Handling Serverless on Kubernetes

Serverless computing and Kubernetes represent two powerful approaches to modern application deployment. Whilst Kubernetes excels at container orchestration, serverless architecture abstracts infrastructure management entirely, allowing developers to focus on code rather than servers.

This article explores how to implement serverless workloads on Kubernetes, the frameworks that enable this capability, and when this approach makes sense for your organisation.

If you need help getting started with Kubernetes and implementing serverless functions, book a consultation with our team of cloud experts.

‍

Understanding Serverless Architecture

Serverless architecture enables applications to run without provisioning, maintaining or scaling infrastructure. Code executes only when triggered by events, automatically scaling based on demand and terminating when processing completes. This event-driven model creates significant cost and operational advantages.

Key Characteristics

• Event-driven execution: Functions trigger in response to HTTP requests, queue messages, database changes or scheduled events
• Automatic scaling: Infrastructure scales from zero to handle traffic spikes without manual intervention
• Pay-per-use pricing: Charges apply only for actual compute time, not idle capacity
• No server management: Platform provider handles provisioning, patching and infrastructure maintenance

Why Serverless on Kubernetes?

Kubernetes doesn't provide native serverless capabilities, but specialised frameworks enable serverless deployment patterns on Kubernetes infrastructure. This approach combines serverless benefits with Kubernetes' flexibility and control.

Primary Advantages

• No vendor lock-in: Deploy serverless workloads on any Kubernetes cluster—cloud, on-premises or hybrid environments
• Unified platform: Run both traditional containerised applications and serverless functions on shared infrastructure
• Cost optimisation: Scale-to-zero capabilities eliminate charges for idle resources whilst maintaining rapid response to requests
• Consistent tooling: Leverage existing Kubernetes expertise, monitoring and security tools
• Flexible networking: Integrate serverless functions with existing microservices, databases and service meshes

Leading Serverless Frameworks for Kubernetes

Knative

Knative has emerged as the dominant serverless platform for Kubernetes. Originally developed by Google and now a CNCF project, Knative provides enterprise-grade serverless capabilities with strong industry backing.

Core capabilities:

• Scale-to-zero with rapid cold-start performance
• Concurrency-based autoscaling
• Advanced traffic management (blue-green, canary deployments)
• Comprehensive eventing framework
• Flexible networking options (no longer requires Istio)

Best for: Production environments requiring robust, vendor-neutral serverless with extensive features and strong community support.

OpenFaaS

OpenFaaS emphasises simplicity and developer experience. Its straightforward approach makes serverless accessible without Kubernetes complexity.

Key features:

• Simple deployment via CLI or UI
• Template store for multiple programming languages
• Auto-scaling with Prometheus integration
• Async invocation with automatic retries
• Multi-tenancy support

Best for: Teams wanting rapid serverless adoption without extensive Kubernetes knowledge, particularly for customer-facing extensions and IoT applications.

Fission

Fission delivers industry-leading cold-start performance through pre-warmed container pools, making it ideal for latency-sensitive applications.

Notable capabilities:

• Sub-100ms cold starts through pod pooling
• Live-reload for rapid development iteration
• Native Kubernetes integration using CRDs
• Canary deployment support
• Multiple trigger types (HTTP, timers, message queues)

Best for: Applications where cold-start latency is critical, such as API backends, webhooks and real-time data processing.

Apache OpenWhisk

OpenWhisk offers sophisticated event processing and workflow composition, backed by the Apache Foundation with IBM and Adobe support.

Key concepts:

• Actions, triggers, feeds and rules for complex event handling
• Package system for function composition and reuse
• Multi-language support
• Built-in monitoring and metrics

Best for: Complex event-driven architectures requiring sophisticated trigger mechanisms and workflow orchestration.

The Cold Start Challenge

Cold starts remain the primary technical challenge for serverless on Kubernetes. When functions scale to zero, the first request after idle time incurs latency whilst the platform provisions resources, creates pods and initialises the runtime environment.

Modern Solutions

Significant advances in 2024-2025 have dramatically reduced cold-start impact:

• Predictive scheduling: Machine learning models forecast traffic patterns and pre-warm containers proactively, reducing tail latency by up to 85%
• Profile-guided optimisation: Automated analysis identifies and eliminates unnecessary library loading during initialisation, improving startup times by 2-3x
• Container pooling: Frameworks maintain warm container pools, reducing cold starts from seconds to milliseconds
• Snapshot technology: AWS Lambda SnapStart 2.0 and similar technologies capture initialised application state, effectively eliminating cold starts for supported runtimes
• Granularity-based scheduling: Dynamic scheduling based on function dependencies optimises resource allocation whilst reducing response times by 95%

WebAssembly: The Next Generation

WebAssembly (Wasm) is emerging as a transformative technology for serverless workloads. Wasm modules start in microseconds rather than milliseconds, fundamentally solving the cold-start problem whilst providing enhanced security through sandboxing.

