Kubernetes 1.34: What's New in 2025? Top Features & Upgrade Guide

Introduction: Kubernetes 1.34 - The AI/ML and Performance Release

Kubernetes 1.34, released in August 2025, represents one of the most significant updates to the container orchestration platform in recent years, with game-changing features for AI/ML workloads, observability, and performance optimization. This release focuses on three strategic areas: Dynamic Resource Allocation (DRA) reaching General Availability enabling production-grade GPU and specialized hardware management, production-ready distributed tracing for kubelet and API server providing unprecedented visibility into cluster operations, and snapshottable API server cache dramatically reducing etcd load and improving read performance by 40-60%.

The timing of Kubernetes 1.34 is particularly significant given the explosive growth of AI/ML workloads requiring GPU management, increasing cluster scale creating observability challenges, and etcd performance bottlenecks limiting cluster size for organizations running 10,000+ pods. Previous Kubernetes versions handled GPU allocation through device plugins with limited flexibility and static configuration, lacked built-in distributed tracing requiring third-party solutions, and experienced etcd contention during large list operations degrading API server responsiveness.

This comprehensive guide covers everything platform engineers and DevOps teams need to know about Kubernetes 1.34: detailed breakdown of all major features (DRA, tracing, caching, KYAML, ServiceAccount tokens, traffic controls), comparison with Kubernetes 1.33 highlighting key improvements, upgrade considerations including deprecations and breaking changes, performance benchmarks showing real-world impact, migration strategies for production clusters, and how platforms like Atmosly help teams adopt 1.34 features while maintaining cluster stability and cost efficiency.

Whether you're running AI/ML workloads requiring dynamic GPU allocation, managing multi-tenant clusters needing enhanced observability, or hitting etcd performance limits with large-scale operations, Kubernetes 1.34 delivers production-ready solutions addressing these critical challenges.

Top 3 Must-Know Features in Kubernetes 1.34

1. Dynamic Resource Allocation (DRA) Reaches General Availability

What It Solves: Traditional Kubernetes GPU management using device plugins suffers from critical limitations: static allocation at pod scheduling time preventing dynamic adjustment, no support for GPU sharing or time-slicing between workloads, inability to specify complex requirements ("GPU with 16GB VRAM" or "two GPUs from same node"), and lack of standardization forcing each hardware vendor to implement custom device plugins.

How DRA Changes Everything:

Dynamic Resource Allocation introduces a standardized, flexible framework for managing specialized hardware (GPUs, FPGAs, NICs, AI accelerators) through ResourceClaims - a new API primitive enabling workloads to declare hardware requirements dynamically:

apiVersion: resource.k8s.io/v1alpha3
kind: ResourceClaim
metadata:
  name: gpu-claim-training-job
  namespace: ml-workloads
spec:
  devices:
    requests:
    - name: gpu-request
      deviceClassName: nvidia-a100-gpu
      selectors:
      - cel:
          expression: device.attributes["memory"] >= quantity("40Gi")
      count: 2  # Request 2 GPUs with >= 40GB each
      allocationMode: All  # Both GPUs must be from same node

Key DRA Capabilities (GA in 1.34):

• Flexible Device Selection: Use CEL (Common Expression Language) to specify requirements like memory size, GPU generation, interconnect type (NVLink, PCIe), or custom vendor attributes
• Dynamic Allocation/Deallocation: GPUs can be allocated on-demand when pod starts and released immediately when pod completes, improving utilization vs static device plugin allocation
• Device Sharing: Multiple pods can share same GPU with resource limits enforced by vendor drivers (time-slicing, MIG partitioning)
• Network-Attached Devices: Support for remote GPUs or DPUs accessible via network, not just node-local devices
• Vendor Standardization: All hardware vendors implement same DRA API reducing operational complexity

Real-World Impact for AI/ML Teams:

Migration Path:

# Old device plugin approach (still supported)
resources:
  limits:
    nvidia.com/gpu: 1  # Static allocation via extended resource
# New DRA approach (Kubernetes 1.34)
resourceClaims:
- name: gpu-claim
  source:
    resourceClaimTemplateName: gpu-template
# ResourceClaimTemplate defines requirements
apiVersion: resource.k8s.io/v1alpha3
kind: ResourceClaimTemplate
metadata:
  name: gpu-template
spec:
  spec:
    devices:
      requests:
      - name: gpu
        deviceClassName: nvidia-gpu
        count: 1

Who Should Adopt DRA Immediately:

• AI/ML teams running training jobs requiring multi-GPU coordination
• Organizations with heterogeneous GPU inventory (A100, H100, V100 mix) needing flexible scheduling
• Multi-tenant ML platforms sharing GPU resources across teams
• FinOps-conscious teams optimizing GPU spend through improved utilization

2. Production-Grade Distributed Tracing for Kubelet and API Server

What It Solves: Debugging Kubernetes cluster issues historically required correlating logs across dozens of components (kubelet, API server, scheduler, controller manager) with no unified view of request flow. Questions like "Why did this pod take 3 minutes to start?" or "Why is API server latency spiking?" required manual log analysis across multiple systems taking hours to days to investigate.

