ai-inference-service-mesh skill
Use service mesh patterns for AI inference traffic management, mTLS,
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Install the ai-inference-service-mesh skill
A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.
git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git /tmp/agentic-awesome-skills mkdir -p ~/.claude/skills cp -r /tmp/agentic-awesome-skills/plugins/agentic-awesome-skills-claude/skills/ai-inference-service-mesh ~/.claude/skills/ai-inference-service-mesh
available in every project
In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub
The instructions your agent would load
SKILL.md as published, without the frontmatter. Read it on GitHub
AI Inference Service Mesh
Apply Istio/Linkerd mesh controls to secure and optimize east-west AI traffic across inference microservices.
Why Mesh for AI
- Enforce mTLS between gateway, retriever, reranker, and model services
- Apply fine-grained traffic policies without app code changes
- Run progressive delivery for model-serving backends
- Observe latency hops for retrieval + generation chains
- Route inference requests by model version, tenant, or priority tier
- Protect expensive GPU-backed services from cascading failures
Prerequisites
# 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-inferenceCore Patterns
mTLS Strict Mode Cluster-Wide
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: PERMISSIVEAuthorizationPolicy Per Service Account
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"]Egress Policy for Approved Model Endpoints
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"Traffic Management
VirtualService for A/B Model Testing
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-exhaustedDestinationRule with Subsets
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: 20Circuit Breaking for Inference Backends
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: 30Retry Budget for Streaming Requests
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-codesResilience
Locality-Aware Routing
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: 30sObservability
# 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: 10.0Kiali Dashboard Check
# 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-inferencePitfalls to Avoid
- Aggressive timeouts that break streaming responses -- set 300s+ for generation endpoints
- Blanket retries that amplify expensive generation calls -- disable retries on non-idempotent routes
- Missing identity boundaries between tenant-facing and internal services
- Forgetting to exempt health check and metrics ports from strict mTLS
- Setting outlier ejection too aggressively on small pools (maxEjectionPercent too high)
- Not using holdApplicationUntilProxyStarts causing race conditions on startup
Related Skills
- service-mesh (service-mesh) - Foundational mesh concepts
- llm-gateway (llm-gateway) - North-south API gateway controls
- opentelemetry (opentelemetry) - End-to-end tracing and metrics
When to Use
- You are provisioning, configuring, or troubleshooting the infrastructure component covered by this skill (servers, storage, databases, networking, cloud, local AI).
Limitations
- Infrastructure commands can disrupt services: confirm target host/scope and have backups/snapshots before mutating state.
- Docs-only import: upstream scripts and templates not bundled.
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