AI Topic: Performance Metrics

Specialized tools for measuring, evaluating, and optimizing AI model performance across accuracy, speed, resource utilization, and other critical parameters.

AI Tools in Performance Metrics (19)

DX tool icon

DX

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Performance Metrics

Developer intelligence platform that measures engineering productivity, tracks AI adoption, and provides actionable insights and tooling to improve developer experience and velocity.

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LLM Stats tool icon
LLM Evaluations

Public leaderboards and benchmark site that publishes verifiable evaluations, scores, and performance metrics for large language models and AI providers.

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Artificial Analysis tool icon
Performance Metrics

Independent AI model benchmarking platform providing comprehensive performance analysis across intelligence, speed, cost, and quality metrics

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LM Arena tool icon
Performance Metrics

Web platform for comparing, running, and deploying large language models with hosted inference and API access.

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Design Arena tool icon
User Research

Crowdsourced benchmark for AI‑generated design. Users vote on head‑to‑head outputs (web UI, images, video, audio) to rank models by human preference.

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AI-driven SQL and index recommendations for PostgreSQL and MySQL, integrated into the Aiven Console with a free online optimizer and IDE extensions.

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Alignerr tool icon
Human-in-the-Loop Training

Platform hiring independent contractors to earn money training AI models through data labeling, evaluation, and alignment tasks with flexible project-based work opportunities.

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Vals AI tool icon
Automated Testing

AI evaluation platform for testing LLM applications with industry-specific benchmarks, automated test suites, and performance analytics for enterprise teams.

Vals AI
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AugmentCode tool icon
Performance Metrics

Context-aware AI coding assistant that integrates your organization''s codebase, documentation, and development practices to help developers work faster and smarter

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Weights & Biases tool icon
Performance Metrics

End-to-end MLOps platform for tracking experiments, managing datasets, and optimizing machine learning and LLM workflows

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