We've all seen the explosion of prompt engineering tools lately. While playing around in playgrounds is fun, when we tried to take our AI prompts to production, we hit a wall. I'm guessing many of you have experienced similar pain points.
We kept hitting questions nobody seemed to be answering: How do you version control thousands of prompts? How do you handle multiple production deployments? How do you scale from prototype to millions of requests per day? How do you collaborate across hundreds of engineers without stepping on each other's toes?
So we built Portkey's Prompt Engineering Studio - a complete toolkit designed specifically for productionizing AI prompts across 1600+ models.
Some technical details that make our approach different:
- High-performance infrastructure: We've deployed prompts as large as 500,000 tokens with production-level latency - Git-like version control with instant rollbacks for prompt deployments - Mustache templating system for parameterization and reusable snippets - Publish/release flow with proper dev/staging/prod environments - Real-time analytics tracking prompt performance, latency, and token usage - Native integrations with Langchain, Llamaindex, and Promptfoo
The scaling capabilities have enabled some impressive use cases:
- A content company running 500+ prompts across 700+ websites - A tech firm that cut deployment times from 3 days to near-instant - Education platforms with hundreds of non-technical creators building AI workflows
Our platform has processed hundreds of millions of prompt completion requests already, with over 10,000 prompts deployed to production environments.
We think the HN community will especially appreciate our approach to bringing software engineering best practices to AI development!
You can try it yourself at prompt.new
I'd genuinely love to hear how others in the community are handling these challenges, what you think of our approach, or any other feedback you might have. This community has been invaluable in shaping how we think about developer tools.
