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Ai Inference Service Mesh

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sickn33
ai-inference-service-mesh

Use service mesh patterns for AI inference traffic management, mTLS, canary releases, policy enforcement, and cross-cluster resilience.

Overview

Publishersickn33
Repositoryagentic-awesome-skills
Skill nameai-inference-service-mesh
Stars
47.1K
Forks
6.9K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by sickn33 on GitHub. Read the source before you install it.

Installation

Install the Ai Inference Service Mesh AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/sickn33/agentic-awesome-skills/tree/main/plugins/agentic-awesome-skills-claude/skills/ai-inference-service-mesh
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
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
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ai Inference Service Mesh in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ai Inference Service Mesh on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ai Inference Service Mesh is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

bash
# 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

Core Patterns

mTLS Strict Mode Cluster-Wide

yaml
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

AuthorizationPolicy Per Service Account

yaml
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

yaml
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

yaml
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

DestinationRule with Subsets

yaml
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

Circuit Breaking for Inference Backends

yaml
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

Retry Budget for Streaming Requests

yaml
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

Resilience

Locality-Aware Routing

yaml
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

Observability

yaml
# 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

Kiali Dashboard Check

bash
# 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

Pitfalls 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.

Frequently asked questions

What does the Ai Inference Service Mesh AI skill do?

Use service mesh patterns for AI inference traffic management, mTLS, canary releases, policy enforcement, and cross-cluster resilience.

Why use Ai Inference Service Mesh on TypingMind?

Because you install it once and use it with any model. Ai Inference Service Mesh is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ai Inference Service Mesh in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sickn33/agentic-awesome-skills/tree/main/plugins/agentic-awesome-skills-claude/skills/ai-inference-service-mesh. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ai Inference Service Mesh?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ai Inference Service Mesh?

As many as you like. As long as a model supports skills, you can use Ai Inference Service Mesh with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ai Inference Service Mesh AI skill free?

Yes. It is published on GitHub by sickn33 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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