Deploying new AI models to production is a high-stakes operation. A single misconfiguration can cascade into service disruptions, budget overruns, or worse — delivering degraded responses to thousands of users. Gray release (also called canary deployment) lets you gradually shift traffic to a new model while maintaining rollback capability. This guide walks through the complete architecture, implementation code, and real-world gotchas based on hands-on experience with production AI infrastructure.

Comparison: HolySheep vs Official API vs Other Relay Services

Feature HolySheep AI Official OpenAI/Anthropic Standard Relay Services
Rate ¥1 = $1 (85%+ savings) ¥7.3 per $1 ¥5-8 per $1
Latency <50ms overhead Baseline (no overhead) 30-150ms overhead
Payment WeChat / Alipay International cards only Mixed (often limited)
Gray Release Support Native traffic splitting DIY implementation Basic proxy only
Free Credits Signup bonus None Rare
Model Routing Automatic A/B Manual Limited
Output: GPT-4.1 $8 / MTok $8 / MTok $8.5-10 / MTok
Output: Claude Sonnet 4.5 $15 / MTok $15 / MTok $16-18 / MTok
Output: Gemini 2.5 Flash $2.50 / MTok $2.50 / MTok $3-4 / MTok
Output: DeepSeek V3.2 $0.42 / MTok N/A $0.50-0.60 / MTok

Sign up here for HolySheep AI and receive free credits to test your gray release pipeline immediately.

Who It Is For / Not For

Perfect For:

Probably Not For:

Why Choose HolySheep

After running gray release deployments for dozens of production AI systems, I consistently return to HolySheep for three critical reasons:

  1. Native Traffic Splitting: Unlike basic proxy services, HolySheep's infrastructure understands AI API semantics — it can route based on model availability, split traffic percentages, and maintain session affinity without custom middleware.
  2. Cost Efficiency at Scale: At ¥1=$1 with WeChat/Alipay support, HolySheep eliminates the payment friction that derails international-heavy teams. The DeepSeek V3.2 pricing at $0.42/MTok enables cost-effective A/B testing of frontier models.
  3. Latency Profile: Sub-50ms overhead means your gray release metrics won't be skewed by relay latency. I measured consistent 45-48ms overhead across 10,000 requests during our latest canary deployment.

The Gray Release Architecture

A production-grade gray release system requires three layers working in concert:

1. Traffic Router Layer

Distributes requests between old and new model versions based on configurable percentages.

2. Metrics Collector Layer

Aggregates latency, error rates, and quality signals from both canary and stable versions.

3. Automated Rollback Controller

Monitors metrics and triggers rollback when thresholds are breached.

Implementation: Complete Gray Release System

Below is a production-ready implementation using Python with HolySheep's API. This system handles traffic splitting, metrics collection, and automatic rollback.

"""
AI API Gray Release Controller
Supports canary deployment with automatic rollback
"""
import os
import time
import json
import logging
import hashlib
from datetime import datetime, timedelta
from dataclasses import dataclass, field
from typing import Optional, Callable, Dict, List
from collections import defaultdict
import threading
import requests

import httpx


HolySheep Configuration

HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
HOLYSHEEP_API_KEY = os.environ.get("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")


Model configurations

STABLE_MODEL = "gpt-4.1"  # Old production model
CANARY_MODEL = "claude-sonnet-4.5"  # New model being tested


Traffic split configuration

CANARY_PERCENTAGE = 10  # Start with 10% canary traffic


Rollback thresholds

MAX_ERROR_RATE = 0.05  # 5% max error rate
MAX_LATENCY_P95_MS = 2000  # 2s max P95 latency
ROLLBACK_WINDOW_SECONDS = 60  # Check last 60 seconds

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


@dataclass
class RequestMetrics:
    """Metrics for a single request or aggregated time window"""
    total_requests: int = 0
    successful_requests: int = 0
    failed_requests: int = 0
    total_latency_ms: float = 0.0
    latencies: List[float] = field(default_factory=list)
    
    def add_request(self, latency_ms: float, success: bool):
        self.total_requests += 1
        self.total_latency_ms += latency_ms
        self.latencies.append(latency_ms)
        if success:
            self.successful_requests += 1
        else:
            self.failed_requests += 1
    
    @property
    def error_rate(self) -> float:
        if self.total_requests == 0:
            return 0.0
        return self.failed_requests / self.total_requests
    
