Self-Hosting OpenClaw on Kubernetes with DeepSeek V3.2 via AkashML: A Complete Technical Guide

Comprehensive guide to deploying OpenClaw AI assistant using Helm charts, connected to DeepSeek V3.2 running on Akash Network's decentralized GPU infrastructure. Covers architecture, configuration, cost analysis, and deployment walkthrough.

Axelgamer.com

25. März 2026March 25, 2026 · 7 Min. Lesezeit7 min read

Self-Hosting OpenClaw on Kubernetes with DeepSeek V3.2 via AkashML: A Complete Technical Guide

Executive Summary#

This article explores a cutting-edge deployment architecture: self-hosting the OpenClaw AI assistant using a Kubernetes Helm chart, powered by DeepSeek V3.2 running on AkashML's decentralized GPU network. This combination represents the convergence of three powerful trends in AI infrastructure: self-hosted AI assistants, decentralized cloud computing, and state-of-the-art open-source language models.

Architecture Overview#

┌─────────────────────────────────────────────────────────────┐
│                    Kubernetes Cluster                        │
│  ┌──────────────────────────────────────────────────────┐  │
│  │           OpenClaw Helm Chart Deployment             │  │
│  │  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  │  │
│  │  │   Main      │  │  Chromium   │  │   Init      │  │  │
│  │  │  Container  │  │  Sidecar    │  │ Containers  │  │  │
│  │  │             │  │             │  │             │  │  │
│  │  └─────────────┘  └─────────────┘  └─────────────┘  │  │
│  │        │                    │               │        │  │
│  │        ▼                    ▼               ▼        │  │
│  │  Persistent Volume Claims (PVCs)                    │  │
│  └──────────────────────────────────────────────────────┘  │
│                              │                              │
└──────────────────────────────┼──────────────────────────────┘
                               │
                               ▼
┌─────────────────────────────────────────────────────────────┐
│              AkashML Decentralized Network                  │
│  ┌──────────────────────────────────────────────────────┐  │
│  │         DeepSeek V3.2 Inference Endpoints            │  │
│  │     (65+ global datacenters, decentralized GPUs)     │  │
│  └──────────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────────┘

Component 1: The OpenClaw Helm Chart#

Architecture#

The openclaw-helm chart (maintained by serhanekicii) provides a production-ready Kubernetes deployment for OpenClaw with:

Core Containers:

Key Features:

Configuration#

The chart uses a ConfigMap-based configuration system with openclaw.json:

{
  "gateway": {
    "port": 18789,
    "mode": "local",
    "trustedProxies": ["10.0.0.1"]
  },
  "agents": {
    "defaults": {
      "model": {
        "primary": "anthropic/claude-opus-4-6"
      }
    }
  }
}

Deployment Command:

helm install openclaw ./openclaw-helm \
  --set openclawVersion="2026.3.23-2" \
  --set chromiumVersion="146.0.7680.154"

Component 2: DeepSeek V3.2 on AkashML#

The Model: DeepSeek V3.2#

Technical Specifications:

Key Innovations:

  1. Scalable RL Framework: Post-training compute exceeds 10% of pre-training cost
  2. Agentic Task Synthesis: 1,800+ environments, 85,000+ complex prompts
  3. Gold-medal performance in 2025 IMO and IOI competitions

The Infrastructure: AkashML#

AkashML is the first fully managed AI inference service built entirely on decentralized GPUs:

Economic Advantages:

Technical Advantages:

API Integration:

import requests

response = requests.post(
    "https://api.akashml.com/v1/chat/completions",
    headers={
        "Authorization": "Bearer AKASHML_API_KEY",
        "Content-Type": "application/json"
    },
    json={
        "model": "deepseek-ai/DeepSeek-V3.2",
        "messages": [{"role": "user", "content": "Hello"}],
        "max_tokens": 150
    }
)

Integration: Connecting OpenClaw to DeepSeek V3.2#

Configuration Update#

Modify the OpenClaw Helm chart configuration to use DeepSeek V3.2 via AkashML:

{
  "agents": {
    "defaults": {
      "model": {
        "primary": "akashml/deepseek-ai/DeepSeek-V3.2",
        "provider": "akashml",
        "endpoint": "https://api.akashml.com/v1/chat/completions",
        "apiKey": "${AKASHML_API_KEY}"
      }
    }
  }
}

