Advanced 模型验证 Tools for Professionals

Discover cutting-edge 模型验证 tools built for intricate workflows. Perfect for experienced users and complex projects.

模型验证

  • Revolutionize LLM evaluation with Confident AI's seamless platform.
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    What is Confident AI?
    Confident AI offers an all-in-one platform for evaluating large language models (LLMs). It provides tools for regression testing, performance analysis, and quality assurance, enabling teams to validate their LLM applications efficiently. With advanced metrics and comparison features, Confident AI helps organizations ensure their models are reliable and effective. The platform is suitable for developers, data scientists, and product managers, offering insights that lead to better decision-making and improved model performance.
    Confident AI Core Features
    • Regression testing for LLMs
    • Performance metrics analysis
    • Model comparison tools
    • Confidence scoring
    • Integration capabilities
    Confident AI Pro & Cons

    The Cons

    No direct mobile or app store presence noted
    May require technical expertise to fully utilize evaluation customization
    Pricing details require visiting the pricing page, no immediate transparency on free vs paid tiers

    The Pros

    Open-source and supported by a large developer community
    Provides comprehensive LLM evaluation with 30+ metrics and automated testing
    Enterprise-grade security and compliance (HIPAA, SOCII)
    Supports on-premises deployment and multi-data residency
    Integrates easily into CI/CD pipelines for regression testing
    Offers detailed tracing and observability for debugging
    Trusted by top global companies and backed by Y Combinator
    Confident AI Pricing
    Has free planYES
    Free trial details
    Pricing modelFreemium
    Is credit card requiredNo
    Has lifetime planNo
    Billing frequencyMonthly

    Details of Pricing Plan

    Free

    0 USD
    • DeepEval testing reports on Confident AI
    • Evals in development and CI/CD
    • LLM tracing
    • Prompt versioning
    • Community and documentation support
    • Limited to 1 project
    • 5 test runs per week
    • 1 week data retention

    Starter

    19.99 USD
    • Everything in Free, plus full LLM unit and regression testing suite
    • Model and prompt scorecards
    • Annotate evaluation datasets on the cloud
    • Custom metrics for any use case
    • Online evaluations
    • Human-in-the-loop feedback leaving
    • Email support
    • Starting from 1 user seat
    • Starting from 1 project
    • Starting from 20k LLM traces/month
    • Starting from 5k online evaluation metric runs/month
    • 1 month data retention

    Premium

    79.99 USD
    • Everything in Starter, plus real-time performance alerting
    • Dataset backup and revision history
    • Publicly sharable testing reports
    • No-code LLM evaluation workflows
    • Custom evaluation model
    • Dedicated support channel
    • Add-Ons: HIPAA, Data residency in EU (or anywhere else on-demand), API access
    • Starting from 1 user seat
    • Starting from 1 project
    • Starting from 75K LLM traces/month
    • Starting from 25k online evaluation metric runs/month
    • 6 months data retention

    Enterprise

    USD
    • Custom pricing with unlimited advanced features
    • Everything in Premium, plus guardrails
    • Metrics and guardrails accuracy validation
    • User and permissions management
    • Dedicated On-Prem Deployment
    • SSO, SOC2
    • Dedicated 24x7 technical support
    • Unlimited user seats, projects, traces, online evaluations
    • Proprietary LLM for evaluations
    • Customized data retention
    For the latest prices, please visit: https://www.confident-ai.com/pricing
  • The Frontier Model Forum aims to advance AI safety and promote responsible development of frontier AI models.
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    What is frontiermodelforum.org?
    The Frontier Model Forum is a collaborative industry body formed by leading technology companies such as Microsoft, Anthropic, Google, and OpenAI. The Forum is committed to advancing AI safety research, promoting the responsible development of frontier models, and minimizing potential risks associated with AI technologies. By drawing on the expertise of its members, the Forum aims to contribute to the public good by sharing best practices and developing a public library of AI safety resources.
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