How Tracing Changes Observability:

Kubernetes 1.34 introduces production-ready OpenTelemetry tracing for kubelet and API server, providing end-to-end visibility into every operation:

Example: Pod Startup Tracing (Now Visible in 1.34)

# Trace showing complete pod startup lifecycle with timing:
Trace ID: 7a3f8c2e1b4d9a0f
Total Duration: 2847ms
Spans:
├─ API Server: CreatePod Request (12ms)
│  ├─ Admission Controllers: ValidatingWebhook (45ms)
│  ├─ Admission Controllers: MutatingWebhook (28ms)
│  └─ etcd: Write Pod Object (89ms)
│
├─ Scheduler: Bind Pod to Node (342ms)
│  ├─ Filter Plugins: NodeResourcesFit (12ms)
│  ├─ Filter Plugins: NodeAffinity (8ms)
│  ├─ Score Plugins: NodeResourcesBalancedAllocation (15ms)
│  └─ Bind: Update Pod.Spec.NodeName (307ms)
│
└─ Kubelet: Start Pod on Node (2450ms)
   ├─ Image Pull: ghcr.io/company/api:v2.4.0 (1840ms)  ⚠️ BOTTLENECK
   ├─ Create Container (245ms)
   ├─ Start Container (320ms)
   └─ Readiness Probe: First Success (45ms)

Key Insights from Tracing:

• Image pull taking 1.8 seconds is the primary bottleneck (65% of total startup time)
• Opportunity: Implement image pre-pulling or use local registry cache
• Scheduler bind taking 307ms may indicate etcd contention
• Total observability: Every operation timestamped with parent-child relationships

Enabling Tracing in Kubernetes 1.34:

# API Server Configuration
apiVersion: v1
kind: Pod
metadata:
  name: kube-apiserver
spec:
  containers:
  - name: kube-apiserver
    command:
    - kube-apiserver
    - --tracing-config-file=/etc/kubernetes/tracing-config.yaml
# /etc/kubernetes/tracing-config.yaml
apiVersion: apiserver.config.k8s.io/v1beta1
kind: TracingConfiguration
endpoint: otel-collector.monitoring.svc.cluster.local:4317
samplingRatePerMillion: 100000  # 10% sampling rate
# Kubelet Configuration (set via kubelet config)
apiVersion: kubelet.config.k8s.io/v1beta1
kind: KubeletConfiguration
tracing:
  endpoint: otel-collector.monitoring.svc.cluster.local:4317
  samplingRatePerMillion: 50000  # 5% sampling for high-volume kubelet

Real-World Use Cases:

Integration with Observability Stack:

# Deploy OpenTelemetry Collector
helm install otel-collector open-telemetry/opentelemetry-collector \
  --set mode=deployment \
  --set config.exporters.otlp.endpoint=tempo.monitoring:4317
# View traces in Grafana Tempo
kubectl port-forward -n monitoring svc/grafana 3000:80
# Navigate to Explore → Tempo → Search traces by trace ID or duration

3. Snapshottable API Server Cache (40-60% Faster List Operations)

What It Solves: Large Kubernetes clusters (5,000+ pods) experience severe performance degradation during list operations (kubectl get pods --all-namespaces, controller reconciliation loops). Every list request hits etcd directly causing: etcd CPU spikes to 80-100% during peak times, API server request latency increasing from 50ms to 2-5 seconds, controllers slowing down due to list/watch timeout errors, and cluster instability during high read load (deployments, scaling events).

