Production Deployment Guide

Note

Production-ready deployment

This guide walks you through deploying HoneyHive in production environments with proper security, monitoring, and scalability considerations.

Overview

Deploying HoneyHive in production requires careful consideration of:

Security: API key management and data protection
Performance: Minimizing overhead and optimizing throughput
Reliability: Error handling and failover strategies
Monitoring: Observing the observability system itself
Scalability: Handling high-volume applications

This guide provides step-by-step instructions for each consideration.

Security Configuration

API Key Management

Never hardcode API keys in production code.

Recommended: Environment Variables

# .env file (not committed to version control)
HH_API_KEY=hh_prod_your_production_key_here
HH_SOURCE=production

import os
from honeyhive import HoneyHiveTracer

# Secure initialization
tracer = HoneyHiveTracer.init(
    api_key=os.getenv("HH_API_KEY"),
    source=os.getenv("HH_SOURCE")
)

Enterprise Secret Management:

For production environments, use dedicated secret management services:

AWS Secrets Manager: Retrieve from secretsmanager using boto3
HashiCorp Vault: Use hvac client to fetch from kv store
Azure Key Vault: Use azure-keyvault-secrets SDK
Google Secret Manager: Use google-cloud-secret-manager

All services follow the same pattern: fetch credentials at startup, handle failures gracefully, and return None if unavailable to enable graceful degradation.

Network Security

Configure TLS and network security:

from honeyhive import HoneyHiveTracer

tracer = HoneyHiveTracer.init(
    api_key=os.getenv("HH_API_KEY"),
    base_url="https://api.honeyhive.ai",  # Always use HTTPS
    timeout=30.0,  # Reasonable timeout
    # Configure for corporate environments
    verify_ssl=True,  # Verify SSL certificates
)

Firewall and Proxy Configuration:

import os

# Configure proxy if needed
os.environ['HTTPS_PROXY'] = 'https://corporate-proxy:8080'
os.environ['HTTP_PROXY'] = 'http://corporate-proxy:8080'

# Or configure in code
tracer = HoneyHiveTracer.init(
    api_key=os.getenv("HH_API_KEY"),
    # Custom HTTP configuration if needed
)

Performance Optimization

Minimize Overhead

1. Selective Tracing

Don’t trace everything - focus on business-critical operations:

from honeyhive import HoneyHiveTracer, trace
import random

from honeyhive.models import EventType

tracer = HoneyHiveTracer.init(
    api_key=os.getenv("HH_API_KEY")

)

# Trace critical business operations
@trace(tracer=tracer, event_type=EventType.session)
def process_payment(user_id: str, amount: float):
    # Always trace financial operations
    pass

# Sample high-frequency operations
@trace(tracer=tracer, event_type=EventType.tool)
def handle_api_request(request):
    # Only trace 1% of API requests
    if random.random() < 0.01:
        # Detailed tracing
        pass

2. Async Processing

Use async patterns for high-throughput applications:

import asyncio
from honeyhive import HoneyHiveTracer, trace

tracer = HoneyHiveTracer.init(
    api_key=os.getenv("HH_API_KEY")

)

@trace(tracer=tracer)
async def process_user_request(user_id: str):
    """Async processing with automatic tracing."""
    # Non-blocking I/O operations
    user_data = await fetch_user_data(user_id)
    result = await process_data(user_data)
    return result

3. Batch Operations

Group operations to reduce overhead:

@trace(tracer=tracer, event_type=EventType.tool)
def process_batch(items: list):
    """Process multiple items in one traced operation."""
    results = []

    with tracer.trace("batch_validation") as span:
        valid_items = [item for item in items if validate_item(item)]
        span.set_attribute("batch.valid_count", len(valid_items))

    with tracer.trace("batch_processing") as span:
        results = [process_item(item) for item in valid_items]
        span.set_attribute("batch.processed_count", len(results))

    return results

Error Handling & Reliability

Graceful Degradation

The SDK provides built-in graceful degradation - tracing failures will never crash your application.

HoneyHive automatically handles errors in tracing operations, ensuring your business logic continues uninterrupted even if the tracing infrastructure is unavailable.

Comprehensive Error Handling:

All SDK operations are wrapped in try-except blocks that catch and log errors without propagating them:

from honeyhive import HoneyHiveTracer, trace
import logging

logger = logging.getLogger(__name__)

# ✅ Tracer initialization - NEVER throws exceptions
# Even with invalid API key, network failures, or configuration errors
tracer = HoneyHiveTracer.init(
    api_key="invalid-key",  # Won't crash - gracefully degrades
    source=os.getenv("HH_SOURCE", "production"),
    timeout=10.0  # Configure timeout for slow networks (default: 30s)
)

# ✅ Decorator tracing - NEVER throws exceptions
# Works even if HoneyHive API is down or unreachable
@trace(tracer=tracer)
def critical_business_function():
    """This function ALWAYS executes - tracing errors logged but not raised."""
    # Your business logic here - never interrupted by tracing errors
    return "success"

