> ## Documentation Index
> Fetch the complete documentation index at: https://mcp-server-langgraph.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# VMware VM Resource Estimation for MCP Server LangGraph
> Target Platform: VMware vSphere / ESXi 1. [Executive Summary](#executive-summary) 2. [Component Resource Requirements](#component-resource-requirem...
**Version:** 2.6.0
**Date:** 2025-10-16
**Target Platform:** VMware vSphere / ESXi
### Table of Contents
1. [Executive Summary](#executive-summary)
2. [Component Resource Requirements](#component-resource-requirements)
3. [Deployment Scenarios](#deployment-scenarios)
4. [VM Sizing Recommendations](#vm-sizing-recommendations)
5. [Storage Requirements](#storage-requirements)
6. [Network Requirements](#network-requirements)
7. [VMware-Specific Optimizations](#vmware-specific-optimizations)
8. [Monitoring and Operations](#monitoring-and-operations)
9. [Scaling Considerations](#scaling-considerations)
10. [Cost Optimization](#cost-optimization)
***
### Executive Summary
This document provides detailed resource estimates for deploying the MCP Server LangGraph application stack on VMware infrastructure. The application is a production-ready MCP server with LangGraph, featuring comprehensive authentication (JWT/Keycloak), fine-grained authorization (OpenFGA), secrets management (Infisical), and OpenTelemetry-based observability.
#### Quick Reference
| Deployment Scenario | VMs | Total vCPUs | Total RAM | Total Storage |
| ------------------------------ | ---- | ----------- | --------- | ------------- |
| Development/Testing | 2-3 | 12-16 | 16-24 GB | 100-150 GB |
| Production (Standard) | 4-6 | 32-48 | 48-72 GB | 250-400 GB |
| Production (High Availability) | 6-10 | 64-96 | 96-144 GB | 500-800 GB |
***
### Component Resource Requirements
Based on the Kubernetes manifests and Docker configurations, here are the resource requirements for each component:
#### 1. MCP Server LangGraph (Main Application)
**Purpose:** AI agent server with LangGraph, multi-LLM support, and MCP protocol
**Container Resources (per pod):**
* **Requests:** 500m CPU, 512 Mi RAM
* **Limits:** 2000m CPU, 2 Gi RAM
* **Replicas:** 3 (default), 3-10 (autoscaling)
**Total Resources (3 replicas):**
* **CPU:** 1.5 cores (requests) to 6 cores (limits)
* **RAM:** 1.5 GB (requests) to 6 GB (limits)
**Key Features:**
* Python 3.12-based FastAPI application
* Multi-LLM support (Anthropic, OpenAI, Google, Azure, AWS Bedrock, Ollama)
* Stateful agent with checkpointing
* Health checks with startup/liveness/readiness probes
#### 2. PostgreSQL (Shared Database)
**Purpose:** Database for OpenFGA and Keycloak
**Container Resources:**
* **Requests:** 250m CPU, 512 Mi RAM
* **Limits:** 2000m CPU, 2 Gi RAM
* **Replicas:** 1 (StatefulSet)
**Storage:**
* **Persistent Volume:** 10 Gi (default)
* **Recommended:** 20-50 Gi for production
**Total Resources:**
* **CPU:** 0.25 to 2 cores
* **RAM:** 512 MB to 2 GB
* **Storage:** 10-50 GB persistent
#### 3. OpenFGA (Authorization)
**Purpose:** Fine-grained authorization (Zanzibar-style)
**Container Resources (per pod):**
* **Requests:** 250m CPU, 256 Mi RAM
* **Limits:** 1000m CPU, 1 Gi RAM