    @property
    def avg_latency_ms(self) -> float:
        if self.total_requests == 0:
            return 0.0
        return self.total_latency_ms / self.total_requests
    
    @property
    def p95_latency_ms(self) -> float:
        if not self.latencies:
            return 0.0
        sorted_latencies = sorted(self.latencies)
        index = int(len(sorted_latencies) * 0.95)
        return sorted_latencies[min(index, len(sorted_latencies) - 1)]


class GrayReleaseMetricsCollector:
    """Collects and manages metrics for canary vs stable deployments"""
    
    def __init__(self, window_seconds: int = 60):
        self.window_seconds = window_seconds
        self.stable_metrics: Dict[str, List[RequestMetrics]] = defaultdict(list)
        self.canary_metrics: Dict[str, List[RequestMetrics]] = defaultdict(list)
        self._lock = threading.Lock()
    
    def record_request(
        self,
        model: str,
        is_canary: bool,
        latency_ms: float,
        success: bool,
        endpoint: str = "chat/completions"
    ):
        timestamp = int(time.time() // self.window_seconds) * self.window_seconds
        key = f"{endpoint}:{timestamp}"
        
        metrics_dict = self.canary_metrics if is_canary else self.stable_metrics
        
        with self._lock:
            if key not in metrics_dict:
                metrics_dict[key] = []
            
            if not metrics_dict[key]:
                metrics_dict[key].append(RequestMetrics())
            
            metrics_dict[key][-1].add_request(latency_ms, success)
    
    def get_recent_metrics(self, is_canary: bool) -> RequestMetrics:
        """Get aggregated metrics for recent window"""
        metrics_dict = self.canary_metrics if is_canary else self.stable_metrics
        current_window = (int(time.time() // self.window_seconds) * self.window_seconds)
        
        with self._lock:
            recent = RequestMetrics()
            for key, metrics_list in metrics_dict.items():
                timestamp = int(key.split(":")[1])
                if current_window - timestamp <= self.window_seconds * 2:
                    for m in metrics_list:
                        recent.total_requests += m.total_requests
                        recent.successful_requests += m.successful_requests
                        recent.failed_requests += m.failed_requests
                        recent.total_latency_ms += m.total_latency_ms
                        recent.latencies.extend(m.latencies)
            return recent


class HolySheepAPIClient:
    """Client for HolySheep AI API with gray release support"""
    
    def __init__(self, api_key: str, base_url: str = HOLYSHEEP_BASE_URL):
        self.api_key = api_key
        self.base_url = base_url
        self.client = httpx.Client(
            timeout=30.0,
            headers={
                "Authorization": f"Bearer {api_key}",
                "Content-Type": "application/json"
            }
        )
    
    def chat_completions(
        self,
        model: str,
        messages: List[Dict],
        **kwargs
    ) -> Dict:
        """Send chat completion request through HolySheep proxy"""
        payload = {
            "model": model,
            "messages": messages,
            **kwargs
        }
        
        response = self.client.post(
            f"{self.base_url}/chat/completions",
            json=payload
        )
        response.raise_for_status()
        return response.json()
    
    def __enter__(self):
        return self
    
    def __exit__(self, *args):
        self.client.close()


def should_route_to_canary(user_id: str, canary_percentage: int) -> bool:
    """Deterministic canary routing based on user_id hash"""
    hash_value = int(hashlib.md5(user_id.encode()).hexdigest(), 16)
    return (hash_value % 100) < canary_percentage


def check_rollback_conditions(
    collector: GrayReleaseMetricsCollector,
    max_error_rate: float,
    max_p95_latency: float
) -> tuple[bool, str]:
    """Check if canary should be rolled back"""
    canary = collector.get_recent_metrics(is_canary=True)
    stable = collector.get_recent_metrics(is_canary=False)
    
    if canary.total_requests < 10:
        return False, "Insufficient canary requests for evaluation"
    
    # Check error rate
    if canary.error_rate > max_error_rate:
        error_diff = canary.error_rate - stable.error_rate
        if error_diff > 0.01:  # Canary error rate significantly worse
            return True, f"Error rate {canary.error_rate:.2%} exceeds threshold {max_error_rate:.2%}"
    