# ✅ Manual span enrichment - NEVER throws exceptions
# Even with invalid data types or API failures
@trace(tracer=tracer)
def user_request_handler(user_id, query):
    try:
        result = process_query(query)
        # Enrichment errors are caught internally
        tracer.enrich_span(metadata={"user_id": user_id})
        return result
    except Exception as e:
        # Your error handling - SDK never adds exceptions here
        logger.error(f"Business logic error: {e}")
        raise

What Gets Caught Internally:

Network Failures: Timeouts, connection errors, DNS failures
Authentication Errors: Invalid API keys, expired tokens
Serialization Errors: Invalid span data, encoding issues
API Errors: Rate limits, service unavailable, malformed responses
Configuration Errors: Invalid URLs, missing environment variables

Note

Timeout Configuration

The timeout parameter controls how long the SDK waits for API responses before gracefully degrading. Lower timeouts (5-10s) ensure faster degradation in network issues, while higher timeouts (30-60s) accommodate slow networks. Default is 30 seconds.

Evidence in Production:

# REAL-WORLD TEST: These ALL work without exceptions

# ❌ Invalid API key → Logs warning, continues execution
tracer1 = HoneyHiveTracer.init(api_key="invalid")

# ❌ HoneyHive API down → Logs error, continues execution
tracer2 = HoneyHiveTracer.init(
    api_key=os.getenv("HH_API_KEY"),
    server_url="https://nonexistent-domain.invalid"
)

# ❌ Network timeout → Logs timeout, continues execution
tracer3 = HoneyHiveTracer.init(
    api_key=os.getenv("HH_API_KEY"),
    timeout=0.001  # Impossibly short timeout
)

# ✅ ALL of the above initialize successfully and your code continues
# ✅ Traced functions execute normally even with failed tracers
# ✅ Check logs for warnings - application never crashes

Network Retries

The SDK provides built-in network retry logic for transient failures.

HoneyHive automatically retries failed API requests with exponential backoff, handling temporary network issues without requiring manual retry implementation.

from honeyhive import HoneyHiveTracer

# Simple initialization - retries are automatic
tracer = HoneyHiveTracer.init(
    api_key=os.getenv("HH_API_KEY"),
    source=os.getenv("HH_SOURCE", "production")
)

# The SDK handles:
# - Network timeouts → automatic retry with backoff
# - Transient API errors → automatic retry with backoff
# - Connection failures → graceful degradation after retries

Note

Built-in Retry Behavior

The SDK automatically retries failed requests up to 3 times with exponential backoff. This handles most transient network issues without requiring custom retry logic.

Container Deployment

Docker Configuration

Key HoneyHive-specific Docker configuration:

# Use Python 3.11+ for HoneyHive SDK
FROM python:3.11-slim

# Install HoneyHive SDK
RUN pip install honeyhive>=0.1.0

# HoneyHive environment variables (overridden at runtime)
ENV HH_API_KEY=""
ENV HH_SOURCE="production"

docker-compose.yml - pass HoneyHive credentials:

services:
  app:
    environment:
      - HH_API_KEY=${HH_API_KEY}
      - HH_SOURCE=production

Kubernetes Deployment

Store API key in Kubernetes Secret:

kubectl create secret generic honeyhive-secret \
  --from-literal=api-key=<your-api-key>

Reference in Deployment:

env:
- name: HH_API_KEY
  valueFrom:
    secretKeyRef:
      name: honeyhive-secret
      key: api-key
- name: HH_SOURCE
  value: "production"

Production Checklist

Before Going Live

Security: - [ ] API keys stored in secure secret management - [ ] HTTPS-only communication configured - [ ] Network access properly restricted - [ ] No sensitive data in trace attributes

Performance: - [ ] Tracing overhead measured and acceptable - [ ] Selective tracing strategy implemented - [ ] Batch processing for high-volume operations - [ ] Circuit breaker pattern implemented

Reliability: - [ ] Graceful degradation when tracing fails - [ ] Retry logic for transient failures - [ ] Health checks for tracing infrastructure - [ ] Monitoring and alerting in place

Operations: - [ ] Deployment strategy tested - [ ] Rollback plan prepared - [ ] Documentation updated - [ ] Team trained on troubleshooting

Compliance: - [ ] Data retention policies configured - [ ] Privacy requirements met - [ ] Audit logging enabled - [ ] Compliance team approval obtained

Ongoing Maintenance

Weekly: - Monitor tracing performance metrics - Review error rates and patterns - Check for new SDK updates

Monthly: - Analyze tracing data for insights - Review and optimize trace selection - Update documentation as needed

Quarterly: - Security review of configuration - Performance optimization review - Disaster recovery testing

Best Practices Summary:

Security First: Never compromise on API key security
Graceful Degradation: Tracing failures shouldn’t crash your app
Monitor Everything: Monitor your monitoring system
Start Simple: Begin with basic tracing, add complexity gradually
Test Thoroughly: Test tracing in staging environments first

Tip

Production observability is about balance - you want comprehensive visibility without impacting application performance or reliability. Start conservative and expand your tracing coverage based on actual operational needs.