* **Replicas:** 2 (HA deployment)
**Total Resources (2 replicas):**
* **CPU:** 0.5 to 2 cores
* **RAM:** 512 MB to 2 GB
#### 4. Keycloak (Identity Management)
**Purpose:** SSO, user management, OAuth2/OIDC
**Container Resources (per pod):**
* **Requests:** 500m CPU, 1 Gi RAM
* **Limits:** 2000m CPU, 2 Gi RAM
* **Replicas:** 2 (HA deployment)
**Total Resources (2 replicas):**
* **CPU:** 1 to 4 cores
* **RAM:** 2 GB to 4 GB
**Notes:**
* Keycloak is Java-based and benefits from higher RAM allocation
* Startup time: 60-120 seconds
#### 5. Redis (Session Management + Checkpoints)
**Purpose:** Session storage and conversation checkpoints
**Container Resources:**
* **Requests:** 100m CPU, 256 Mi RAM
* **Limits:** 500m CPU, 1 Gi RAM
* **Replicas:** 1 (can scale to Redis Sentinel/Cluster for HA)
**Storage:**
* **Persistent Volume:** 5 Gi (default)
* **Configuration:** AOF + RDB persistence
**Total Resources:**
* **CPU:** 0.1 to 0.5 cores
* **RAM:** 256 MB to 1 GB
* **Storage:** 5-10 GB persistent
#### 6. OpenTelemetry Collector
**Purpose:** Observability data collection and export
**Container Resources (per pod):**
* **Requests:** 200m CPU, 256 Mi RAM
* **Limits:** 1000m CPU, 512 Mi RAM
* **Replicas:** 2 (default), 2-10 (autoscaling)
**Total Resources (2 replicas):**
* **CPU:** 0.4 to 2 cores
* **RAM:** 512 MB to 1 GB
#### 7. Monitoring Stack (Optional but Recommended)
##### Prometheus
* **CPU:** 500m to 2 cores
* **RAM:** 2 GB to 4 GB
* **Storage:** 50-100 GB (time-series data)
##### Grafana
* **CPU:** 200m to 1 core
* **RAM:** 512 MB to 1 GB
* **Storage:** 5-10 GB
##### Jaeger
* **CPU:** 500m to 2 cores
* **RAM:** 1 GB to 2 GB
* **Storage:** 20-50 GB (trace data)
***
### Deployment Scenarios
#### Scenario 1: Development/Testing
**Use Case:** Local development, testing, CI/CD pipelines
**Components:**
* 1x MCP Server LangGraph (single replica)
* 1x PostgreSQL
* 1x OpenFGA (single replica)
* 1x Redis
* Optional: Lightweight monitoring (Prometheus + Grafana)
**VM Configuration:**
##### Option A: Consolidated (2 VMs)
1. **Application VM** (all services except DB)
* **vCPUs:** 6-8
* **RAM:** 8-12 GB
* **Storage:** 50 GB
2. **Database VM** (PostgreSQL + Redis)
* **vCPUs:** 4
* **RAM:** 6-8 GB
* **Storage:** 50-100 GB
##### Option B: Minimal (1 VM)
* **vCPUs:** 8-12
* **RAM:** 16-20 GB
* **Storage:** 100 GB
**Notes:**
* No Keycloak (use in-memory auth)
* No autoscaling
* Minimal monitoring
* Suitable for developers and staging environments
#### Scenario 2: Production (Standard)
**Use Case:** Production deployment with moderate traffic (\< 1000 req/min)
**Components:**
* 3x MCP Server LangGraph
* 1x PostgreSQL (with backups)
* 2x OpenFGA
* 2x Keycloak
* 1x Redis (with persistence)
* 2x OpenTelemetry Collector
* Full monitoring stack
**VM Configuration (4-6 VMs):**
1. **Control Plane VM** (Kubernetes control plane, if using K8s)
* **vCPUs:** 4
* **RAM:** 8 GB
* **Storage:** 100 GB
2. **Application VM 1** (Node 1)
* **vCPUs:** 8
* **RAM:** 16 GB
* **Storage:** 100 GB
* **Workload:** 1x MCP Server, 1x OpenFGA, 1x Keycloak, 1x OTel Collector
3. **Application VM 2** (Node 2)
* **vCPUs:** 8
* **RAM:** 16 GB
* **Storage:** 100 GB
* **Workload:** 1x MCP Server, 1x OpenFGA, 1x Keycloak, 1x OTel Collector
