Question: How can I efficiently experiment with different prompts and models for my AI project without affecting production?

If you want to try out different prompts and models for your AI project without breaking anything in production, Vellum is a good candidate to check out. Vellum has tools for prompt engineering, semantic search, prompt chaining, and large-scale prompt evaluation. It's built for enterprise-class use, with features like SOC2 Type II compliance, HIPAA compliance, and virtual private cloud deployments, so your experiments can be secure and scalable.

Another good option is PROMPTMETHEUS, which lets you write, test, optimize and deploy one-shot prompts to more than 80 models from many different providers. The service has a full-featured toolbox for constructing and refining prompts, along with features like composability, cost estimation and data export. Pricing ranges from free to enterprise, so PROMPTMETHEUS can accommodate a wide range of needs and budgets.

If you want a collaborative environment for lots of testing and evaluation, HoneyHive is worth a look. HoneyHive offers an LLMOps environment for prompt management, automated CI testing and observability. It supports more than 100 models through common GPU clouds, and it's got features like evaluation reports, benchmarking and a playground for collaborative testing and deployment.

Last, Humanloop is a collaborative playground for developers and product managers to build and iterate on AI features. It's got a prompt management system with version control and a suite for debugging and monitoring AI performance. With Python and TypeScript SDKs for easy integration and pricing tiers that work for rapid prototyping and enterprise-wide deployment, Humanloop is a good option for optimizing AI development workflows.