Key Features
Collaborative Prompt Development
Leverage the Playground to experiment, debug, and collaborate on prompts in a user-friendly environment, allowing teams to iterate and refine their language models effectively.
Performance Testing
Create a suite of tests to evaluate how changes to prompt text or LLM parameters affect the application’s performance, ensuring consistent and reliable behavior.
Seamless Deployment
Publish prompts as API endpoints, enabling teams to iterate on their language models without redeploying the entire application codebase.
Real-time Monitoring and Insights
Observe real-world user inputs and how the language model responds. Monitor important data points such as latency and cost, allowing teams to optimize their language models for efficiency and cost-effectiveness.

Why Use Langtail?
Do any of these situations sound familiar?- I have a text file of sample user inputs and I paste them one by one into my app to see the output “seems good”.
- My teammate from marketing wants to help write prompts but … all of them live in the codebase. So, I paste the prompt into a Google Doc and share it with them. Then copy it back to code when they’re done.
- I have no idea if some users are costing me more money than others. The only data I have available is the monthly spend chart from OpenAI’s billing dashboard.
- My LLM provider released a new version of their model but I’m too nervous to upgrade because I don’t want to break my app.