4. **Application VM 3** (Node 3)
* **vCPUs:** 8
* **RAM:** 16 GB
* **Storage:** 100 GB
* **Workload:** 1x MCP Server
5. **Database VM**
* **vCPUs:** 4-8
* **RAM:** 8-16 GB
* **Storage:** 200 GB (100 GB OS + data, 100 GB backups)
6. **Monitoring VM** (Optional)
* **vCPUs:** 4
* **RAM:** 8 GB
* **Storage:** 150 GB
* **Workload:** Prometheus, Grafana, Jaeger
**Total Resources:**
* **vCPUs:** 32-48
* **RAM:** 48-72 GB
* **Storage:** 250-400 GB (excluding long-term backup storage)
**High Availability Features:**
* Pod anti-affinity across VMs
* 2 replicas of critical services (OpenFGA, Keycloak)
* Pod Disruption Budgets (minimum 2 available for MCP Server)
* PostgreSQL with daily backups
#### Scenario 3: Production (High Availability)
**Use Case:** Production with high traffic (> 1000 req/min), mission-critical
**Components:**
* 3-10x MCP Server LangGraph (autoscaling)
* 3x PostgreSQL (HA cluster with replication)
* 2-3x OpenFGA
* 2-3x Keycloak
* 3x Redis (Sentinel/Cluster mode)
* 2-10x OpenTelemetry Collector (autoscaling)
* Full monitoring stack with high availability
**VM Configuration (6-10 VMs):**
1. **Control Plane VMs** (3 VMs for HA Kubernetes control plane)
* Each: **vCPUs:** 4, **RAM:** 8 GB, **Storage:** 100 GB
2. **Application VMs** (3-5 VMs for worker nodes)
* Each: **vCPUs:** 12-16, **RAM:** 24-32 GB, **Storage:** 100 GB
3. **Database VMs** (3 VMs for PostgreSQL HA with replication)
* Primary: **vCPUs:** 8, **RAM:** 16 GB, **Storage:** 200 GB
* Replica 1: **vCPUs:** 8, **RAM:** 16 GB, **Storage:** 200 GB
* Replica 2: **vCPUs:** 8, **RAM:** 16 GB, **Storage:** 200 GB
4. **Monitoring VMs** (2 VMs for redundant monitoring)
* Each: **vCPUs:** 4-6, **RAM:** 8-12 GB, **Storage:** 150-200 GB
**Total Resources:**
* **vCPUs:** 64-96
* **RAM:** 96-144 GB
* **Storage:** 500-800 GB (excluding long-term storage)
**Enterprise Features:**
* Multi-master PostgreSQL with synchronous replication
* Redis Sentinel for automatic failover
* Geographic distribution across availability zones/clusters
* Autoscaling based on CPU (70%) and Memory (80%) utilization
* Service mesh (Istio/Linkerd) optional
* External secrets management (HashiCorp Vault, AWS Secrets Manager)
***
### Storage Requirements
#### Persistent Volume Breakdown
| Component | Type | Size (Dev) | Size (Prod Standard) | Size (Prod HA) | IOPS Requirements |
| ------------------ | ---- | ---------- | -------------------- | -------------- | ----------------- |
| PostgreSQL Data | RWO | 10 GB | 20-50 GB | 100-200 GB | 1000-3000 |
| PostgreSQL Backups | RWO | N/A | 50-100 GB | 200-500 GB | 500-1000 |
| Redis Data | RWO | 5 GB | 5-10 GB | 10-20 GB | 500-1000 |
| Prometheus TSDB | RWO | 20 GB | 50-100 GB | 100-200 GB | 500-1000 |
| Jaeger Traces | RWO | 10 GB | 20-50 GB | 50-100 GB | 500-1000 |
| Grafana Data | RWO | 5 GB | 5-10 GB | 10 GB | 100-500 |
| Application Logs | RWX | Optional | 10-20 GB | 20-50 GB | 500 |
#### VMware Storage Recommendations
1. **Storage Classes:**
* **High Performance (SSD/NVMe):** PostgreSQL, Redis
* **Standard Performance (SAS/SATA):** Application containers, logs
* **Archive (SATA):** Backups, long-term trace storage
