2. Wild Moose Jobs
    Helps on-call developers more quickly identify the source of production incidents. We do this by providing a conversational AI trained on their environment. Our LLM-based AI tailor-made to accurately answer questions about production information such as logs and metrics.

Thank you for reading we will provide updates on the Generative AI Infrastructure as the space evolves. Follow us for more updates on Generative AI!