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What is Scorecard?

Scorecard is an AI evaluation platform designed to help teams build trust-worthy AI products by providing systematic testing, metrics and insights.

Scorecard supports the full lifecycle of AI agent development: it lets you convert real-user interactions into structured test-cases, run experiments across models and prompts, monitor how your AI behaves in production, and catch regressions early before they impact users.

Scorecard

Scorecard Plans and Pricing

Scorecard’s pricing is designed to suit AI teams, developers, and growing organizations. They offer flexible plans based on testing volume and feature needs. To check out their monthly and annual pricing, click on the button below.

Scorecard Use Cases

Scorecard helps AI teams **test, evaluate, and monitor their models with precision**, ensuring consistent performance and reliability across deployments. From prompt tuning to production monitoring, it empowers developers to **build trustworthy, high-quality AI products that users can rely on.**

AI Model Evaluation: If you’re building or fine-tuning AI models, Scorecard allows you to evaluate them using standardized metrics and test cases. This ensures your model performs consistently across different datasets and scenarios before going live.

Prompt Optimization and Testing: For teams working with LLMs or generative AI, Scorecard helps you test different prompts and configurations. You can compare results, measure output quality, and refine prompts for better accuracy and relevance.

Regression Testing for AI Models: When you update your model or retrain it, Scorecard automatically checks for regressions—performance drops or behavioral changes—to ensure that new updates don’t break what was already working well.

Continuous Monitoring in Production: Once your AI model is deployed, Scorecard monitors its real-world performance. It detects anomalies, drift, and degradation over time so your team can quickly identify issues and maintain reliability.

Benchmarking Across Models: If you’re experimenting with multiple models (like GPT, Claude, or custom fine-tuned versions), Scorecard lets you benchmark them side-by-side using identical datasets. This makes it easy to identify which model delivers the best performance for your use case.

Who is Scorecard ideal for?

AI and Machine Learning Teams: Tailored for AI developers and data scientists, Scorecard simplifies the process of evaluating and monitoring model performance. Teams can efficiently test model accuracy, detect regressions, and ensure consistent quality without manually reviewing results or writing complex evaluation scripts.

Product and Engineering Teams: Ideal for cross-functional product and engineering teams building AI-powered features, Scorecard helps them validate model behavior before deployment. It ensures that updates and iterations don’t introduce unintended issues, making AI product releases safer and more reliable.

AI Startups and Founders: For startups building generative AI or machine learning tools, Scorecard provides an end-to-end testing and monitoring framework. Founders can benchmark model performance, optimize prompts, and maintain trust in their AI without needing a full MLOps setup.

Research and Data Science Teams: Research labs and data science departments can use Scorecard to evaluate multiple models, datasets, or prompt variations simultaneously. It enables easy comparison and documentation of experimental results, helping teams publish insights faster with confidence in reproducibility.

Enterprises and Regulated Industries: Perfect for organizations operating in finance, healthcare, or other regulated sectors, Scorecard provides the transparency and documentation needed for compliance. It helps teams maintain governance over AI systems by tracking fairness, accuracy, and explainability metrics.

Scorecard Key Features

Test-set Management for Real Scenarios
Prompt Versioning and Tracking
Validated Metric Library (customizable metrics)
Structured Evaluation Runs & Results
Live Observability & Monitoring of AI Agent Behavior
Comparison & A/B-Testing of Models/Prompts
Integration with CI/CD Workflows and SDKs
Version History, Tracking and Rollback of Prompts/Models
Customizable Metrics & Business-Specific Evaluation
Single-Source Library for Prompts, Test-cases, and Evaluation Results

Weights & Biases (W&B): One of the most established tools for ML experiment tracking and model performance visualization, widely used by AI research and enterprise teams.

Evidently AI: Specializes in data and model monitoring with detailed performance metrics, drift detection, and customizable dashboards for production AI systems.

Arize AI: A strong competitor offering end-to-end model observability, performance tracking, and bias detection for machine learning models at scale.

Truera: Focused on explainability and responsible AI, Truera helps teams understand model behavior, fairness, and compliance across different stages of deployment.

WhyLabs: Known for its robust data and AI monitoring platform, WhyLabs provides automated quality checks, anomaly detection, and transparency tools for AI reliability.

While these platforms offer solid monitoring and testing solutions, Scorecard stands out for its simplicity, structured test-case approach, and focus on LLM and agent-based AI evaluation — helping teams build trustworthy AI faster and with less complexity.

Scorecard alternatives and competitors

If you don’t have time to wait for a Scorecard lifetime deal, here are some recurring alternatives you can explore that offer similar AI evaluation, testing, and monitoring capabilities.

Why we can get Scorecard

At Prime, we shamelessly request successful SaaS companies to offer an LTD on our platform no matter how big they already are. Somehow this works for us. Our past record gives us hope that we can get the Scorecard lifetime deal, if we get enough requests on this page. Let us bring this gem for you.