2. **Storage Protocols:**
* **VMFS Datastores:** Best for RWO (ReadWriteOnce) volumes
* **NFS/vSAN:** Required for RWX (ReadWriteMany) if using shared logs
* **vSphere CSI Driver:** Recommended for dynamic provisioning
3. **Backup Strategy:**
* **PostgreSQL:** Daily full backup + WAL archiving
* **Redis:** RDB snapshots + AOF logs
* **Application State:** Container images in registry
* **Retention:** 7 days (daily), 4 weeks (weekly), 12 months (monthly)
***
### Network Requirements
#### Bandwidth Estimates
| Traffic Type | Dev | Prod Standard | Prod HA |
| --------------------- | ----------- | ------------- | ----------------- |
| External API Traffic | 10-50 Mbps | 100-500 Mbps | 500 Mbps - 2 Gbps |
| LLM API Calls | 10-100 Mbps | 100-500 Mbps | 500 Mbps - 1 Gbps |
| Internal Service Mesh | 50-100 Mbps | 200-500 Mbps | 500 Mbps - 2 Gbps |
| Observability Data | 10-50 Mbps | 50-200 Mbps | 200-500 Mbps |
| Database Replication | N/A | 10-50 Mbps | 50-200 Mbps |
#### Network Configuration
1. **VM Network Adapters:**
* **Type:** VMXNET3 (paravirtualized, best performance)
* **NICs per VM:**
* Application VMs: 1 NIC (1-10 Gbps)
* Database VMs: 2 NICs (dedicated replication network)
* Control Plane: 1 NIC (1-10 Gbps)
2. **Network Segmentation:**
* **Public Network:** External API traffic (load balancer)
* **Private Network:** Inter-service communication (overlay network)
* **Management Network:** SSH, monitoring, backups
* **Storage Network:** iSCSI, NFS (if using network storage)
3. **Firewall Rules:**
* **Inbound:** 443/TCP (HTTPS), 80/TCP (HTTP redirect)
* **Inter-VM:**
* 8000/TCP (MCP Server)
* 5432/TCP (PostgreSQL)
* 6379/TCP (Redis)
* 8080/TCP (OpenFGA, Keycloak)
* 4317/TCP, 4318/TCP (OTLP)
* **Outbound:**
* 443/TCP (LLM APIs, secrets management)
* 53/UDP (DNS)
4. **Load Balancing:**
* **External LB:** VMware NSX-T, HAProxy, or cloud load balancer
* **Internal LB:** Kubernetes Service (ClusterIP with kube-proxy)
* **Session Affinity:** Not required (stateless with Redis sessions)
***
### VMware-Specific Optimizations
#### 1. VM Configuration Best Practices
##### CPU Configuration
* **vCPU Allocation:** 1:2 to 1:4 overcommitment ratio
* **CPU Reservation:** Reserve 50% for production VMs
* **CPU Shares:** High priority for database and application VMs
* **NUMA Optimization:** Enable vNUMA for VMs with >8 vCPUs
* **CPU Hot-Add:** Enable for application VMs (not recommended for DB)
##### Memory Configuration
* **Memory Reservation:** 100% for database VMs, 50% for application VMs
* **Memory Shares:** High priority for database and Keycloak VMs
* **Balloon Driver:** Enabled (VMware Tools required)
* **Memory Hot-Add:** Enable for application VMs
* **Large Memory Pages:** Enable for database VMs
##### Storage Configuration
* **Disk Type:** Thick Provisioned Eager Zeroed for production
* **SCSI Controller:** VMware Paravirtual (PVSCSI) for database VMs
* **Multi-Writer:** Disable (not needed for this workload)
* **Disk Shares:** High for database VMs, Normal for applications
#### 2. VMware vSphere Features
##### High Availability (HA)
* **VM Restart Priority:**
* High: PostgreSQL, Redis
* Medium: MCP Server, Keycloak, OpenFGA
* Low: Monitoring stack
