Conduct periodic access reviews and certifications. Implement access governance and recertification workflows. Use when managing access compliance.
中文简介正在准备中。当前显示原文说明。
Use service mesh patterns for AI inference traffic management, mTLS, canary releases, policy enforcement, and cross-cluster resilience.
中文简介正在准备中。当前显示原文说明。
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Apply Istio/Linkerd mesh controls to secure and optimize east-west AI traffic across inference microservices.
# Install Istio with production profile
istioctl install --set profile=default \
--set meshConfig.accessLogFile=/dev/stdout \
--set meshConfig.defaultConfig.holdApplicationUntilProxyStarts=true
# Label inference namespace for sidecar injection
kubectl create namespace ai-inference
kubectl label namespace ai-inference istio-injection=enabled
# Verify installation
istioctl verify-install
istioctl analyze -n ai-inference
apiVersion: security.istio.io/v1beta1
kind: PeerAuthentication
metadata:
name: default
namespace: istio-system
spec:
mtls:
mode: STRICT
---
# Namespace-level override if needed for gradual rollout
apiVersion: security.istio.io/v1beta1
kind: PeerAuthentication
metadata:
name: ai-inference-mtls
namespace: ai-inference
spec:
mtls:
mode: STRICT
portLevelMtls:
# gRPC inference port
8081:
mode: STRICT
# Prometheus metrics port - allow plaintext scraping
9090:
mode: PERMISSIVE
apiVersion: security.istio.io/v1beta1
kind: AuthorizationPolicy
metadata:
name: model-server-access
namespace: ai-inference
spec:
selector:
matchLabels:
app: model-server
action: ALLOW
rules:
- from:
- source:
principals:
- "cluster.local/ns/ai-inference/sa/api-gateway"
- "cluster.local/ns/ai-inference/sa/orchestrator"
to:
- operation:
methods: ["POST"]
paths: ["/v1/predict", "/v1/embeddings", "/v2/models/*/infer"]
---
apiVersion: security.istio.io/v1beta1
kind: AuthorizationPolicy
metadata:
name: deny-external-to-retriever
namespace: ai-inference
spec:
selector:
matchLabels:
app: vector-retriever
action: DENY
rules:
- from:
- source:
notNamespaces: ["ai-inference"]
apiVersion: networking.istio.io/v1alpha3
kind: ServiceEntry
metadata:
name: openai-api
namespace: ai-inference
spec:
hosts:
- api.openai.com
ports:
- number: 443
name: https
protocol: TLS
resolution: DNS
location: MESH_EXTERNAL
---
apiVersion: networking.istio.io/v1alpha3
kind: DestinationRule
metadata:
name: openai-api-tls
namespace: ai-inference
spec:
host: api.openai.com
trafficPolicy:
tls:
mode: SIMPLE
connectionPool:
http:
h2UpgradePolicy: UPGRADE
tcp:
maxConnections: 50
---
apiVersion: security.istio.io/v1beta1
kind: AuthorizationPolicy
metadata:
name: restrict-egress
namespace: ai-inference
spec:
action: ALLOW
rules:
- to:
- operation:
hosts:
- "api.openai.com"
- "models.anthropic.com"
- "*.blob.core.windows.net"
apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
metadata:
name: model-server
namespace: ai-inference
spec:
hosts:
- model-server
http:
# Route by header for explicit model version selection
- match:
- headers:
x-model-version:
exact: "v2-experimental"
route:
- destination:
host: model-server
subset: v2-experimental
timeout: 120s
# Route by header for A/B test cohort
- match:
- headers:
x-ab-cohort:
exact: "treatment"
route:
- destination:
host: model-server
subset: v2-experimental
weight: 100
timeout: 120s
# Default traffic split: 90/10 canary
- route:
- destination:
host: model-server
subset: v1-stable
weight: 90
- destination:
host: model-server
subset: v2-experimental
weight: 10
timeout: 60s
retries:
attempts: 2
perTryTimeout: 30s
retryOn: unavailable,resource-exhausted
apiVersion: networking.istio.io/v1alpha3
kind: DestinationRule
metadata:
name: model-server
namespace: ai-inference
spec:
host: model-server
trafficPolicy:
connectionPool:
http:
h2UpgradePolicy: UPGRADE
maxRequestsPerConnection: 100
tcp:
maxConnections: 200
connectTimeout: 5s
loadBalancer:
simple: LEAST_REQUEST
subsets:
- name: v1-stable
labels:
version: v1
trafficPolicy:
connectionPool:
http:
maxRequestsPerConnection: 50
- name: v2-experimental
labels:
version: v2
trafficPolicy:
connectionPool:
http:
maxRequestsPerConnection: 20
apiVersion: networking.istio.io/v1alpha3
kind: DestinationRule