Projects like SpinKube enable WebAssembly workloads on Kubernetes, offering:

• Near-instant startup times (microseconds)
• Significantly smaller binary sizes
• Enhanced security isolation
• Cross-platform portability

Implementation Approaches

Basic Deployment

Unlike traditional Kubernetes deployments requiring detailed YAML manifests, serverless frameworks abstract infrastructure complexity. Functions typically deploy via CLI commands:

# OpenFaaS examplefaas-cli deploy --image=myfunction:latest --name=my-function# Knative examplekn service create my-function --image=myfunction:latest

The framework automatically creates deployments, services, ingress routes and scaling policies.

Modern Deployment Patterns

Production environments increasingly manage serverless functions through GitOps workflows:

• Infrastructure as Code: Define functions in version-controlled repositories
• Automated CI/CD: Build, test and deploy functions through automated pipelines
• Progressive delivery: Implement canary deployments and traffic splitting for safe releases
• Declarative configuration: Tools like Flux and ArgoCD automatically synchronise function definitions from Git

When to Use Serverless on Kubernetes

Ideal Use Cases

• Variable traffic patterns: Applications with sporadic or unpredictable usage benefit from automatic scaling and pay-per-use pricing
• Event-driven architectures: Microservices responding to queue messages, webhooks or database changes
• Rapid development cycles: Projects requiring fast time-to-market without infrastructure management overhead
• Multi-cloud strategies: Organisations avoiding vendor lock-in by maintaining deployment flexibility
• API backends: REST or GraphQL APIs with variable request volumes
• Scheduled tasks: Cron-like jobs and periodic data processing
• Stream processing: Real-time data transformation and analysis

When Traditional Deployments Make More Sense

• Consistent high load: Applications with predictable, sustained traffic may cost more with serverless pay-per-use models
• Long-running processes: Tasks requiring hours of continuous execution suit traditional container deployments better
• Strict latency requirements: Applications where even optimised cold starts (50-100ms) are unacceptable
• Stateful applications: Services requiring persistent connections or in-memory state across requests
• Complex dependencies: Large runtime environments with extensive library requirements increase cold-start overhead

Cost Considerations

Serverless on Kubernetes offers distinct cost profiles:

Cost Advantages

• Zero charges for idle periods through scale-to-zero
• Reduced operational overhead—no infrastructure management
• Efficient resource utilisation through automatic scaling
• Lower total cost of ownership for variable workloads

Cost Considerations

• High-frequency invocations may cost more than dedicated resources
• Cold-start mitigation strategies (warm pools) consume some baseline resources
• Monitoring and observability tools for serverless architectures

Observability and Monitoring

Serverless architectures require different monitoring approaches:

• Cold-start tracking: Monitor initialisation times and frequency
• Invocation metrics: Track request rates, execution duration and error rates
• Cost allocation: Attribute compute costs to specific functions and teams
• Distributed tracing: Follow requests across multiple serverless functions
• Resource utilisation: Optimise memory allocation and execution time

Tools like Prometheus, Grafana, Jaeger and OpenTelemetry integrate well with Kubernetes-based serverless platforms.

Security Considerations

Serverless on Kubernetes inherits Kubernetes security capabilities whilst introducing specific considerations:

• Function isolation: Each function runs in isolated containers with resource limits
• Network policies: Control traffic between functions and other services
• Secret management: Integrate with Kubernetes secrets and external secret managers
• Authentication and authorisation: Implement function-level access controls
• Runtime security: Monitor function behaviour for anomalies

Getting Started

Begin with a framework evaluation:

1. Assess requirements: Determine latency tolerance, traffic patterns and integration needs
2. Start with Knative or OpenFaaS: Both offer strong documentation and community support
3. Deploy proof-of-concept: Test with non-critical workloads to understand behaviour
4. Measure performance: Track cold starts, execution times and costs
5. Implement observability: Deploy monitoring before production workloads
6. Optimise iteratively: Tune memory allocation, concurrency and scaling parameters

The Future: AI and Edge Computing

Serverless on Kubernetes is evolving to address emerging use cases:

• AI/ML inference: Serverless GPU functions for on-demand model serving
• Edge computing: Lightweight Kubernetes distributions running serverless at the edge
• WebAssembly adoption: Wasm-based serverless eliminating cold-start concerns
• Enhanced event sources: Deeper integration with cloud services and IoT platforms

Conclusion

Serverless on Kubernetes combines the operational efficiency of serverless computing with the flexibility and control of Kubernetes. Modern frameworks have addressed early limitations—particularly cold starts—making this deployment model viable for production workloads.

Success requires matching workload characteristics to serverless strengths: variable traffic, event-driven patterns and rapid development cycles benefit most. For applications with these characteristics, serverless on Kubernetes delivers reduced costs, faster deployment and improved developer productivity without vendor lock-in.

If you need help with Kubernetes and implementing serverless functions, book a consultation with our team of cloud experts.

‍