* **Host Isolation Response:** Leave Powered On
* **Datastore Heartbeats:** Enable for network isolation detection
##### Distributed Resource Scheduler (DRS)
* **Automation Level:** Fully Automated
* **VM-VM Anti-Affinity Rules:**
* Keep PostgreSQL replicas on different hosts
* Keep MCP Server replicas on different hosts
* **VM-Host Affinity Rules:** Pin database VMs to high-performance hosts
##### Storage DRS
* **SDRS Mode:** Fully Automated
* **I/O Metric:** Enable SDRS for load balancing
* **Affinity Rules:** Keep DB and logs on separate datastores
#### 3. Resource Pools
Create resource pools for workload isolation:
```mermaid theme={null}
%% ColorBrewer2 Set3 palette - each component type uniquely colored
flowchart TD
Root[Cluster Root] --> Prod[Production]
Root --> Dev[Development
expandable reservation]
Prod --> Apps[Applications
DB[Databases
Obs[Observability
MCP[MCP-Server
shares: high]
Apps --> KC[Keycloak
shares: high]
Apps --> OFG[OpenFGA
shares: normal]
DB --> PG[PostgreSQL
shares: high
reservation: 100%]
DB --> Redis[Redis
shares: normal]
Obs --> Prom[Prometheus
shares: normal]
Obs --> Graf[Grafana
shares: low]
Obs --> Jaeg[Jaeger
shares: normal]
classDef rootStyle fill:#8dd3c7,stroke:#2a9d8f,stroke-width:2px,color:#333
classDef prodStyle fill:#fdb462,stroke:#e67e22,stroke-width:2px,color:#333
classDef devStyle fill:#b3de69,stroke:#7cb342,stroke-width:2px,color:#333
classDef appPoolStyle fill:#ffffb3,stroke:#f39c12,stroke-width:2px,color:#333
classDef dbPoolStyle fill:#80b1d3,stroke:#3498db,stroke-width:2px,color:#333
classDef obsPoolStyle fill:#bebada,stroke:#8e44ad,stroke-width:2px,color:#333
classDef appStyle fill:#ccebc5,stroke:#82c99a,stroke-width:2px,color:#333
classDef dataStyle fill:#fb8072,stroke:#c0392b,stroke-width:2px,color:#333
class Root rootStyle
class Prod prodStyle
class Dev devStyle
class Apps appPoolStyle
class DB dbPoolStyle
class Obs obsPoolStyle
class MCP,KC,OFG appStyle
class PG,Redis dataStyle
class Prom,Graf,Jaeg obsPoolStyle
```
#### 4. Monitoring and Alerts
##### vSphere Metrics to Monitor
* **CPU Ready Time:** \< 5% (indicates CPU contention)
* **CPU Co-Stop:** \< 3% (indicates vCPU scheduling issues)
* **Memory Ballooning:** \< 1% (indicates memory pressure)
* **Storage Latency:** \< 20ms for DB, \< 50ms for apps
* **Network Dropped Packets:** 0 (indicates network saturation)
##### Integration with Application Monitoring
* Use VMware vRealize Operations for VM-level metrics
* Correlate with Prometheus/Grafana for application metrics
* Set up alerts for resource exhaustion before it impacts apps
***
### Monitoring and Operations
#### Resource Utilization Monitoring
##### Key Metrics (Prometheus)
```bash theme={null}
## CPU Utilization (target: 50-70%)
avg(rate(container_cpu_usage_seconds_total[5m])) by (pod)
## Memory Utilization (target: 60-80%)
avg(container_memory_working_set_bytes) by (pod)
## Disk I/O (IOPS)
rate(node_disk_reads_completed_total[5m])
rate(node_disk_writes_completed_total[5m])
## Network Traffic
rate(container_network_receive_bytes_total[5m])
rate(container_network_transmit_bytes_total[5m])
```
##### Grafana Dashboards
* **Infrastructure Dashboard:** VM resources, storage, network
