Compare the Top AI Fine-Tuning Platforms that integrate with Redis as of October 2025

This a list of AI Fine-Tuning platforms that integrate with Redis. Use the filters on the left to add additional filters for products that have integrations with Redis. View the products that work with Redis in the table below.

What are AI Fine-Tuning Platforms for Redis?

AI fine-tuning platforms are tools used to improve the performance of artificial intelligence models. These platforms provide a framework for training and optimizing AI algorithms, allowing them to better understand and respond to data. They offer a variety of features such as automated hyperparameter tuning and data augmentation techniques. Users can also visualize the training process and monitor the model's accuracy over time. Overall, these platforms aim to streamline the process of fine-tuning AI models for various applications and industries. Compare and read user reviews of the best AI Fine-Tuning platforms for Redis currently available using the table below. This list is updated regularly.

  • 1
    Klu

    Klu

    Klu

    Klu.ai is a Generative AI platform that simplifies the process of designing, deploying, and optimizing AI applications. Klu integrates with your preferred Large Language Models, incorporating data from varied sources, giving your applications unique context. Klu accelerates building applications using language models like Anthropic Claude, Azure OpenAI, GPT-4, and over 15 other models, allowing rapid prompt/model experimentation, data gathering and user feedback, and model fine-tuning while cost-effectively optimizing performance. Ship prompt generations, chat experiences, workflows, and autonomous workers in minutes. Klu provides SDKs and an API-first approach for all capabilities to enable developer productivity. Klu automatically provides abstractions for common LLM/GenAI use cases, including: LLM connectors, vector storage and retrieval, prompt templates, observability, and evaluation/testing tooling.
    Starting Price: $97
  • 2
    LLMWare.ai

    LLMWare.ai

    LLMWare.ai

    Our open source research efforts are focused both on the new "ware" ("middleware" and "software" that will wrap and integrate LLMs), as well as building high-quality, automation-focused enterprise models available in Hugging Face. LLMWare also provides a coherent, high-quality, integrated, and organized framework for development in an open system that provides the foundation for building LLM-applications for AI Agent workflows, Retrieval Augmented Generation (RAG), and other use cases, which include many of the core objects for developers to get started instantly. Our LLM framework is built from the ground up to handle the complex needs of data-sensitive enterprise use cases. Use our pre-built specialized LLMs for your industry or we can customize and fine-tune an LLM for specific use cases and domains. From a robust, integrated AI framework to specialized models and implementation, we provide an end-to-end solution.
    Starting Price: Free
  • 3
    Label Studio

    Label Studio

    Label Studio

    The most flexible data annotation tool. Quickly installable. Build custom UIs or use pre-built labeling templates. Configurable layouts and templates adapt to your dataset and workflow. Detect objects on images, boxes, polygons, circular, and key points supported. Partition the image into multiple segments. Use ML models to pre-label and optimize the process. Webhooks, Python SDK, and API allow you to authenticate, create projects, import tasks, manage model predictions, and more. Save time by using predictions to assist your labeling process with ML backend integration. Connect to cloud object storage and label data there directly with S3 and GCP. Prepare and manage your dataset in our Data Manager using advanced filters. Support multiple projects, use cases, and data types in one platform. Start typing in the config, and you can quickly preview the labeling interface. At the bottom of the page, you have live serialization updates of what Label Studio expects as an input.
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