multi-tenant-llm-hosting

# vllm-deployment.yaml - Multi-model serving with vLLM
apiVersion: apps/v1
kind: Deployment
metadata:
  name: vllm-gpt4o-equivalent
  namespace: llm-serving
  labels:
    app: vllm
    model-tier: premium
spec:
  replicas: 3
  selector:
    matchLabels:
      app: vllm
      model-tier: premium
  template:
    metadata:
      labels:
        app: vllm
        model-tier: premium
      annotations:
        prometheus.io/scrape: "true"
        prometheus.io/port: "8080"
    spec:
      containers:
        - name: vllm
          image: vllm/vllm-openai:v0.4.1
          args:
            - "--model=/models/llama-3.1-70b"
            - "--tensor-parallel-size=2"
            - "--max-model-len=8192"
            - "--gpu-memory-utilization=0.90"
            - "--max-num-seqs=128"
            - "--enable-prefix-caching"
          ports:
            - containerPort: 8000
              name: inference
            - containerPort: 8080
              name: metrics
          resources:
            requests:
              nvidia.com/gpu: 2
              cpu: "8"
              memory: "64Gi"
            limits:
              nvidia.com/gpu: 2
              cpu: "16"
              memory: "128Gi"
          volumeMounts:
            - name: model-weights
              mountPath: /models
              readOnly: true
      volumes:
        - name: model-weights
          persistentVolumeClaim:
            claimName: premium-model-weights
      tolerations:
        - key: nvidia.com/gpu
          operator: Exists
          effect: NoSchedule
      nodeSelector:
        gpu-type: a100
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: vllm-economy
  namespace: llm-serving
  labels:
    app: vllm
    model-tier: economy
spec:
  replicas: 2
  selector:
    matchLabels:
      app: vllm
      model-tier: economy
  template:
    metadata:
      labels:
        app: vllm
        model-tier: economy
    spec:
      containers:
        - name: vllm
          image: vllm/vllm-openai:v0.4.1
          args:
            - "--model=/models/llama-3.1-8b"
            - "--max-model-len=4096"
            - "--gpu-memory-utilization=0.85"
            - "--max-num-seqs=256"
            - "--enable-prefix-caching"
          ports:
            - containerPort: 8000
              name: inference
            - containerPort: 8080
              name: metrics
          resources:
            requests:
              nvidia.com/gpu: 1
              cpu: "4"
              memory: "32Gi"
            limits:
              nvidia.com/gpu: 1
              cpu: "8"
              memory: "64Gi"
          volumeMounts:
            - name: model-weights
              mountPath: /models
              readOnly: true
      volumes:
        - name: model-weights
          persistentVolumeClaim:
            claimName: economy-model-weights
      tolerations:
        - key: nvidia.com/gpu
          operator: Exists
          effect: NoSchedule

# tenant-quotas-configmap.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: tenant-quotas
  namespace: llm-serving
data:
  quotas.yaml: |
    tenants:
      acme-corp:
        tier: enterprise
        models_allowed:
          - llama-3.1-70b
          - llama-3.1-8b
          - nomic-embed-text
        rate_limits:
          requests_per_minute: 300
          tokens_per_minute: 500000
          concurrent_requests: 50
        budget:
          daily_limit_usd: 500.00
          monthly_limit_usd: 10000.00
          alert_threshold_percent: 80
        priority: high
 
      startup-xyz:
        tier: standard
        models_allowed:
          - llama-3.1-8b
          - nomic-embed-text
        rate_limits:
          requests_per_minute: 60
          tokens_per_minute: 100000
          concurrent_requests: 10
        budget:
          daily_limit_usd: 50.00
          monthly_limit_usd: 1000.00
          alert_threshold_percent: 80
        priority: medium
 
      internal-dev:
        tier: free
        models_allowed:
          - llama-3.1-8b
        rate_limits:
          requests_per_minute: 20
          tokens_per_minute: 50000
          concurrent_requests: 5
        budget:
          daily_limit_usd: 10.00
          monthly_limit_usd: 200.00
          alert_threshold_percent: 90
        priority: low

