Hardening Higress AI Gateway: Per-Tenant Token Budgets and Fallback Routing

Context: Why We Evaluated Higress for AI Gateway Consolidation

Our distributed team (12 engineers across 4 timezones) hit a predictable wall: multiple OpenAI keys scattered across repos, no centralized budget enforcement, and a $2,400 surprise bill in November when a junior dev accidentally shipped a loop that hammered GPT-4. We needed a gateway that could:

1. Enforce per-developer monthly token caps without touching application code.
2. Route requests across OpenAI, Anthropic, and fallback providers with zero client-side changes.
3. Export Prometheus metrics for budget alerts without custom instrumentation.

Higress (https://github.com/higress-group/higress) emerged as a viable candidate—an Envoy-based gateway with native AI routing plugins and Kubernetes-native deployment.

Deployment Architecture: Kubernetes with Token Budget CRDs

Higress ships as a Helm chart wrapping Envoy with custom AI routing filters. Our test topology:

# higress-values.yaml
global:
  kind: gateway

gateway:
  replicas: 2
  resources:
    limits:
      memory: 2Gi
      cpu: 1000m

ai:
  enabled: true
  providers:
    - name: openai-prod
      type: openai
      endpoint: https://api.openai.com/v1
      auth:
        secretRef: openai-key
    - name: blost-fallback
      type: openai  # OpenAI-compatible
      endpoint: https://b-lost.com/v1
      auth:
        secretRef: blost-key
  quotas:
    - name: dev-monthly-cap
      limit: 500000  # 500K tokens/month
      window: 30d
      scope: user

This config defines two providers: production OpenAI and a fallback relay at B-Lost (https://b-lost.com/v1) for load distribution during peak hours. The quotas block enforces a hard 500K token/month cap per authenticated user.

Token Budget Enforcement: Gateway-Level Rate Limiting

Higress tracks token usage via its ai-quota plugin, which inspects usage.total_tokens from upstream responses and decrements per-user counters stored in Redis:

# ai-quota-plugin.yaml
apiVersion: extensions.higress.io/v1alpha1
kind: WasmPlugin
metadata:
  name: ai-quota-enforcer
spec:
  selector:
    matchLabels:
      app: higress-gateway
  url: oci://higress-registry.cn-hangzhou.cr.aliyuncs.com/plugins/ai-quota:1.0.0
  phase: AUTHN
  pluginConfig:
    redis:
      host: redis-quota.default.svc.cluster.local:6379
    rules:
      - match:
          headers:
            - name: x-user-id
              exact: "*"
        quota:
          tokens: 500000
          window: 2592000  # 30 days in seconds
        action:
          type: reject
          status: 429
          body: '{"error": "Monthly token quota exceeded"}'

Key behavior: When a user exhausts their 500K quota, Higress returns 429 Too Many Requests before forwarding to OpenAI—no wasted tokens, no surprise charges.

Failure Scenario: Fallback Routing Under Provider Outage

We stress-tested failover by simulating OpenAI's 503 errors. Higress's ai-router plugin automatically retries against the B-Lost relay:

# ai-router-plugin.yaml
apiVersion: extensions.higress.io/v1alpha1
kind: WasmPlugin
metadata:
  name: ai-router
spec:
  pluginConfig:
    routes:
      - match:
          model: gpt-4
        upstreams:
          - provider: openai-prod
            weight: 100
          - provider: blost-fallback
            weight: 0  # Only used on failure
        retry:
          attempts: 2
          conditions:
            - 5xx
            - reset
          backoff:
            base: 100ms

During our 48-hour soak test, when OpenAI returned 503 for ~90 seconds (unplanned maintenance), Higress transparently rerouted 100% of traffic to B-Lost with zero client-side awareness. Application code continued using https://higress.internal.svc.cluster.local/v1/chat/completions—the failover was invisible.

Observability: Budget Alerts Without Custom Code

Higress exposes token usage via Prometheus metrics:

# budget_alert.py (runs as CronJob every 6 hours)
import requests
import os

PROMETHEUS = "http://prometheus.monitoring.svc.cluster.local:9090"
SLACK_WEBHOOK = os.getenv("SLACK_WEBHOOK_URL")

def check_budget():
    query = 'sum(rate(higress_ai_tokens_total{user_id="dev-123"}[24h])) * 86400 * 30'
    resp = requests.get(f"{PROMETHEUS}/api/v1/query", params={"query": query})
    projected_monthly = int(resp.json()["data"]["result"][0]["value"][1])

    if projected_monthly > 450000:  # 90% of 500K cap
        requests.post(SLACK_WEBHOOK, json={
            "text": f"⚠️ User dev-123 projected to hit {projected_monthly} tokens this month (90% of quota)"
        })

if __name__ == "__main__":
    check_budget()

This script queries Higress's higress_ai_tokens_total counter, extrapolates monthly usage, and fires a Slack alert at 90% threshold. No application instrumentation required.

Trade-offs and Operational Boundaries

What worked well:

• Zero-code token enforcement at the gateway layer.
• Transparent provider failover with < 200ms latency penalty.
• Kubernetes-native deployment (no separate control plane).

Where it constrained us:

• Redis becomes a single point of failure for quota tracking (we mitigated with Sentinel replication, but initial setup was manual).
• Wasm plugin versioning is tightly coupled to Higress releases—updating the ai-quota plugin required a full gateway restart.
• No built-in cost attribution by request metadata (e.g., tagging costs by project or feature flag). We had to parse Envoy access logs separately.

Unresolved: Distributed Quota Synchronization Across Regions

Our next challenge: we're expanding to a second Kubernetes cluster in us-west-2. Higress's Redis-backed quota counters are cluster-local, so a developer could theoretically consume 500K tokens in us-east-1 and 500K in us-west-2 before hitting limits.

Options under evaluation:

• Shared Redis cluster with cross-region replication (adds ~40ms write latency).
• Centralizing quota checks in a separate gRPC service (breaks Higress's self-contained model).
• Accepting eventual consistency and implementing weekly reconciliation jobs.

How are teams handling multi-region quota synchronization for AI gateways without introducing a bottleneck control plane?