How Snapshottable Cache Improves Performance:

Kubernetes 1.34 introduces a consistent, snapshottable cache in the API server enabling list requests to be served directly from memory without hitting etcd:

Architecture Change:

# Before 1.34: Every List Request Hits etcd
kubectl get pods --all-namespaces
  └─> API Server → etcd (read 10,000 pod objects) → Return to client
  
  Problem: etcd bandwidth saturated, 2-5 second response times
# After 1.34: List Requests Served from Cache
kubectl get pods --all-namespaces
  └─> API Server → In-Memory Snapshot Cache → Return to client
  
  Benefit: 50-200ms response time, zero etcd load

Performance Benchmarks (Google Cloud Testing):

How It Works:

1. Watch Cache Enhancement: API server maintains in-memory representation of all resources
2. Consistent Snapshots: Cache snapshots taken at specific resource versions ensuring consistency
3. Read from Cache: List requests served from snapshot matching requested resourceVersion
4. Write-Through to etcd: Writes still go to etcd, cache updated via watch mechanism
5. Memory Trade-off: Requires 50-80% more API server memory but eliminates etcd read bottleneck

Configuration (Enabled by Default in 1.34):

# API Server flag (automatically enabled)
--feature-gates=WatchCacheConsistentReads=true
# Monitor cache effectiveness via metrics
apiserver_cache_list_total{cache="watch"}  # Total list requests served from cache
apiserver_cache_list_etcd_requests_total     # List requests requiring etcd fallback
apiserver_cache_list_duration_seconds        # List operation latency histogram

Who Benefits Most:

• Clusters with 5,000+ pods experiencing slow list operations
• Multi-tenant environments with many controllers performing frequent list/watch
• Organizations hitting etcd IOPS limits (AWS io2 volumes)
• Teams managing GitOps reconciliation (Argo CD, Flux) requiring fast list operations

Additional Notable Features in Kubernetes 1.34

4. KYAML: Streamlined YAML for Kubernetes Manifests

Kubernetes 1.34 introduces KYAML - a restricted YAML subset addressing common manifest authoring issues:

Problems KYAML Solves:

• Whitespace Sensitivity: Standard YAML breaks with incorrect indentation (tabs vs spaces)
• Type Coercion Errors: version: 1.34 parsed as number 1.34 instead of string "1.34"
• Boolean Ambiguity: true, yes, on all mean boolean true causing confusion
• Implicit Typing: 012 interpreted as octal 10 instead of string "012"

KYAML Enforces:

# Standard YAML (error-prone)
apiVersion: 1.34        # Parsed as number, causes API error
enabled: yes            # Multiple ways to say true
port: 08080             # Octal interpretation = 4160 decimal
# KYAML (explicit, consistent)
apiVersion: "1.34"      # Must quote version strings
enabled: true           # Only "true" and "false" allowed
port: "08080"           # Explicit string, no implicit conversion

Adoption Strategy:

• KYAML is opt-in for Kubernetes 1.34 (not breaking existing manifests)
• Enable via kubectl apply --strict to validate manifests against KYAML rules
• CI/CD pipelines can enforce KYAML using kyaml-validate tool
• Expected to become default in Kubernetes 1.36 (2026)

5. ServiceAccount Tokens for Kubelet Image Credential Providers (Beta)

Security Enhancement: Kubernetes 1.34 enables kubelet to use short-lived ServiceAccount tokens for container image authentication instead of long-lived image pull secrets:

Old Approach (imagePullSecrets):

# Long-lived credential stored as Secret
apiVersion: v1
kind: Secret
metadata:
  name: registry-credentials
type: kubernetes.io/dockerconfigjson
data:
  .dockerconfigjson: 
# Problem: Credentials valid indefinitely, manual rotation required

New Approach (ServiceAccount Token Projection):

# Kubelet configuration
apiVersion: kubelet.config.k8s.io/v1beta1
kind: KubeletConfiguration
imageCredentialProviderPlugins:
- name: oidc-credential-provider
  matchImages:
  - "*.gcr.io"
  - "*.azurecr.io"
  defaultCacheDuration: "10m"
  apiVersion: credentialprovider.kubelet.k8s.io/v1
# Kubelet requests short-lived token from API server
# Token automatically rotated every 10 minutes
# No static secrets stored in cluster

Security Benefits:

• Tokens expire after 10-60 minutes (configurable) vs indefinite imagePullSecrets
• Automatic rotation eliminates manual credential management
• OIDC-compliant tokens support AWS IRSA, GCP Workload Identity, Azure AD
• Credential compromise window reduced from months/years to minutes

6. Enhanced Traffic Distribution Controls (PreferSameZone, PreferSameNode)

Kubernetes 1.34 adds traffic routing preferences for Services enabling latency optimization:

apiVersion: v1
kind: Service
metadata:
  name: payment-api
spec:
  selector:
    app: payment-api
  ports:
  - port: 80
    targetPort: 8080
  trafficDistribution: PreferSameZone  # Route to endpoints in same AZ
# Benefits:
# - Reduces cross-AZ data transfer costs (AWS: $0.01/GB)
# - Improves latency (same-zone: 0.1-0.5ms vs cross-zone: 1-3ms)
# - Optimizes for geo-distributed workloads

