PromptLayer

PromptLayer: The cleanest way to prompt engineer

PromptLayer is a platform for prompt management, evaluations, and LLM observability. It enables teams to edit, evaluate, deploy, and monitor prompts with a focus on collaboration, non-technical participation, and end-to-end prompt lifecycle management.

How it works

• Visually edit, evaluate, and deploy prompts from a centralized Prompt Registry.
• Perform A/B testing and compare usage, latency, and model performance across prompt variants.
• Collaborate with non-technical stakeholders by sharing prompts, running evaluations, and iterating in a team-friendly environment.
• Leverage LLM observability to read logs, identify edge cases, and improve prompts over time.
• Run one-off batch jobs and historical backtests to validate prompts against historical data.

What you can do with PromptLayer

• Prompt management and collaboration with a CMS-style interface
• No-code prompt editor for quick edits and experimentation
• Versioned prompts: edit, diff, annotate, and roll back changes
• Deploy prompts to prod and dev environments with interactive publishing
• A/B test prompts and compare model performance
• Historical backtests to evaluate new prompts against past data
• Regression testing and automated evals triggered on prompt updates
• Model comparison to see how prompts perform across different models
• One-off bulk prompt pipelines for batch testing on inputs
• Usage monitoring and analytics without needing to jump between tools
• Privacy and security: SOC 2 Type 2 compliant

Why teams choose PromptLayer

• Collaborate with experts and non-technical stakeholders without sacrificing control
• Decouple prompt deployments from engineering releases
• Centralized prompt governance reduces scattered prompts across codebases
• Empowers product, marketing, and content teams to directly edit and test prompts

Core Features

• Visual prompt editor and CMS for non-technical collaboration
• Version control for prompts with diffing and notes
• Deploy prompts interactively to prod and dev environments
• A/B testing and metrics comparison (latency, usage, model performance)
• Historical backtests to validate prompts against past data
• Regression tests and one-off batch evals
• Model-agnostic prompt testing across multiple models
• Usage analytics and observability integrated in the dashboard
• SOC 2 Type 2 compliant privacy and security