promptfoo vs scalene
Side-by-side comparison of two AI agent tools
promptfooopen-source
Test your prompts, agents, and RAGs. Red teaming/pentesting/vulnerability scanning for AI. Compare performance of GPT, Claude, Gemini, Llama, and more. Simple declarative configs with command line and
scaleneopen-source
Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals
Metrics
| promptfoo | scalene | |
|---|---|---|
| Stars | 18.9k | 13.3k |
| Star velocity /mo | 1.7k | 30 |
| Commits (90d) | — | — |
| Releases (6m) | 10 | 8 |
| Overall score | 0.7957593044797683 | 0.6054114136616837 |
Pros
- +Comprehensive testing suite covering both performance evaluation and security red teaming in a single tool
- +Multi-provider support with easy comparison between OpenAI, Anthropic, Claude, Gemini, Llama and dozens of other models
- +Strong CI/CD integration with automated pull request scanning and code review capabilities for production deployments
- +AI-powered optimization suggestions provide actionable recommendations beyond just identifying bottlenecks
- +Exceptional performance - runs orders of magnitude faster than traditional profilers while providing more detailed information
- +Comprehensive monitoring covers CPU, GPU, and memory usage with line-by-line granularity in a single tool
Cons
- -Requires API keys and credits for multiple LLM providers, which can become expensive for extensive testing
- -Command-line focused interface may have a learning curve for teams preferring GUI-based tools
- -Limited to evaluation and testing - does not provide actual LLM application development capabilities
- -Python-specific tool, not suitable for other programming languages
- -AI optimization features may require internet connectivity and external API access
- -GPU profiling capabilities may need additional setup depending on hardware configuration
Use Cases
- •Automated testing and evaluation of prompt performance across different models before production deployment
- •Security vulnerability scanning and red teaming of LLM applications to identify potential risks and compliance issues
- •Systematic comparison of model performance and cost-effectiveness to optimize AI application architecture
- •Identifying performance bottlenecks in data science and machine learning pipelines with both CPU and GPU components
- •Memory leak detection and optimization in long-running Python applications or web services
- •Performance analysis of scientific computing code to optimize numerical algorithms and reduce execution time