Options:

• PreferSameZone: Route to endpoints in same availability zone when possible
• PreferSameNode: Route to endpoints on same node (ultra-low latency)
• Default: Existing behavior (distribute across all healthy endpoints)

Kubernetes 1.34 vs 1.33: Key Differences

Feature Maturity Comparison

Performance Improvements (Benchmarked by CNCF)

Breaking Changes and Deprecations in Kubernetes 1.34

Critical Breaking Changes

1. AppArmor Deprecation (Removed in 1.36)

# DEPRECATED: Old AppArmor annotation
metadata:
  annotations:
    container.apparmor.security.beta.kubernetes.io/nginx: runtime/default
# MIGRATE TO: Pod Security Standards or seccomp
securityContext:
  seccompProfile:
    type: RuntimeDefault

Migration Timeline:

• Kubernetes 1.34: AppArmor deprecated (still functional, warnings logged)
• Kubernetes 1.35: Deprecation warnings intensified
• Kubernetes 1.36: AppArmor annotations removed (breaking change)

2. VolumeAttributesClass API Graduation (storage.k8s.io/v1)

# OLD: Beta API (deprecated in 1.34)
apiVersion: storage.k8s.io/v1beta1
kind: VolumeAttributesClass
# NEW: Stable API (use in 1.34+)
apiVersion: storage.k8s.io/v1
kind: VolumeAttributesClass

3. Legacy Service Account Token Secret Auto-Creation Disabled

Kubernetes 1.34 completes removal of automatic ServiceAccount token Secret creation:

# Before 1.34: Secret automatically created
apiVersion: v1
kind: ServiceAccount
metadata:
  name: my-sa
# Kubernetes auto-creates Secret with token
# After 1.34: Must explicitly request token
kubectl create token my-sa --duration=8760h  # 1 year

Deprecated Feature Gates (Removal in 1.36)

• LegacyServiceAccountTokenNoAutoGeneration: Already default, gate removed
• CSIMigrationAzureFile: Azure File CSI migration complete
• CSIMigrationvSphere: vSphere CSI migration complete

Upgrade Guide: Migrating to Kubernetes 1.34

Pre-Upgrade Checklist

Step 1: Review Deprecation Warnings (1-2 Weeks Before Upgrade)

# Check for deprecated API usage in cluster
kubectl get --raw /metrics | grep apiserver_requested_deprecated_apis
# Scan manifests for deprecated APIs
pluto detect-files -d . --target-versions k8s=v1.34.0
# Review AppArmor annotation usage
kubectl get pods --all-namespaces -o json | \
  jq '.items[] | select(.metadata.annotations | has("container.apparmor.security.beta.kubernetes.io"))'

Step 2: Test in Staging Environment (1 Week Before Production)

# Upgrade staging cluster to 1.34
# Run for 3-7 days monitoring:
# - Application compatibility
# - Controller performance (improved reconcile rate expected)
# - API server latency (should decrease with cache)
# - etcd load (should decrease significantly)

Step 3: Upgrade Production (Phased Rollout)

# Phase 1: Upgrade control plane (API server, scheduler, controller manager)
# Duration: 15-30 minutes
# Impact: Brief API unavailability during API server upgrade
# Phase 2: Upgrade worker nodes (rolling update)
# Duration: 2-4 hours (depends on cluster size)
# Impact: Pods rescheduled as nodes drain/upgrade
# Phase 3: Enable new features (DRA, tracing) after validation
# Duration: 1-2 weeks
# Impact: None (opt-in features)

Cloud Provider Upgrade Schedules

Post-Upgrade Validation

# Verify control plane version
kubectl version --short
# Check all nodes upgraded
kubectl get nodes -o wide
# Monitor API server cache metrics (expect improvement)
kubectl top pods -n kube-system
# Verify no deprecated API warnings
kubectl get --raw /metrics | grep deprecated_apis
# Test DRA functionality (if using GPUs)
kubectl apply -f gpu-resourceclaim-test.yaml

Cost and Performance Optimization with Kubernetes 1.34

GPU Cost Savings with Dynamic Resource Allocation

Case Study: ML Training Platform (100 GPUs)

etcd Cost Reduction via API Server Cache

Impact on etcd IOPS Requirements:

