Best AI Infrastructure Platforms for OpenAI

Find and compare the best AI Infrastructure platforms for OpenAI in 2024

Use the comparison tool below to compare the top AI Infrastructure platforms for OpenAI on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    Klu Reviews
    Klu.ai, a Generative AI Platform, simplifies the design, deployment, and optimization of AI applications. Klu integrates your Large Language Models and incorporates data from diverse sources to give your applications unique context. Klu accelerates the building of applications using language models such as Anthropic Claude (Azure OpenAI), GPT-4 (Google's GPT-4), and over 15 others. It allows rapid prompt/model experiments, data collection and user feedback and model fine tuning while cost-effectively optimising performance. Ship prompt generation, chat experiences and workflows in minutes. Klu offers SDKs for all capabilities and an API-first strategy to enable developer productivity. Klu automatically provides abstractions to common LLM/GenAI usage cases, such as: LLM connectors and vector storage, prompt templates, observability and evaluation/testing tools.
  • 2
    Azure OpenAI Service Reviews

    Azure OpenAI Service

    Microsoft

    $0.0004 per 1000 tokens
    You can use advanced language models and coding to solve a variety of problems. To build cutting-edge applications, leverage large-scale, generative AI models that have deep understandings of code and language to allow for new reasoning and comprehension. These coding and language models can be applied to a variety use cases, including writing assistance, code generation, reasoning over data, and code generation. Access enterprise-grade Azure security and detect and mitigate harmful use. Access generative models that have been pretrained with trillions upon trillions of words. You can use them to create new scenarios, including code, reasoning, inferencing and comprehension. A simple REST API allows you to customize generative models with labeled information for your particular scenario. To improve the accuracy of your outputs, fine-tune the hyperparameters of your model. You can use the API's few-shot learning capability for more relevant results and to provide examples.
  • 3
    Brev.dev Reviews

    Brev.dev

    Brev.dev

    $0.04 per hour
    Find, provision and configure AI-ready Cloud instances for development, training and deployment. Install CUDA and Python automatically, load the model and SSH in. Brev.dev can help you find a GPU to train or fine-tune your model. A single interface for AWS, GCP and Lambda GPU clouds. Use credits as you have them. Choose an instance based upon cost & availability. A CLI that automatically updates your SSH configuration, ensuring it is done securely. Build faster using a better development environment. Brev connects you to cloud providers in order to find the best GPU for the lowest price. It configures the GPU and wraps SSH so that your code editor can connect to the remote machine. Change your instance. Add or remove a graphics card. Increase the size of your hard drive. Set up your environment so that your code runs always and is easy to share or copy. You can either create your own instance or use a template. The console should provide you with a few template options.
  • 4
    Featherless Reviews

    Featherless

    Featherless

    $10 per month
    Featherless, an AI model provider, offers its subscribers access to an ever-expanding library of Hugging Faces. You need dedicated tools to keep pace with the hype. With hundreds of models being added daily, you will need dedicated tools. Featherless lets you find and use the latest AI models, no matter what your use case is. LLaMA-3 models are supported, including LLaMA-3, QWEN-2, and LLaMA-3. Note that QWEN-2 models can only be supported up to 16 000 context length. Soon, we plan to add new architectures to the list of supported architectures. As new models become available on Hugging Face, we continue to add them. As we grow, our goal is to automate the process so that all Hugging Face models available publicly with compatible architecture are included. To ensure fair account usage, the number of concurrent requests is limited based on the plan selected. The output is delivered between 10-40 tokens/second, depending on the prompt size and model.
  • Previous
  • You're on page 1
  • Next