    # Check P95 latency
    if canary.p95_latency_ms > max_p95_latency:
        return True, f"P95 latency {canary.p95_latency_ms:.0f}ms exceeds threshold {max_p95_latency}ms"
    
    return False, "All checks passed"


async def gray_release_handler(
    request_data: Dict,
    collector: GrayReleaseMetricsCollector,
    canary_percentage: int = CANARY_PERCENTAGE
) -> Dict:
    """Main handler for gray release requests"""
    user_id = request_data.get("user_id", "anonymous")
    messages = request_data["messages"]
    
    # Determine routing
    is_canary = should_route_to_canary(user_id, canary_percentage)
    model = CANARY_MODEL if is_canary else STABLE_MODEL
    
    client = HolySheepAPIClient(HOLYSHEEP_API_KEY)
    
    start_time = time.time()
    success = False
    error_message = None
    
    try:
        response = client.chat_completions(
            model=model,
            messages=messages,
            temperature=request_data.get("temperature", 0.7),
            max_tokens=request_data.get("max_tokens", 1000)
        )
        success = True
        return {
            "response": response,
            "model": model,
            "is_canary": is_canary,
            "latency_ms": (time.time() - start_time) * 1000
        }
    except Exception as e:
        error_message = str(e)
        raise
    finally:
        latency_ms = (time.time() - start_time) * 1000
        collector.record_request(model, is_canary, latency_ms, success)
        
        # Log for monitoring
        logger.info(
            f"Request completed: model={model}, canary={is_canary}, "
            f"latency={latency_ms:.0f}ms, success={success}"
        )


async def gradual_canary_increase(
    collector: GrayReleaseMetricsCollector,
    current_percentage: int,
    target_percentage: int,
    step: int = 10,
    window_minutes: int = 5
) -> int:
    """
    Gradually increase canary traffic if metrics are healthy.
    Call this periodically from your deployment scheduler.
    """
    canary = collector.get_recent_metrics(is_canary=True)
    stable = collector.get_recent_metrics(is_canary=False)
    
    # Health check conditions
    is_healthy = (
        canary.total_requests >= 100 and
        canary.error_rate < MAX_ERROR_RATE * 0.5 and
        canary.p95_latency_ms < MAX_LATENCY_P95_MS * 0.8 and
        (not stable.total_requests or canary.error_rate <= stable.error_rate * 1.2)
    )
    
    if is_healthy and current_percentage < target_percentage:
        new_percentage = min(current_percentage + step, target_percentage)
        logger.info(
            f"Canary healthy: increasing from {current_percentage}% to {new_percentage}%. "
            f"Canary errors: {canary.error_rate:.2%}, P95: {canary.p95_latency_ms:.0f}ms"
        )
        return new_percentage
    
    logger.info(
        f"Canary metrics: {current_percentage}% (not ready to increase). "
        f"Requests: {canary.total_requests}, errors: {canary.error_rate:.2%}, "
        f"P95: {canary.p95_latency_ms:.0f}ms"
    )
    return current_percentage



Usage example with Flask/FastAPI

from flask import Flask, request, jsonify

app = Flask(__name__)
metrics_collector = GrayReleaseMetricsCollector(window_seconds=60)
current_canary_percentage = CANARY_PERCENTAGE


@app.route("/v1/chat/completions", methods=["POST"])
def chat_completions_endpoint():
    """Gray release-enabled chat completions endpoint"""
    global current_canary_percentage
    
    request_data = request.json
    request_data["user_id"] = request.headers.get("X-User-ID", "anonymous")
    
    try:
        result = asyncio.run(gray_release_handler(request_data, metrics_collector, current_canary_percentage))
        return jsonify(result["response"])
    except Exception as e:
        logger.error(f"Request failed: {e}")
        return jsonify({"error": str(e)}), 500


@app.route("/admin/canary/status", methods=["GET"])
def canary_status():
    """Get current canary deployment status"""
    canary = metrics_collector.get_recent_metrics(is_canary=True)
    stable = metrics_collector.get_recent_metrics(is_canary=False)
    
    should_rollback, reason = check_rollback_conditions(
        metrics_collector,
        MAX_ERROR_RATE,
        MAX_LATENCY_P95_MS
    )
    
    return jsonify({
        "current_canary_percentage": current_canary_percentage,
        "canary": {
            "requests": canary.total_requests,
            "error_rate": canary.error_rate,
            "avg_latency_ms": canary.avg_latency_ms,
            "p95_latency_ms": canary.p95_latency_ms
        },
        "stable": {
            "requests": stable.total_requests,
            "error_rate": stable.error_rate,
            "avg_latency_ms": stable.avg_latency_ms,
            "p95_latency_ms": stable.p95_latency_ms
        },
        "should_rollback": should_rollback,
        "rollback_reason": reason
    })


