HoneyHive
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About
HoneyHive is an AI observability and evaluation platform designed to help engineering teams build, monitor, and govern production-grade AI agents and applications. By providing a unified interface that works across any model, framework, or agent runtime, the platform allows teams to move beyond simple completions to complex, multi-step agentic workflows. It bridges the gap between development and production by offering a suite of tools for distributed tracing, real-time monitoring, and systematic evaluation, ensuring that AI systems remain reliable, cost-effective, and safe as they scale. The core of the platform revolves around its distributed tracing and evaluation capabilities. Users can instrument end-to-end applications to track every component of a request, including prompts, retrieval steps, tool calls, and model outputs. This visibility is paired with a library of over 25 pre-built evaluators that continuously analyze live traffic for quality and safety. Additionally, the experiment module allows developers to run automated test suites within CI/CD pipelines, comparing different model versions or prompt configurations to catch regressions before they reach the end user. HoneyHive is particularly well-suited for AI engineers, product managers, and domain experts working in startups or large enterprises. It facilitates collaboration through an artifact management system that serves as a single source of truth for prompts, datasets, and metrics, which are kept in sync between the web UI and the code. This makes it ideal for teams that need to maintain high standards of reliability in sensitive industries like finance or healthcare, where SOC-2, GDPR, and HIPAA compliance are critical requirements. What sets HoneyHive apart is its OpenTelemetry-native architecture and its flexible deployment options. Unlike closed-source observability tools, it adheres to open standards, making it easier to integrate into existing technical stacks without vendor lock-in. Furthermore, it offers diverse hosting solutions, including multi-tenant SaaS, dedicated SaaS, and self-hosted private cloud options, giving organizations full control over their data residency and security posture.
Pros & Cons
OpenTelemetry-native architecture prevents vendor lock-in and follows industry standards.
Supports self-hosting and private cloud deployments for high security requirements.
Includes 25+ pre-built evaluators for continuous quality and safety monitoring.
Certified for SOC-2, GDPR, and HIPAA compliance for enterprise-grade security.
Synchronizes prompts and datasets between the web UI and code for seamless team collaboration.
The free Developer plan is limited to 30 days of data retention.
Access to custom SSO and SAML is restricted to the Enterprise tier.
The Developer tier is restricted to a maximum of 10,000 events per month.
Custom usage limits and SLAs require reaching out for a dedicated Enterprise quote.
Use Cases
AI engineering teams can use distributed tracing to debug complex agentic workflows and tool calls in real-time.
Product managers can manage and version prompts in the Prompt Studio without needing to change application code directly.
DevOps engineers can integrate automated evaluations into CI/CD pipelines to prevent quality regressions during deployments.
Security officers in regulated industries can utilize the self-hosting option to ensure data never leaves their private cloud.
Domain experts can use the annotation and evaluation tools to provide feedback on model outputs to improve accuracy.
Platform
Task
Features
• prompt management
• distributed tracing
• self-hosted deployment options
• opentelemetry-native architecture
• real-time monitoring & alerts
• artifact management
• ci/cd regression testing
• automated evaluators
FAQs
What types of AI components can HoneyHive trace?
HoneyHive provides end-to-end instrumentation for prompts, retrieval steps, tool calls, Model Context Protocol (MCP) servers, and model outputs. This allows teams to visualize the entire execution flow of an agentic application to identify and fix bottlenecks or errors quickly.
Does HoneyHive support automated testing in CI/CD?
Yes, the platform includes an experiments module specifically designed to validate agents pre-deployment on large test suites. It allows you to compare different versions and catch regressions within your existing CI/CD pipelines before changes are pushed to production.
What compliance standards does the platform meet?
HoneyHive is designed for enterprise-grade security and is compliant with SOC-2, GDPR, and HIPAA standards. This makes it suitable for organizations handling sensitive data in regulated industries like healthcare and finance.
Can I host HoneyHive on my own infrastructure?
Yes, Enterprise customers have the option to choose between multi-tenant SaaS, dedicated SaaS, or a self-hosted private cloud deployment. This flexibility ensures that organizations can meet specific data residency and privacy requirements.
How many events are included in the free tier?
The Developer plan is free and includes up to 10,000 events per month. It also supports up to 5 users and a single workspace with 30 days of data retention.
Pricing Plans
Enterprise
Unknown Price• Custom usage limits
• Unlimited users and workspaces
• Multi-tenant SaaS, dedicated SaaS, or self-hosting
• Custom SSO & SAML
• Dedicated support
• SLA guarantee
• Team trainings
Developer
Free Plan• 10K events per month
• Up to 5 users
• Single workspace
• 30d data retention
• Full evaluation suite
• Full observability suite
• Prompt management suite
Job Opportunities
Member of Technical Staff, Platform
Monitor, evaluate, and govern AI agents across any model or framework with distributed tracing, automated evaluations, and real-time monitoring for reliability.
Benefits:
Competitive salary + meaningful equity
Health, vision, and dental benefits
Unlimited PTO
Assistance in relocating to NYC or SF
MacBook Pro + peripherals
Experience Requirements:
5+ years of full-time experience in infrastructure / platform engineering roles
Deep expertise in Kubernetes and experience managing deployments in production environments
Experience with high-throughput data systems and databases like ClickHouse
Experience operating production systems in multiple cloud environments (AWS/GCP/Azure)
Familiarity with event-driven architectures and message queuing systems (e.g., NATS, Kafka)
Other Requirements:
Strong expertise in TypeScript and Go
Track record of building developer-facing products with intuitive APIs
Natural curiosity and bias for action
Previous experience at early-stage startups
Familiarity with core AI engineering concepts like prompt engineering, RAG, evals, etc.
Responsibilities:
Own the development and optimization of our core microservices architecture running on Kubernetes
Build scalable data processing pipelines that handle large volumes of AI interaction data
Scale distributed databases like ClickHouse to enable powerful analytics capabilities
Build and automate secure customer‑VPC deployments across AWS, Azure, and GCP
Collaborate with the founding team to shape our technical architecture and product roadmap
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