HoneyHive

What is HoneyHive?

HoneyHive is an AI developer platform designed to enable teams to securely deploy and enhance Language and Learning Models (LLMs) in production environments. It offers a comprehensive set of tools to work with any model, framework, or environment. The platform includes vital monitoring and evaluation tools to ensure the quality and performance of LLM agents. It facilitates the confident deployment of LLM-powered products and provides features for offline evaluation, monitoring, prompt engineering collaboration, debugging support, evaluation metrics, and model registry management. HoneyHive emphasizes enterprise-grade security, scalability, and end-to-end encryption, offering hosting options both on their cloud or in a Virtual Private Cloud (VPC). The platform also provides dedicated customer support to assist users throughout their AI development journey.

Who created HoneyHive?

Honeyhive was launched on December 22, 2022. The platform was created by Mohak Shah. Honeyhive is an AI developer platform designed to safely deploy and improve Language and Learning Models (LLMs) in production. It offers essential tools for teams to deploy LLM-powered products with confidence, including monitoring, evaluation tools, and a collaborative prompt engineering toolkit.

What is HoneyHive used for?

• Filter and curate datasets from production logs
• Export datasets for fine-tuning custom models
• Build active learning pipelines
• Monitoring and evaluation of Language and Learning Models (LLMs)
• Deploy LLM-powered products
• Collaborative prompt engineering
• Debugging of complex chains, agents, and RAG pipelines
• Model registry and version management system
• Seamlessly integrate with any LLM stack
• Pipeline-centric approach for complex chains, agents, and retrieval pipelines

Who is HoneyHive for?

• Data scientists
• AI developers
• Data engineers
• Machine learning engineers
• Data Analysts
• Engineers
• Domain experts

How to use HoneyHive?

To use HoneyHive, follow these steps:

1. Monitoring Metrics: Use HoneyHive to monitor your application's performance, usage, and security metrics for early issue detection.
2. Online Evaluation: Conduct live auto-evaluations to detect and address failures promptly.
3. Dashboard Insights: Access a dashboard for quick insights into essential metrics.
4. Custom Charts: Create custom charts to track key metrics based on your data queries.
5. Filters and Groups: Slice and dice your data for in-depth analysis using filters and groups.
6. Custom Properties: Log hundreds of properties for detailed analysis and insights.
7. User Feedback: Track real-time feedback from end-users to enhance user experience.
8. Prompt Management: Utilize the Studio workspace to collaborate on prompts, iterate with your team, and debug efficiently.
9. Playground Testing: Test new prompts and models within a shared workspace.
10. Version Management: Keep track of prompt changes and iterations with version management.
11. Deployments: Easily deploy prompt templates with a single click for seamless integration.
12. Infrastructure and Integration: Get started with just 3 lines of code for fast integration. Seamlessly integrate using Python, TypeScript SDKs, or OTEL traces from any programming language.
13. Security and Scalability: Benefit from secure and scalable infrastructure with end-to-end encryption, role-based access controls, and data privacy measures.

HoneyHive ensures secure data management, automated evaluations, human feedback integration, and collaborative workspace functionalities for AI model development and deployment. It is designed for enterprise-scale AI projects, offering dedicated support and secure hosting options.