KServe Consulting

KServe is a Kubernetes-native model serving platform for deploying and operating machine learning inference services. It is commonly used by MLOps and platform engineering teams that need a consistent way to expose models as scalable endpoints while aligning with cluster governance, networking, and observability practices. KServe helps standardize how models move from training to production, reducing ad-hoc deployment patterns across teams.

It typically runs in Kubernetes clusters and integrates with common CI/CD and GitOps workflows, enabling teams to manage model rollout and lifecycle alongside other cloud-native services. For broader MLOps context, see MLOps Engineering.

• Kubernetes-based inference services with autoscaling
• Support for multiple model frameworks and serving runtimes
• Traffic splitting for safe rollouts (e.g., canary deployments)
• Model versioning and lifecycle management patterns
• Integration with service mesh and observability tooling

• MLOps allows for streamlined model deployment by standardizing the pipeline from development to production.
• The use of MLOps encourages effective communication between data scientists, engineers, and other stakeholders which enhances decision-making processes and results in robust machine learning applications.
• With the incorporation of concepts like continuous integration, delivery, and training, MLOps ensures that models are always updated, thoroughly tested, and smoothly deployed.
• Automated quality assurance and validation of machine learning models are inherent features of MLOps, which improve the reliability and performance of the models in production.
• MLOps frameworks are equipped with capabilities for ongoing monitoring of model performance and system health, facilitating early detection and resolution of any potential issues.
• MLOps ensures that all models conform to necessary regulatory and governance requirements, a critical consideration in highly-regulated sectors like finance and healthcare.
• By creating an efficient system for model operationalization and delivery, MLOps effectively addresses the 'last mile' problem of machine learning implementation.
• Model reproducibility is promoted by MLOps and it also offers a version control system for ML models which is vital for debugging and model improvements.
• MLOps aids in efficient management of computational resources which in turn helps in reducing operational costs.
• By providing a controlled environment for ML model deployment, MLOps mitigates risks associated with the introduction of new models or updates in the production environment.

KServe is used to deploy, scale, and operate machine learning inference services on Kubernetes with a consistent, production-ready interface for multiple model frameworks and runtimes. It helps teams standardize model serving while keeping operations aligned with Kubernetes-native patterns.

• Kubernetes-native inference services that fit existing cluster networking, security, and deployment workflows.
• Autoscaling and scale-to-zero support to reduce cost for spiky or low-traffic models while maintaining responsiveness.
• Standardized inference API and service definitions that simplify onboarding and reduce bespoke serving implementations.
• Multiple runtime options, including prebuilt predictors and custom containers, to support diverse model stacks without rewriting infrastructure.
• Canary rollouts and traffic splitting to safely introduce new model versions and reduce deployment risk.
• GPU and accelerator friendly scheduling via Kubernetes resource requests and node selection for performance-sensitive workloads.
• Centralized observability hooks for metrics and logging to support SLOs, debugging, and capacity planning.
• Composable with CI/CD and GitOps tooling so model serving changes can be reviewed, audited, and promoted across environments.
• Multi-tenant and governance-friendly patterns using Kubernetes namespaces, RBAC, and policy controls.
• Integrates well with common MLOps stacks such as Kubeflow for end-to-end pipelines and serving workflows.

KServe is a strong fit when Kubernetes is the standard runtime platform and the goal is consistent, governed model serving across teams. Trade-offs include added operational complexity compared to fully managed services, and careful tuning is often needed for cold-start latency, GPU utilization, and autoscaling behavior.

Common alternatives include Seldon Core, BentoML, Ray Serve, and managed endpoints from AWS SageMaker, Google Vertex AI, and Azure Machine Learning.

Our experience with KServe helped us build practical knowledge, reusable delivery patterns, and operational tooling that we use to help clients run reliable model serving on Kubernetes across development, staging, and production.

Some of the things we did include:

• Implemented KServe for real-time inference on Kubernetes with standardized model deployment templates and clear promotion paths between environments.
• Integrated KServe with Istio for ingress, traffic management, canary rollouts, and mTLS, including safe rollout and rollback procedures.
• Hardened KServe deployments with Kubernetes RBAC, network policies, and secrets management, and aligned runtime permissions with least-privilege access.
• Automated KServe model deployments through GitOps using Argo CD, including environment overlays, policy checks, and drift detection.
• Built CI/CD pipelines that package model artifacts, publish container images, and trigger KServe updates with validation gates and reproducible releases.
• Set up observability for KServe inference services using Prometheus metrics and Grafana dashboards, with SLOs and alerting tuned for latency and error budgets.
• Optimized performance and cost by right-sizing resources, configuring autoscaling, and testing concurrency/latency trade-offs under realistic load patterns.
• Designed multi-tenant patterns for shared clusters, including namespace isolation, quota management, and standardized onboarding for new teams and models.
• Implemented resilience practices such as pod disruption budgets, readiness/liveness probes, and failure-mode testing to improve availability during upgrades and node churn.
• Created runbooks and trained platform and ML teams on operating KServe day-to-day, covering incident response, release processes, and common troubleshooting workflows.

This experience helped us accumulate significant knowledge across multiple KServe use-cases, and it enables us to deliver high-quality KServe setups that are secure, observable, and maintainable in production.

Some of the things we can help you do with KServe include:

• Assess your current Kubernetes model serving approach and deliver a findings report covering reliability, latency, scaling, and operational risk.
• Create an adoption roadmap and reference architecture for standardized inference services across teams and environments.
• Implement production-grade KServe deployments on Kubernetes, including InferenceService patterns, traffic management, and rollout strategies.
• Automate delivery with GitOps using Argo CD and CI pipelines for repeatable, auditable promotion to production.
• Establish security and compliance guardrails (RBAC, network policies, image provenance, secrets handling) to support governed model releases.
• Optimize cost and performance with right-sized resources, autoscaling, GPU/CPU scheduling strategies, and request/response tuning for target SLAs.
• Integrate observability for inference (metrics, logs, traces, SLOs) and build operational dashboards and alerting for on-call readiness.
• Harden multi-tenant operations with namespaces, quotas, admission controls, and standardized templates to reduce platform friction.
• Troubleshoot production issues such as cold starts, scaling instability, timeouts, and model artifact loading bottlenecks.
• Enable teams through hands-on training, runbooks, and operational playbooks for day-2 support and continuous improvement.