* **Application Dashboard:** MCP Server, LLM calls, agent performance
* **Database Dashboard:** PostgreSQL queries, connections, replication lag
* **Observability Dashboard:** OpenTelemetry pipeline health
#### Capacity Planning Alerts
```yaml theme={null}
## CPU pressure
alert: HighCPUUsage
expr: avg(rate(container_cpu_usage_seconds_total[5m])) > 0.8
for: 10m
## Memory pressure
alert: HighMemoryUsage
expr: (container_memory_working_set_bytes / container_spec_memory_limit_bytes) > 0.85
for: 5m
## Storage space
alert: LowDiskSpace
expr: (node_filesystem_avail_bytes / node_filesystem_size_bytes) < 0.15
for: 5m
## Database connections
alert: HighDatabaseConnections
expr: pg_stat_database_numbackends > 80
for: 5m
```
***
### Scaling Considerations
#### Horizontal Pod Autoscaling (HPA)
Based on the Kubernetes configurations:
##### MCP Server LangGraph
```yaml theme={null}
minReplicas: 3
maxReplicas: 10
targetCPUUtilization: 70%
targetMemoryUtilization: 80%
```
**Scaling Triggers:**
* CPU > 70% for 30 seconds → scale up (max 4 pods or 100% increase)
* CPU \< 40% for 5 minutes → scale down (max 2 pods or 50% decrease)
**VM Capacity Planning:**
* Reserve capacity for 2x current load (6-8 pods)
* Add 1 VM for every 3-4 additional pods
##### OpenTelemetry Collector
```yaml theme={null}
minReplicas: 2
maxReplicas: 10
targetCPUUtilization: 70%
targetMemoryUtilization: 80%
```
**Scaling Triggers:**
* Scales with observability data volume
* 1 collector can handle \~1000 req/sec trace data
#### Vertical Scaling
##### When to Scale Up VM Resources
1. **Consistent CPU > 80%** across all pods for 1+ hour
2. **Memory pressure** causing OOM kills or pod evictions
3. **Disk I/O wait** > 10% during normal operations
4. **Network saturation** on VM NICs
##### Recommended Scaling Steps
| Metric | Current | Step 1 | Step 2 | Step 3 |
| ------- | ------- | ------ | ------ | ------ |
| vCPUs | 8 | 12 | 16 | 24 |
| RAM | 16 GB | 24 GB | 32 GB | 48 GB |
| Storage | 100 GB | 200 GB | 400 GB | 800 GB |
**Downtime:**
* CPU/Memory: Requires VM reboot (plan maintenance window)
* Storage: Can be expanded online (no downtime)
#### Load Testing Recommendations
Before deploying to production, conduct load testing:
```bash theme={null}
## Example: Simulate 1000 concurrent users
hey -n 100000 -c 1000 -q 10 \
-H "Authorization: Bearer $TOKEN" \
https://mcp-server.example.com/health
## Stress test with wrk
wrk -t12 -c400 -d30s --latency \
-H "Authorization: Bearer $TOKEN" \
https://mcp-server.example.com/agent
```
**Expected Performance (Production Standard):**
* **Throughput:** 500-1000 req/sec
* **Latency (p95):** \< 5s for agent responses
* **Latency (p99):** \< 10s
* **Concurrent Users:** 1000-5000
***
### Cost Optimization
#### Resource Right-Sizing
##### Development Environment
* **Reduce replicas:** 1 instance of all services
* **Disable monitoring:** Use lightweight Prometheus only
* **Use in-memory auth:** Skip Keycloak
* **Smaller VMs:** 4 vCPU, 8 GB RAM per VM
**Savings:** 60-70% reduction vs. production
##### Production Environment
* **Auto-shutdown:** Dev/test environments during off-hours
* **Spot Instances:** For non-critical monitoring workloads (if using cloud)