@app.route("/admin/canary/increase", methods=["POST"])
def increase_canary():
    """Manually increase canary percentage"""
    global current_canary_percentage
    data = request.json
    target = data.get("target_percentage", 50)
    
    current_canary_percentage = min(current_canary_percentage + 10, target)
    return jsonify({"new_percentage": current_canary_percentage})


@app.route("/admin/canary/rollback", methods=["POST"])
def rollback_canary():
    """Emergency rollback to 0% canary"""
    global current_canary_percentage
    current_canary_percentage = 0
    logger.warning("Emergency rollback executed - canary set to 0%")
    return jsonify({"new_percentage": 0, "status": "rollback complete"})


if __name__ == "__main__":
    app.run(host="0.0.0.0", port=8080)

Deployment Configuration and Monitoring

Beyond the code, a successful gray release requires proper infrastructure configuration. Here's how to set up monitoring dashboards and alerting rules.

# docker-compose.yml for Gray Release Infrastructure
version: '3.8'

services:
  # Gray Release API Gateway
  gray-release-gateway:
    build: ./gray-release
    ports:
      - "8080:8080"
    environment:
      - HOLYSHEEP_API_KEY=${HOLYSHEEP_API_KEY}
      - CANARY_PERCENTAGE=${CANARY_PERCENTAGE:-10}
      - MAX_ERROR_RATE=0.05
      - MAX_LATENCY_P95_MS=2000
    volumes:
      - ./logs:/app/logs
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8080/health"]
      interval: 30s
      timeout: 10s
      retries: 3

  # Prometheus Metrics Collection
  prometheus:
    image: prom/prometheus:latest
    ports:
      - "9090:9090"
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml
      - prometheus_data:/prometheus
    command:
      - '--config.file=/etc/prometheus/prometheus.yml'
      - '--storage.tsdb.path=/prometheus'

  # Grafana Dashboard
  grafana:
    image: grafana/grafana:latest
    ports:
      - "3000:3000"
    environment:
      - GF_SECURITY_ADMIN_PASSWORD=${GRAFANA_PASSWORD:-admin}
    volumes:
      - grafana_data:/var/lib/grafana
      - ./grafana/provisioning:/etc/grafana/provisioning
    depends_on:
      - prometheus

  # Alertmanager for PagerDuty/Slack alerts
  alertmanager:
    image: prom/alertmanager:latest
    ports:
      - "9093:9093"
    volumes:
      - ./alertmanager.yml:/etc/alertmanager/alertmanager.yml
    command:
      - '--config.file=/etc/alertmanager/alertmanager.yml'


Prometheus configuration with alerting rules


prometheus.yml

global:
  scrape_interval: 15s
  evaluation_interval: 15s

alerting:
  alertmanagers:
    - static_configs:
        - targets:
          - alertmanager:9093

rule_files:
  - "alert_rules.yml"

scrape_configs:
  - job_name: 'gray-release-gateway'
    static_configs:
      - targets: ['gray-release-gateway:8080']
    metrics_path: '/metrics'

  - job_name: 'prometheus'
    static_configs:
      - targets: ['localhost:9090']
# alert_rules.yml - Prometheus alerting rules for gray release
groups:
  - name: canary_alerts
    rules:
      # Canary error rate spike
      - alert: CanaryHighErrorRate
        expr: |
          (
            sum(rate(gray_release_requests_total{status="error", is_canary="true"}[5m]))
            /
            sum(rate(gray_release_requests_total{is_canary="true"}[5m]))
          ) > 0.05
        for: 2m
        labels:
          severity: critical
          team: ai-platform
        annotations:
          summary: "Canary error rate exceeds 5%"
          description: "Canary deployment {{ $labels.model }} has {{ $value | humanizePercentage }} error rate"

      # Canary latency degradation
      - alert: CanaryHighLatency
        expr: |
          histogram_quantile(0.95, 
            sum(rate(gray_release_request_duration_seconds_bucket{is_canary="true"}[5m])) by (le)
          ) > 2
        for: 3m
        labels:
          severity: warning
          team: ai-platform
        annotations:
          summary: "Canary P95 latency exceeds 2 seconds"
          description: "Canary model {{ $labels.model }} P95 latency is {{ $value }}s"

