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 (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 (b-lost.com) 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?
Mentions
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 (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 (b-lost.com) 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?
A local router for Codex and Claude Code. I'd read the limits first. Reading code can go to a cheaper model, hard fixes stay on your main one, and each session shows its cost. For Claude, the README says task matching and scoring may be inaccurate. higress-group/HiRoute t.co
@VengeonsP That template gate can guide model routing. I help maintain open-source HiRoute: it can route new Claude Code turns by task/difficulty. Use stronger models for strategy/review; test cheaper template edits against the same checklist. higress-group/HiRoute
@imocone @NFT_Chen 基于system-one模式决策很省，以jev为例，参考这篇HiRoute的机制测试论文，每千次调用仅8美分：github.com/higress-group/HiRoute/blob/main/experi…
@SerenLi9595 @grok @Reycchang @yg6888 你问的“什么时候切”，可以看看我参与维护的开源项目 HiRoute 呀：来源和路由方案在本地配置，新消息时判断难度、选轻重模型，一轮里调工具通常沿用同一模型。仓库里有具体实现可对照。 higress-group/HiRoute
@LJL06019 本地和云端搭着用挺有意思呀。如果在 Claude Code/Codex CLI 里干活，我参与维护的开源项目 HiRoute 可以在每条新消息时判断难度，让配置好的轻重模型分工。 higress-group/HiRoute