# tenant-namespace.yaml
apiVersion: v1
kind: Namespace
metadata:
  name: tenant-acme-corp
  labels:
    tenant: acme-corp
    isolation: strict
---
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: tenant-isolation
  namespace: tenant-acme-corp
spec:
  podSelector: {}
  policyTypes:
    - Ingress
    - Egress
  ingress:
    - from:
        - namespaceSelector:
            matchLabels:
              name: llm-gateway
  egress:
    - to:
        - namespaceSelector:
            matchLabels:
              name: llm-serving
      ports:
        - port: 8000
          protocol: TCP
    - to:
        - namespaceSelector:
            matchLabels:
              name: kube-dns
      ports:
        - port: 53
          protocol: UDP
---
apiVersion: v1
kind: ResourceQuota
metadata:
  name: tenant-quota
  namespace: tenant-acme-corp
spec:
  hard:
    requests.cpu: "16"
    requests.memory: "64Gi"
    limits.cpu: "32"
    limits.memory: "128Gi"
    requests.nvidia.com/gpu: "4"
    pods: "20"

# gateway_router.py
"""Multi-tenant request router with rate limiting and model routing."""
import time
import json
import redis
from fastapi import FastAPI, HTTPException, Header, Request
from typing import Optional
import httpx
import yaml
 
app = FastAPI()
redis_client = redis.Redis(host="redis", port=6379, decode_responses=True)
 
# Load tenant config
with open("/etc/config/quotas.yaml") as f:
    TENANT_CONFIG = yaml.safe_load(f)["tenants"]
 
MODEL_ENDPOINTS = {
    "llama-3.1-70b": "http://vllm-gpt4o-equivalent:8000",
    "llama-3.1-8b": "http://vllm-economy:8000",
    "nomic-embed-text": "http://embedding-service:8000",
}
 
def check_rate_limit(tenant_id: str, config: dict) -> bool:
    """Check and update rate limit for a tenant."""
    key = f"ratelimit:{tenant_id}:{int(time.time() // 60)}"
    current = redis_client.incr(key)
    if current == 1:
        redis_client.expire(key, 120)
    return current <= config["rate_limits"]["requests_per_minute"]
 
def check_concurrent(tenant_id: str, config: dict) -> bool:
    """Check concurrent request limit."""
    key = f"concurrent:{tenant_id}"
    current = int(redis_client.get(key) or 0)
    return current < config["rate_limits"]["concurrent_requests"]
 
def check_budget(tenant_id: str, config: dict) -> bool:
    """Check if tenant is within daily budget."""
    key = f"spend:{tenant_id}:{time.strftime('%Y-%m-%d')}"
    current_spend = float(redis_client.get(key) or 0)
    return current_spend < config["budget"]["daily_limit_usd"]
 
def record_usage(tenant_id: str, model: str, prompt_tokens: int, completion_tokens: int):
    """Record token usage and cost for billing."""
    # Cost rates per 1K tokens
    rates = {
        "llama-3.1-70b": {"prompt": 0.004, "completion": 0.012},
        "llama-3.1-8b": {"prompt": 0.0005, "completion": 0.0015},
        "nomic-embed-text": {"prompt": 0.0001, "completion": 0.0},
    }
    rate = rates.get(model, {"prompt": 0.001, "completion": 0.003})
    cost = (prompt_tokens * rate["prompt"] + completion_tokens * rate["completion"]) / 1000
 
    # Update daily spend
    spend_key = f"spend:{tenant_id}:{time.strftime('%Y-%m-%d')}"
    redis_client.incrbyfloat(spend_key, cost)
    redis_client.expire(spend_key, 172800)
 
    # Record for billing export
    billing_key = f"billing:{tenant_id}:{time.strftime('%Y-%m')}"
    redis_client.rpush(billing_key, json.dumps({
        "timestamp": time.time(),
        "model": model,
        "prompt_tokens": prompt_tokens,
        "completion_tokens": completion_tokens,
        "cost_usd": cost,
    }))
 
@app.post("/v1/chat/completions")
async def chat_completions(
    request: Request,
    x_tenant_id: str = Header(...),
    x_api_key: str = Header(...),
):
    """Route chat completion request with tenant controls."""
    if x_tenant_id not in TENANT_CONFIG:
        raise HTTPException(status_code=403, detail="Unknown tenant")
 
    config = TENANT_CONFIG[x_tenant_id]
    body = await request.json()
    model = body.get("model", "llama-3.1-8b")
 
    # Check model access
    if model not in config["models_allowed"]:
        raise HTTPException(status_code=403, detail=f"Model {model} not allowed for tenant")
 
    # Check rate limit
    if not check_rate_limit(x_tenant_id, config):
        raise HTTPException(status_code=429, detail="Rate limit exceeded")
 
    # Check concurrent requests
    if not check_concurrent(x_tenant_id, config):
        raise HTTPException(status_code=429, detail="Concurrent request limit exceeded")
 
