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Ultimate aplicações de modelos de linguagem Solutions for Everyone

Discover all-in-one aplicações de modelos de linguagem tools that adapt to your needs. Reach new heights of productivity with ease.

aplicações de modelos de linguagem

  • Inductor

    A platform to prototype, evaluate, and improve LLM applications rapidly.
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    What is Inductor?
    Inductor.ai is a robust platform aimed at empowering developers to build, prototype, and refine Large Language Model (LLM) applications. Through systematic evaluation and constant iteration, it facilitates the development of reliable, high-quality LLM-powered functionality. With features like custom playgrounds, continuous testing, and hyperparameter optimization, Inductor ensures that your LLM applications are always market-ready, streamlined, and cost-effective.
    Inductor Core Features
    • Prototyping
    • Custom Playgrounds
    • Continual Evaluation
    • Hyperparameter Optimization
    • Systematic Testing
    Inductor Pro & Cons

    The Cons

    Limited publicly available detailed product information.
    No clear indication of open-source availability.
    No direct links to app stores or community platforms.

    The Pros

    Purpose-built AI agents tailored for commercial applications.
    Focus on improving business KPIs such as reducing costs and boosting sales.
    Offers demos to showcase product capabilities.
    Inductor Pricing
    Has free planNo
    Free trial details
    Pricing model
    Is credit card requiredNo
    Has lifetime planNo
    Billing frequency
    For the latest prices, please visit: https://inductor.ai

  • LLMStack

    LLMStack is a managed platform to build, orchestrate and deploy production-grade AI applications with data and external APIs.
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    What is LLMStack?
    LLMStack enables developers and teams to turn language model projects into production-grade applications in minutes. It offers composable workflows for chaining prompts, vector store integrations for semantic search, and connectors to external APIs for data enrichment. Built-in job scheduling, real-time logging, metrics dashboards, and automated scaling ensure reliability and observability. Users can deploy AI apps via a one-click interface or API, while enforcing access controls, monitoring performance, and managing versions—all without handling servers or DevOps.
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