* **Tiered Storage:** Move old logs/traces to cheaper storage after 30 days
#### VMware License Optimization
| Feature | Required License | Alternative |
| ------------------- | ------------------ | ------------------------- |
| vSphere HA | Standard+ | Manual failover |
| DRS | Standard+ | Manual load balancing |
| Storage DRS | Enterprise+ | Manual storage management |
| vRealize Operations | Additional license | Open-source monitoring |
**Recommendation:** vSphere Standard is sufficient for Dev/Test; Enterprise Plus recommended for Production HA.
#### Multi-Tenancy
If running multiple environments (dev, staging, prod):
* **Option 1:** Separate clusters (better isolation, higher cost)
* **Option 2:** Resource pools on same cluster (lower cost, shared resources)
* **Option 3:** Namespaces in Kubernetes on same VMs (lowest cost, minimal isolation)
**Recommended:** Option 2 for most use cases (balance of cost and isolation)
***
### Summary and Recommendations
#### Recommended Starting Configuration
For a typical production deployment:
**Infrastructure:**
* **VMware Cluster:** 4-6 ESXi hosts (for DRS and HA)
* **Total VMs:** 6 (1 control plane, 3 workers, 1 database, 1 monitoring)
* **Total Resources:** 40 vCPUs, 64 GB RAM, 400 GB storage
**Growth Plan:**
* Start with **Production (Standard)** scenario
* Monitor for 30 days
* Scale based on actual utilization patterns
* Plan for 2x capacity headroom for traffic spikes
#### Key Takeaways
1. **Start Small:** Development scenario can run on 2 VMs (16 vCPUs, 24 GB RAM)
2. **Plan for HA:** Production needs minimum 3 VMs for application redundancy
3. **Database is Critical:** Allocate best storage and CPU to PostgreSQL
4. **Monitor First:** Use Prometheus/Grafana to identify actual bottlenecks
5. **Autoscaling Works:** HPA can handle 3x traffic spikes without manual intervention
6. **Storage Grows:** Plan for 50-100 GB/month growth in observability data
#### Next Steps
1. **Provision Infrastructure:**
* Set up VMware resource pools
* Create VM templates (Kubernetes node OS)
* Configure storage classes (SSD, SATA, backup)
2. **Deploy Kubernetes:**
* Install Kubernetes 1.28+ (kubeadm, Rancher, or Tanzu)
* Configure vSphere CSI driver
* Set up network overlay (Calico, Cilium)
3. **Deploy Application:**
* Use Helm charts: `helm install langgraph-agent ./deployments/helm/mcp-server-langgraph`
* Or Kustomize: `kubectl apply -k deployments/kubernetes/overlays/production`
4. **Validate and Monitor:**
* Run load tests
* Tune resource requests/limits based on actual usage
* Set up alerting and runbooks
#### Support
For deployment assistance:
* **Documentation:** [Kubernetes Deployment Guide](/deployment/kubernetes)
* **Issues:** [https://github.com/vishnu2kmohan/mcp-server-langgraph/issues](https://github.com/vishnu2kmohan/mcp-server-langgraph/issues)
* **Discussions:** [https://github.com/vishnu2kmohan/mcp-server-langgraph/discussions](https://github.com/vishnu2kmohan/mcp-server-langgraph/discussions)
***
**Document Version:** 1.0
**Last Updated:** 2025-10-16
**Maintained By:** MCP Server LangGraph Contributors