Platform Architecture

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Overview

HoneyHive is a production-grade AI observability and evaluation platform built on enterprise-class infrastructure. Our architecture is designed to meet the stringent requirements of enterprise customers including security, compliance, scalability, and reliability. The platform consists of three core components:

1. Log Ingestion & Enrichment Pipeline - Real-time event processing with zero data loss
2. Evaluation & Analysis Engine - Asynchronous job processing for offline evaluations
3. Web Application & API - User-facing interfaces and programmatic access

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Network Architecture

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Infrastructure Overview

Our multi-tenant SaaS platform is hosted entirely within AWS US-West-2. Our dedicated SaaS version can be hosted in any AWS region worldwide.

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Edge & Network Layer

• Amazon Route 53: Global DNS routing with health checks and failover capabilities
• AWS Certificate Manager (ACM): Automated SSL/TLS certificate management for encrypted connections
• Application Load Balancer (ALB): Distributes incoming traffic across availability zones with automatic scaling
• VPC Architecture: Isolated Virtual Private Cloud with segregated public and private subnets across multiple availability zones

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Security & Access Control

• AWS IAM Roles for Service Accounts (IRSA): Fine-grained permission management for Kubernetes pods without shared credentials
• AWS Secrets Manager: Centralized secrets management with automatic rotation
• AWS KMS: Customer-managed encryption keys for data-at-rest encryption
• AWS Firewall Manager: Centralized firewall rule management and DDoS protection
• NAT Gateway: Secure outbound internet access for private subnet resources
• VPC Internet Gateway: Controlled ingress for public-facing services

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Compute & Orchestration

• Amazon EKS (Elastic Kubernetes Service): Managed Kubernetes cluster with automatic updates and patches
  • Multi-AZ deployment for high availability
  • Auto-scaling groups for dynamic capacity management
  • Pod security policies and network policies enforced
  • AWS EKS cluster runs in private subnets with no direct internet exposure

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Data Storage & Processing

• PostgreSQL on Amazon RDS:
  • Metadata storage for projects, configurations, and user management
  • Multi-AZ deployment with automatic failover
  • Encrypted at rest using AWS KMS
  • Automated backups with point-in-time recovery
  • Read replicas for performance optimization
• ClickHouse Instance:
  • High-performance columnar database for event storage
  • Customer events are encrypted at rest
  • Optimized for analytical queries on large datasets
  • Data retention policies configurable per customer
• Amazon S3:
  • Long-term log storage and archival
  • Server-side encryption (SSE-KMS)
  • Versioning enabled for audit trails
  • Lifecycle policies for cost optimization

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Monitoring & Observability

• Amazon CloudWatch: Real-time monitoring, logging, and alerting
• AWS CloudTrail: Comprehensive audit logging for all AWS API calls
• ArgoCD for GitOps: Infrastructure-as-code with automated deployments and rollbacks

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Message Queue

• NATS: High-performance message queue for asynchronous processing
  • TLS encryption for data in transit
  • At-least-once delivery guarantees
  • Isolated queues per tenant for data segregation

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Data Flow Architecture

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Request Flow

1. API Gateway Layer
   • User requests enter through API Gateway with authentication and rate limiting
   • Elastic Load Balancer distributes traffic across multiple availability zones
   • TLS 1.2+ encryption enforced for all connections
2. Kubernetes Service Mesh
   • EKS Load Balancer (Kube-system Namespace): Internal load balancing within the cluster
   • VPC Deployment Runner (Control Plane Namespace): Orchestrates deployment and service discovery
3. Backend Services (Backend Namespace)
   • Backend Service: Handles API requests, authentication, and authorization
   • Connects to PostgreSQL RDS for metadata operations (prompts, datasets, configurations)
   • Implements tenant isolation at the application layer

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Event Processing Pipeline

The event processing pipeline is designed for high throughput, low latency, and zero data loss:

1. Ingestion Service
   • Receives events from customer applications via SDK or API
   • Validates and normalizes incoming events
   • Publishes to NATS Encrypted Queue for downstream processing
   • Immediately acknowledges receipt to minimize client latency
2. ClickHouse Data Layer (Data Layer Namespace)
   • Stores encrypted customer events with tenant isolation
   • Optimized for high-volume writes and analytical queries
   • Data encrypted at rest with customer-managed keys
3. Enrichment Service
   • Consumes events from NATS queue
   • Performs real-time enrichment (session inheritance, metric calculations)
   • Updates event records with computed metadata
   • Triggers online evaluators if configured
4. Evaluation Service
   • Processes offline evaluation jobs
   • Consumes from NATS Encrypted Queue
   • Executes customer-defined evaluators (Python, LLM-based, or custom)
   • Stores evaluation results back to ClickHouse

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Data Storage

• PostgreSQL RDS: Stores metadata including:
  • User accounts and permissions
  • Project configurations
  • Prompt templates and versions
  • Dataset definitions
  • Evaluator configurations
• ClickHouse: Stores telemetry data as wide events, including:
  • Traces and spans
  • Event logs
  • Evaluation scores
  • Aggregated metrics
  • Metadata and custom properties

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Security & Compliance

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Data Encryption

• At Rest: All data encrypted using AWS KMS with customer-managed keys
• In Transit: TLS 1.2+ for all network communications
• Application Layer: Additional encryption for sensitive customer data

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Network Security

• VPC Isolation: Customer VPC with private subnets for all data processing
• Security Groups: Strict ingress/egress rules limiting access to required ports only
• Network Policies: Kubernetes network policies enforce pod-to-pod communication restrictions
• AWS PrivateLink: Available for dedicated SaaS customers to establish private connectivity between your VPC and HoneyHive’s services without exposing traffic to the public internet
• External Secrets Store: Separates secrets from application code

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Access Control

• IAM Roles: Service accounts use temporary credentials via IRSA
• RBAC: Kubernetes Role-Based Access Control for service permissions
• Least Privilege: Each service has minimal required permissions
• Multi-Factor Authentication: Available for all user accounts

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Compliance

• SOC 2 Type II: Audited annually
• GDPR: Data residency and privacy controls
• HIPAA: Available for healthcare customers

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Reliability & Performance

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High Availability

• Multi-AZ Deployment: Services distributed across multiple availability zones
• Automatic Failover: Database and compute resources automatically failover on failure
• Health Checks: Continuous monitoring with automatic recovery
• Zero-Downtime Deployments: Rolling updates with canary deployments

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Scalability

• Horizontal Auto-Scaling: Kubernetes HPA scales pods based on CPU/memory utilization
• Vertical Scaling: Database and storage scale independently
• Queue-Based Architecture: NATS queue buffers traffic spikes