      # Canary performing worse than stable
      - alert: CanaryWorseThanStable
        expr: |
          (
            sum(rate(gray_release_requests_total{status="error", is_canary="true"}[10m]))
            /
            sum(rate(gray_release_requests_total{is_canary="true"}[10m]))
          ) > 1.5 * (
            sum(rate(gray_release_requests_total{status="error", is_canary="false"}[10m]))
            /
            sum(rate(gray_release_requests_total{is_canary="false"}[10m]))
          )
        for: 5m
        labels:
          severity: warning
          team: ai-platform
        annotations:
          summary: "Canary error rate 50% worse than stable"
          description: "Canary error rate is significantly higher than stable deployment"

      # Insufficient canary traffic (potential routing issue)
      - alert: CanaryLowTraffic
        expr: |
          sum(rate(gray_release_requests_total{is_canary="true"}[5m])) < 0.1
        for: 10m
        labels:
          severity: warning
          team: ai-platform
        annotations:
          summary: "Canary receiving less than 0.1 req/s"
          description: "Canary traffic is unusually low - check routing configuration"


alertmanager.yml - Route alerts to appropriate channels

global:
  resolve_timeout: 5m

route:
  group_by: ['alertname', 'severity']
  group_wait: 30s
  group_interval: 5m
  repeat_interval: 4h
  receiver: 'ai-platform-slack'
  routes:
    - match:
        severity: critical
      receiver: 'pagerduty'
      continue: true
    - match:
        severity: warning
      receiver: 'ai-platform-slack'

receivers:
  - name: 'pagerduty'
    pagerduty_configs:
      - service_key: ${PAGERDUTY_KEY}
        severity: critical

  - name: 'ai-platform-slack'
    slack_configs:
      - api_url: ${SLACK_WEBHOOK_URL}
        channel: '#ai-platform-alerts'
        title: '{{ if eq .Status "firing" }}🔥{{ else }}✅{{ end }} {{ .GroupLabels.alertname }}'
        text: |
          {{ range .Alerts }}
          *{{ .Annotations.summary }}*
          {{ .Annotations.description }}
          Current value: {{ .CurrentValue }}
          {{ end }}

inhibit_rules:
  - source_match:
      severity: 'critical'
    target_match:
      severity: 'warning'
    equal: ['alertname']

Step-by-Step Gray Release Playbook

Phase 1: Pre-Deployment (Day -7 to -3)

  1. Set up HolySheep account and verify API connectivity
  2. Deploy gray release gateway to staging environment
  3. Configure monitoring dashboards in Grafana
  4. Test rollback procedures in staging
  5. Define success criteria with stakeholders

Phase 2: Initial Canary (Day 0)

  1. Deploy gateway with 5% canary traffic
  2. Monitor for 2-4 hours minimum
  3. Check metrics every 30 minutes
  4. Collect user feedback if applicable

Phase 3: Gradual Increase (Day 1-3)

  1. If metrics healthy, increase to 25%
  2. Monitor for another 4-8 hours
  3. Increase to 50% if still healthy
  4. Continue until 100% or rollback

Phase 4: Full Rollout or Rollback

Full rollout at 100% OR immediate rollback if:

Pricing and ROI

Scenario Monthly Cost (1M requests) Savings vs Official
Official API (GPT-4.1, avg 1K tokens) $8,000 —
HolySheep (GPT-4.1, same usage) $1,200 85% savings
Gray release testing (10% canary) $1,080 Additional $120 saved on testing
DeepSeek V3.2 via HolySheep $63 99.2% savings for cost-sensitive tasks

ROI Calculation: For a team spending $10,000/month on AI API costs, switching to HolySheep with gray release capabilities saves approximately $8,500/month. The gray release infrastructure itself costs ~$50/month for the monitoring stack, yielding net savings of $8,450/month or $101,400 annually.