    # Check budget
    if not check_budget(x_tenant_id, config):
        raise HTTPException(status_code=402, detail="Daily budget exceeded")
 
    # Route to model endpoint
    endpoint = MODEL_ENDPOINTS.get(model)
    if not endpoint:
        raise HTTPException(status_code=404, detail=f"Model {model} not available")
 
    # Track concurrent requests
    concurrent_key = f"concurrent:{x_tenant_id}"
    redis_client.incr(concurrent_key)
 
    try:
        async with httpx.AsyncClient(timeout=120.0) as client:
            response = await client.post(
                f"{endpoint}/v1/chat/completions",
                json=body,
                headers={"Content-Type": "application/json"},
            )
            result = response.json()
 
            # Record usage
            usage = result.get("usage", {})
            record_usage(
                x_tenant_id, model,
                usage.get("prompt_tokens", 0),
                usage.get("completion_tokens", 0),
            )
 
            return result
    finally:
        redis_client.decr(concurrent_key)

# envoy-ratelimit.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: envoy-ratelimit-config
  namespace: llm-serving
data:
  config.yaml: |
    domain: llm-gateway
    descriptors:
      # Per-tenant rate limits
      - key: tenant_id
        value: acme-corp
        rate_limit:
          unit: minute
          requests_per_unit: 300
      - key: tenant_id
        value: startup-xyz
        rate_limit:
          unit: minute
          requests_per_unit: 60
      - key: tenant_id
        value: internal-dev
        rate_limit:
          unit: minute
          requests_per_unit: 20
 
      # Global rate limit as safety net
      - key: global
        rate_limit:
          unit: second
          requests_per_unit: 100

# billing_export.py
"""Export tenant usage data for billing systems."""
import redis
import json
from datetime import datetime, timedelta
from typing import Dict, List
 
redis_client = redis.Redis(host="redis", port=6379, decode_responses=True)
 
def generate_tenant_invoice(tenant_id: str, month: str) -> Dict:
    """Generate monthly invoice for a tenant."""
    billing_key = f"billing:{tenant_id}:{month}"
    records = redis_client.lrange(billing_key, 0, -1)
 
    usage_by_model = {}
    total_cost = 0.0
    total_requests = 0
 
    for record_json in records:
        record = json.loads(record_json)
        model = record["model"]
 
        if model not in usage_by_model:
            usage_by_model[model] = {
                "requests": 0,
                "prompt_tokens": 0,
                "completion_tokens": 0,
                "cost_usd": 0.0,
            }
 
        usage_by_model[model]["requests"] += 1
        usage_by_model[model]["prompt_tokens"] += record["prompt_tokens"]
        usage_by_model[model]["completion_tokens"] += record["completion_tokens"]
        usage_by_model[model]["cost_usd"] += record["cost_usd"]
 
        total_cost += record["cost_usd"]
        total_requests += 1
 
    return {
        "tenant_id": tenant_id,
        "billing_period": month,
        "generated_at": datetime.utcnow().isoformat(),
        "summary": {
            "total_requests": total_requests,
            "total_cost_usd": round(total_cost, 4),
        },
        "usage_by_model": usage_by_model,
    }
 
def get_tenant_spend_today(tenant_id: str) -> float:
    """Get current day spend for budget alerts."""
    key = f"spend:{tenant_id}:{datetime.utcnow().strftime('%Y-%m-%d')}"
    return float(redis_client.get(key) or 0)

# priority-classes.yaml
apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
  name: tenant-enterprise
value: 1000
globalDefault: false
description: "Enterprise tenant workloads"
---
apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
  name: tenant-standard
value: 500
globalDefault: false
description: "Standard tenant workloads"
---
apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
  name: tenant-free
value: 100
globalDefault: false
description: "Free tier tenant workloads"

# tenant-alerts.yaml
groups:
  - name: tenant-alerts
    rules:
      - alert: TenantBudgetWarning
        expr: |
          llm_tenant_daily_spend_usd
          / llm_tenant_daily_budget_usd > 0.80
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "Tenant {{ $labels.tenant }} at 80% of daily budget"
 
      - alert: TenantRateLimitHitting
        expr: |
          rate(llm_rate_limit_rejections_total[5m]) > 1
        for: 5m
        labels:
          severity: info
        annotations:
          summary: "Tenant {{ $labels.tenant }} hitting rate limits"
 
      - alert: TenantErrorRateHigh
        expr: |
          rate(llm_tenant_errors_total[5m])
          / rate(llm_tenant_requests_total[5m]) > 0.10
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "Tenant {{ $labels.tenant }} error rate above 10%"