About the service
I deploy and configure a production service mesh based on Istio in your Kubernetes cluster so traffic between services becomes secure, observable, and managed—without changing a single line of your application code. The sidecar model does all the heavy lifting: I inject Envoy proxies, bring up the control plane, and give you a mesh where routing, retries, timeouts, and encryption are described by configuration, not baked into services. Your team keeps shipping features while the platform underneath quietly gets guarantees it never had before.
I focus on the spots where teams usually trip up. I enable mTLS across the mesh so every call is authenticated and encrypted by default, then layer authorization policies on top: who is allowed to talk to whom. For releases I set up canary and blue-green deployments with weighted routing—the new version gets one percent of traffic first, proves it works, and gradually takes more, or instantly rolls back if errors rise. Fault injection, circuit breaking, and retry budgets make the system resilient to the unstable dependencies that are inevitable at scale.
Observability is included. You get golden signal dashboards, distributed traces that follow a request through all microservices, and a live call graph. I document the gateways, policies, and deployment playbook, size sidecars so the mesh doesn't eat your cluster, and train your team on day-to-day operations. The result is traffic becomes something you understand and change with confidence.
— mTLS, authorization, and peer authentication policies
— Canary, blue-green, and weighted traffic routing
— Ingress and egress gateways, timeouts, retries, circuit breaking
— Metrics, distributed tracing, and service graph
I focus on the spots where teams usually trip up. I enable mTLS across the mesh so every call is authenticated and encrypted by default, then layer authorization policies on top: who is allowed to talk to whom. For releases I set up canary and blue-green deployments with weighted routing—the new version gets one percent of traffic first, proves it works, and gradually takes more, or instantly rolls back if errors rise. Fault injection, circuit breaking, and retry budgets make the system resilient to the unstable dependencies that are inevitable at scale.
Observability is included. You get golden signal dashboards, distributed traces that follow a request through all microservices, and a live call graph. I document the gateways, policies, and deployment playbook, size sidecars so the mesh doesn't eat your cluster, and train your team on day-to-day operations. The result is traffic becomes something you understand and change with confidence.
— mTLS, authorization, and peer authentication policies
— Canary, blue-green, and weighted traffic routing
— Ingress and egress gateways, timeouts, retries, circuit breaking
— Metrics, distributed tracing, and service graph