metadata:
name: model-server-circuit-breaker
namespace: ai-inference
spec:
host: model-server
trafficPolicy:
connectionPool:
tcp:
maxConnections: 100
connectTimeout: 10s
http:
http1MaxPendingRequests: 50
http2MaxRequests: 200
maxRequestsPerConnection: 10
maxRetries: 3
outlierDetection:
consecutive5xxErrors: 3
interval: 15s
baseEjectionTime: 30s
maxEjectionPercent: 50
minHealthPercent: 30
splitExternalLocalOriginErrors: true
---
# Separate circuit breaker for the vector retriever
apiVersion: networking.istio.io/v1alpha3
kind: DestinationRule
metadata:
name: vector-retriever-circuit-breaker
namespace: ai-inference
spec:
host: vector-retriever
trafficPolicy:
connectionPool:
tcp:
maxConnections: 300
http:
http1MaxPendingRequests: 200
http2MaxRequests: 500
outlierDetection:
consecutive5xxErrors: 5
interval: 10s
baseEjectionTime: 15s
maxEjectionPercent: 30
apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
metadata:
name: streaming-inference
namespace: ai-inference
spec:
hosts:
- model-server
http:
# Streaming endpoint: no retries, long timeout
- match:
- uri:
prefix: /v1/stream
route:
- destination:
host: model-server
subset: v1-stable
timeout: 300s
retries:
attempts: 0
# Embeddings endpoint: safe to retry, short timeout
- match:
- uri:
prefix: /v1/embeddings
route:
- destination:
host: model-server
subset: v1-stable
timeout: 15s
retries:
attempts: 3
perTryTimeout: 5s
retryOn: 5xx,reset,connect-failure,retriable-status-codes
apiVersion: networking.istio.io/v1alpha3
kind: DestinationRule
metadata:
name: model-server-locality
namespace: ai-inference
spec:
host: model-server
trafficPolicy:
loadBalancer:
localityLbSetting:
enabled: true
distribute:
- from: "us-east-1/us-east-1a/*"
to:
"us-east-1/us-east-1a/*": 80
"us-east-1/us-east-1b/*": 20
failover:
- from: us-east-1
to: us-west-2
outlierDetection:
consecutive5xxErrors: 3
interval: 10s
baseEjectionTime: 30s
# Telemetry resource for custom metrics on inference services
apiVersion: telemetry.istio.io/v1alpha1
kind: Telemetry
metadata:
name: inference-telemetry
namespace: ai-inference
spec:
metrics:
- providers:
- name: prometheus
overrides:
- match:
metric: REQUEST_DURATION
mode: CLIENT_AND_SERVER
tagOverrides:
model_name:
operation: UPSERT
value: "request.headers['x-model-name']"
tenant_id:
operation: UPSERT
value: "request.headers['x-tenant-id']"
tracing:
- providers:
- name: zipkin
randomSamplingPercentage: 10.0
# Port-forward Kiali
kubectl port-forward svc/kiali -n istio-system 20001:20001 &
# Verify mesh health via API
curl -s http://localhost:20001/kiali/api/namespaces/ai-inference/health | jq .
# Check proxy sync status
istioctl proxy-status -n ai-inference
# Debug a specific pod sidecar config
istioctl proxy-config routes deploy/model-server -n ai-inference -o json
istioctl proxy-config cluster deploy/model-server -n ai-inference
holdApplicationUntilProxyStarts causing race conditions on startup还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
Conduct periodic access reviews and certifications. Implement access governance and recertification workflows. Use when managing access compliance.
中文简介正在准备中。当前显示原文说明。
Build automated evaluation suites for AI agents using golden datasets, rubrics, and regression gates. Use when shipping agent features, validating prompt changes, or gating deployments on quality.
中文简介正在准备中。当前显示原文说明。
Instrument AI agents with tracing, token metrics, latency, and cost visibility. Use for reliability and debugging.
中文简介正在准备中。当前显示原文说明。
Secure AI agents against prompt injection, tool abuse, and data exfiltration with defense-in-depth controls. Use when building, deploying, or hardening agentic AI systems that invoke tools, access data, or interact with production infrastructure.
中文简介正在准备中。当前显示原文说明。
Secure AI coding agents (Claude Code, Cursor, Codex, Copilot) with permission boundaries, secret protection, code review gates, and safe sandbox configurations for team environments.
中文简介正在准备中。当前显示原文说明。
Orchestrate AI/ML pipelines for data ingestion, model training, batch inference, and RAG indexing using Prefect, Airflow, or Dagster. Build reliable, observable, and retriable workflows for production AI systems.
中文简介正在准备中。当前显示原文说明。