Environment Variables#

Add to the Helm values:

env:
  - name: AKASHML_API_KEY
    valueFrom:
      secretKeyRef:
        name: akashml-credentials
        key: apiKey
  - name: DEFAULT_MODEL
    value: "akashml/deepseek-ai/DeepSeek-V3.2"

Benefits of This Integration#

  1. Cost Efficiency: 70-85% savings vs. proprietary model APIs
  2. Performance: State-of-the-art reasoning capabilities
  3. Decentralization: No single point of failure
  4. Privacy: Self-hosted control over data and conversations
  5. Customization: Full access to OpenClaw's tooling ecosystem

Deployment Walkthrough#

Step 1: Prerequisites#

Step 2: Clone and Configure#

git clone https://github.com/serhanekicii/openclaw-helm
cd openclaw-helm

# Create secrets
kubectl create secret generic akashml-credentials \
  --from-literal=apiKey=YOUR_AKASHML_API_KEY

# Create values override
cat > values-override.yaml <<EOF
openclawVersion: "2026.3.23-2"
chromiumVersion: "146.0.7680.154"
configMode: "merge"

app-template:
  controllers:
    main:
      containers:
        main:
          env:
            - name: AKASHML_API_KEY
              valueFrom:
                secretKeyRef:
                  name: akashml-credentials
                  key: apiKey
EOF

Step 3: Deploy#

helm install openclaw . -f values-override.yaml

# Verify deployment
kubectl get pods -l app.kubernetes.io/name=openclaw
kubectl logs deployment/openclaw-main

Step 4: Configure OpenClaw#

Update the runtime configuration to use DeepSeek V3.2:

# Access the OpenClaw configuration
kubectl exec deployment/openclaw-main -- cat /home/node/.openclaw/config.json

# Update model configuration via ConfigMap or runtime API

Performance Characteristics#

Resource Utilization#

Cost Analysis#

Monthly Cost Estimate:

Comparison:

Security Considerations#

OpenClaw Security Features#

AkashML Security#

  1. Network Policies: Restrict ingress/egress
  2. Secret Management: Use Kubernetes Secrets or external vault
  3. Monitoring: Implement Prometheus/Grafana dashboards
  4. Backup: Regular PVC snapshots
  5. Updates: Automated security patch management

Use Cases and Applications#

1. Personal AI Assistant#

2. Development Team Assistant#

3. Business Process Automation#

4. Research and Education#

Challenges and Solutions#

Challenge 1: Model Switching#

Solution: OpenClaw supports runtime model configuration. Create aliases for different use cases:

{
  "model": {
    "primary": "akashml/deepseek-ai/DeepSeek-V3.2",
    "coding": "akashml/deepseek-ai/DeepSeek-Coder",
    "fast": "akashml/qwen/Qwen3-30B-A3B"
  }
}

Challenge 2: State Management#

Solution: Persistent volumes ensure session continuity across pod restarts.

Challenge 3: Cost Control#

Solution: Implement token usage monitoring and budget alerts via AkashML dashboard.

Future Evolution#

1. Akash Network Upgrades#

2. Model Improvements#

3. OpenClaw Ecosystem#

Conclusion#

The combination of self-hosted OpenClaw via Helm charts with DeepSeek V3.2 on AkashML represents a paradigm shift in AI assistant deployment:

  1. Economic: 70-85% cost savings vs. proprietary solutions
  2. Technical: State-of-the-art reasoning capabilities
  3. Operational: Production-grade Kubernetes deployment
  4. Strategic: Decentralized, vendor-agnostic architecture

This stack delivers enterprise-grade AI capabilities at hobbyist prices, combining the privacy and control of self-hosting with the performance and economics of decentralized cloud computing.

As AI continues to evolve, this architecture provides a foundation that's scalable, cost-effective, and future-proof—ready to integrate new models, new tools, and new capabilities as they emerge.

Resources:

Published: March 25, 2026

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Contents

12 sections

  1. Executive Summary
  2. Architecture Overview
  3. Component 1: The OpenClaw Helm Chart
  4. Component 2: DeepSeek V3.2 on AkashML
  5. Integration: Connecting OpenClaw to DeepSeek V3.2
  6. Deployment Walkthrough
  7. Performance Characteristics
  8. Security Considerations
  9. Use Cases and Applications
  10. Challenges and Solutions
  11. Future Evolution
  12. Conclusion