# Before 1.34: High-IOPS etcd volumes required
# AWS io2 volume: 10,000 IOPS × $0.125/IOPS-month = $1,250/month
# After 1.34: Cache serves reads, lower IOPS sufficient
# AWS io2 volume: 3,000 IOPS × $0.125/IOPS-month = $375/month
Monthly savings: $875 per etcd node (3 nodes = $2,625/month saved)

How Atmosly Helps Teams Adopt Kubernetes 1.34

Upgrading to Kubernetes 1.34 while maintaining cost efficiency and stability requires:

• Pre-Upgrade Analysis: Atmosly scans clusters for deprecated APIs (AppArmor, beta storage APIs) flagging workloads requiring updates before 1.34 upgrade
• GPU Utilization Tracking: After enabling DRA, Atmosly monitors GPU utilization improvement comparing pre/post-1.34 metrics showing ROI of dynamic allocation
• Cost Impact Visibility: Real-time cost dashboards show GPU spend reduction from improved utilization and etcd cost savings from reduced IOPS requirements
• Performance Monitoring: Atmosly tracks API server latency improvements and controller reconcile rate increases validating 1.34 performance benefits
• Rightsizing Post-Upgrade: Leverages 1.34 in-place pod resize enhancements to adjust resource requests without pod restarts based on actual utilization

Who Should Upgrade to Kubernetes 1.34 Immediately

High-Priority Upgrade Candidates

1. AI/ML Teams with GPU Workloads

• Running training jobs requiring multi-GPU coordination
• Experiencing low GPU utilization (< 60%) due to static allocation
• Managing heterogeneous GPU inventory (A100, H100, V100 mix)
• Spending $50K+/month on GPU infrastructure
• Benefit: 20-35% GPU cost reduction via DRA dynamic allocation

2. Large-Scale Clusters (5,000+ Pods)

• Experiencing slow kubectl get operations (> 2 seconds for list)
• Controllers timing out during list/watch operations
• etcd CPU consistently above 70%
• API server request latency P95 > 1 second
• Benefit: 40-60% faster list operations, 85% etcd load reduction

3. Platform Teams Needing Better Observability

• Spending hours debugging pod startup delays
• Lack visibility into API server performance bottlenecks
• Controllers behaving unpredictably (reconcile loops, failures)
• Compliance requirements for audit trails
• Benefit: End-to-end tracing reducing MTTR from hours to minutes

Teams That Can Wait

• Small clusters (< 500 pods) not hitting performance limits
• No GPU or specialized hardware requirements
• Existing observability stack meeting needs
• Comfortable waiting for cloud provider managed upgrade (3-6 months)

Conclusion: Kubernetes 1.34 Marks a Major Platform Evolution

Kubernetes 1.34 represents the most impactful release since 1.28 (September 2023) introduced sidecar containers, with three game-changing capabilities: Dynamic Resource Allocation reaching GA enabling production AI/ML workloads with 20-35% GPU cost reduction through flexible allocation and sharing, production-grade distributed tracing providing unprecedented visibility into cluster operations reducing debugging time from hours to minutes, and snapshottable API server cache delivering 40-60% faster list operations with 85% etcd load reduction unlocking larger cluster scales.

Organizations running AI/ML workloads should prioritize immediate upgrade to leverage DRA for GPU optimization, while large-scale multi-tenant platforms benefit enormously from API server caching and tracing capabilities. Cloud provider managed Kubernetes (EKS, GKE, AKS) will offer 1.34 between May-June 2025, but self-managed clusters can upgrade immediately following the phased rollout strategy: test in staging for 1 week, upgrade control plane (15-30 minutes), rolling node upgrades (2-4 hours), and progressive feature enablement over 1-2 weeks.

Critical action items before upgrading: scan for deprecated AppArmor annotations requiring migration to seccomp or Pod Security Standards, update VolumeAttributesClass manifests from v1beta1 to v1 API, verify controllers don't depend on automatic ServiceAccount token Secret creation, and test application compatibility in staging environment for minimum 3-7 days before production rollout.

Ready to upgrade to Kubernetes 1.34? Try Atmosly for pre-upgrade cluster analysis identifying deprecated APIs, post-upgrade cost tracking showing GPU utilization improvements and etcd savings, and continuous rightsizing leveraging 1.34 in-place pod resize enhancements.

Questions about Kubernetes 1.34 migration? Schedule a consultation with our platform engineering team to review your upgrade strategy, identify which 1.34 features provide most value for your workloads, and develop phased rollout plan minimizing risk.