Multi-Instance Tracing

Run multiple tracer instances for multi-tenant, A/B testing, or environment-specific tracing.

Overview

HoneyHive supports multiple tracer instances in a single application. Each instance maintains its own session context, configuration, and OTel TracerProvider. Instances do not share context.

Projects are selected by the API key. Use a separate project-scoped API key when a tracer should write to a different HoneyHive project. Use metadata or session enrichment when traces should stay in the same project.

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Use Cases

Environment-Based Tracing

Send each environment to the project scoped to its API key:

import os
from honeyhive import HoneyHiveTracer, trace

env = os.getenv("ENVIRONMENT", "development")
api_keys = {
    "development": os.environ["HH_API_KEY_DEVELOPMENT"],
    "staging": os.environ["HH_API_KEY_STAGING"],
    "production": os.environ["HH_API_KEY_PRODUCTION"],
}

tracer = HoneyHiveTracer.init(
    api_key=api_keys[env],
    source=env
)

Multi-Tenant SaaS

Give each tenant its own tracer and session metadata. If tenants need project-level isolation, select a project-scoped API key for that tenant:

from honeyhive import HoneyHiveTracer
import os

TENANT_API_KEYS = {
    "acme": os.environ["HH_API_KEY_TENANT_ACME"],
    "globex": os.environ["HH_API_KEY_TENANT_GLOBEX"],
}

def handle_request(request):
    tracer = HoneyHiveTracer.init(
        api_key=TENANT_API_KEYS[request.tenant_slug],
        source="production"
    )

    tracer.enrich_session({
        "tenant_id": request.tenant_id,
        "plan": request.tenant_plan
    })

    return process_with_tracer(request, tracer)

A/B Testing

Run different configurations side-by-side and compare them with session metadata:

from honeyhive import HoneyHiveTracer, trace
import os

def ab_test_handler(request):
    variant = get_ab_variant(request.user_id)

    tracer = HoneyHiveTracer.init(
        api_key=os.environ["HH_API_KEY"],
        source="production"
    )

    tracer.enrich_session({
        "variant": variant,
        "user_id": request.user_id
    })

    if variant == "A":
        return process_with_gpt4(request, tracer)
    else:
        return process_with_claude(request, tracer)

Feature Isolation

Route different features to projects scoped by separate API keys:

from honeyhive import HoneyHiveTracer, trace
import os

customer_tracer = HoneyHiveTracer.init(
    api_key=os.environ["HH_API_KEY_CUSTOMER_EXPERIENCE"],
    source="production"
)

admin_tracer = HoneyHiveTracer.init(
    api_key=os.environ["HH_API_KEY_INTERNAL_TOOLS"],
    source="production"
)

@trace(tracer=customer_tracer)
def handle_customer_query(query: str) -> str:
    ...

@trace(tracer=admin_tracer)
def generate_internal_report(data: dict) -> str:
    ...

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Per-Request Initialization

For full isolation, initialize a tracer per request:

import os
from honeyhive import HoneyHiveTracer
from flask import Flask, request

app = Flask(__name__)

@app.route("/api/process", methods=["POST"])
def handle():
    tracer = HoneyHiveTracer.init(
        api_key=os.environ["HH_API_KEY"],
        source="api-server"
    )

    tracer.enrich_session({
        "endpoint": request.path,
        "method": request.method
    })

    result = process_request(request.json)
    tracer.force_flush()
    return result

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Thread Safety

HoneyHive uses Python’s contextvars for thread safety. When using thread pools, propagate context explicitly:

from concurrent.futures import ThreadPoolExecutor
import contextvars

def process_in_threads(items, tracer):
    def process_item(item):
        return do_work(item)

    with ThreadPoolExecutor(max_workers=4) as executor:
        futures = [
            executor.submit(contextvars.copy_context().run, process_item, item)
            for item in items
        ]
        return [f.result() for f in futures]

Copy the context for each executor.submit() call. Reusing a single context object across threads causes race conditions. See Multi-Threading for details.

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Best Practices

• Keep tracer instances in a central registry - create once, reuse many times
• Use project-scoped API keys when a tracer should write to a different project
• Store API keys in environment variables or a secret manager, never hard-code them
• Use metadata to differentiate when possible, rather than creating additional tracers
• 2-5 tracers per application is typical and performant

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Performance

Metric	Impact
Memory	~100KB per tracer instance
Network	Batched, async export per tracer
Span Processing	Each tracer has its own processor

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Related

Production Deployment

Initialization patterns for production environments

Multi-Threaded Tracing

Context propagation across threads