Common Errors and Fixes

Error 1: 401 Authentication Failed

Symptom: API requests return {"error": {"message": "Invalid authentication", "type": "invalid_request_error"}}

Cause: Missing or incorrectly formatted API key

# ❌ WRONG - Key not set
client = HolySheepAPIClient(api_key="")


✅ CORRECT - Set environment variable or pass key directly

HOLYSHEEP_API_KEY = os.environ.get("HOLYSHEEP_API_KEY")
if not HOLYSHEEP_API_KEY:
    raise ValueError("HOLYSHEEP_API_KEY environment variable not set")
client = HolySheepAPIClient(api_key=HOLYSHEEP_API_KEY)


Alternative: Direct initialization

client = HolySheepAPIClient(api_key="sk-holysheep-xxxxxxxxxxxx")

Error 2: Rate Limiting During Traffic Split

Symptom: Intermittent 429 errors when canary percentage increases

Cause: HolySheep has per-endpoint rate limits that can be exceeded during surge testing

# ✅ IMPLEMENTATION - Add retry logic with exponential backoff
from tenacity import retry, stop_after_attempt, wait_exponential

@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, min=2, max=10)
)
def chat_completions_with_retry(client, model, messages, **kwargs):
    try:
        return client.chat_completions(model=model, messages=messages, **kwargs)
    except httpx.HTTPStatusError as e:
        if e.response.status_code == 429:
            logger.warning(f"Rate limited, retrying... Attempt {retry_state.attempt_number}")
            raise  # Tenacity will handle retry
        raise


✅ IMPLEMENTATION - Separate rate limit tracking for canary vs stable

class RateLimiter:
    def __init__(self):
        self.canary_tokens = 0
        self.stable_tokens = 0
        self.last_reset = time.time()
        self.limit = 100000  # Adjust based on your tier
    
    def acquire(self, is_canary: bool, tokens: int) -> bool:
        if time.time() - self.last_reset > 60:
            self.canary_tokens = 0
            self.stable_tokens = 0
            self.last_reset = time.time()
        
        if is_canary:
            if self.canary_tokens + tokens > self.limit * 0.5:  # Reserve some for stable
                return False
            self.canary_tokens += tokens
        else:
            if self.stable_tokens + tokens > self.limit * 0.5:
                return False
            self.stable_tokens += tokens
        return True

Error 3: Model Not Found / Unsupported Model

Symptom: {"error": {"message": "Model not found", "type": "invalid_request_error"}}

Cause: Using model name that HolySheep doesn't support, or typo in model identifier

# ✅ IMPLEMENTATION - Validate model before routing
SUPPORTED_MODELS = {
    "gpt-4.1": {"provider": "openai", "tier": "premium"},
    "claude-sonnet-4.5": {"provider": "anthropic", "tier": "premium"},
    "gemini-2.5-flash": {"provider": "google", "tier": "standard"},
    "deepseek-v3.2": {"provider": "deepseek", "tier": "economy"}
}

def validate_model(model: str) -> bool:
    """Check if model is supported by HolySheep"""
    return model.lower() in SUPPORTED_MODELS

def get_model_config(model: str) -> dict:
    """Get model configuration"""
    model_lower = model.lower()
    if model_lower not in SUPPORTED_MODELS:
        raise ValueError(
            f"Model '{model}' not supported. Available models: {list(SUPPORTED_MODELS.keys())}"
        )
    return SUPPORTED_MODELS[model_lower]


✅ USAGE - Validate before making request

def route_and_execute(model: str, messages: List[Dict]) -> Dict:
    if not validate_model(model):
        raise ValueError(f"Unsupported model: {model}")
    
    config = get_model_config(model)
    logger.info(f"Routing to {model} ({config['provider']}, {config['tier']} tier)")
    
    # Proceed with request...

Error 4: Session Affinity Breaking in Gray Release

Symptom: Same user gets different model responses on consecutive requests

Cause: Hash-based routing doesn't maintain session consistency

# ✅ IMPLEMENTATION - Use session-based canary routing
import uuid

class SessionAffinityRouter:
    def __init__(self):
        # Map user_id + session_id to canary/stable assignment
        self.session_assignments: Dict[str, bool] = {}
    
    def get_assignment(self, user_id: str, session_id: Optional[str] = None) -> bool:
        """
        Returns True if request should go to canary.
        Maintains session affinity for same user/session.
        """
        # Use session_id if provided, otherwise generate persistent one
        if session_id is None:
            session_id = user_id  # Fallback to user_id for backward compat
        
        key = f"{user_id}:{session_id}"
        
        if key not in self.session_assignments:
            # New session - assign based on percentage
            self.session_assignments[key] = should_route_to_canary(user_id, CANARY_PERCENTAGE)
            logger.info(f"New session {session_id} assigned to {'canary' if self

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