WaveMaker Enterprise AI Prerequisites
You can set up WaveMaker Enterprise AI on any machine.
This document uses words like VM, Instance to refer a machine.
WME AI setup system requirements
WaveMaker Enterprise AI can be installed on any machine that meets the following requirements. Before you start setting up WaveMaker Enterprise AI, review the minimum and recommended system requirements for each instance type.
WME AI Platform Instance
| Requirement | Minimum configuration |
|---|---|
| Memory |
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| CPU |
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| Hard disk |
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| Host OS |
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| Software |
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| Network |
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WME AI StudioWorkspace Instance and AppDeployment Instance
| Requirement | Minimum configuration |
|---|---|
| Memory |
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| CPU |
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| Hard disk |
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| Host OS |
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| Software |
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| Network |
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WME AI Observability Instance
| Requirement | Minimum configuration |
|---|---|
| Memory |
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| CPU |
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| Hard disk |
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| Host OS |
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| Software |
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| Network |
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IP Addressing and DNS Mapping
You will be needing IP Addresses for the following.
IP Address
- One static IP for accessing the platform machine from your developer's network.
- Machine Static IP: This is the IP assigned to the machine during setup and should be accessible on your network, or
- In the case of VM, it will be the local IP address, which should be rout table from in your LAN.
- In case of AWS instance: Private static IP for the instance within your VPC (assigned via eth0 or via ENI on eth1,ens5)
DNS Mapping
A DNS domain is mandatory for the Platform instance — developers access WaveMaker Studio using a domain name, not an IP address. DNS for other instances is optional but recommended.
| Domain | Domain URL | Required | Description |
|---|---|---|---|
| WaveMaker Studio | wavemakerai.[mycompany].com | Mandatory | Used to access WaveMaker AI Studio |
| WaveMaker Deployed Apps | wmai-apps.[mycompany].com wmai-stage.[mycompany].com wmai-live.[mycompany].com | Optional | Used to access WaveMaker AI Studio apps deployed onto WaveMaker AI Cloud |
| WaveMaker AI Observability | wmai-analytics.[mycompany].com | Optional | Used to access WaveMaker AI Analytics service. If not configured, use port 5050 via IP. |
In the preceding table, [mycompany] is used as an example. Replace [mycompany] with your actual domain name.
Docker Container Access
- An IP range to be assigned to the Docker containers internally. The Minimum CIDR (Classless Inter-Domain Routing) range for Docker container network is 24.
You will be needing to assign a /24 CIDR to Docker during setup. This IP range should not be in use anywhere on your network and can be completely different from your network's range. These IPs are assigned internally by Docker to containers and these IPs won't be exposed on your network.
For example, if your network is using a 10.x.x.x_range and the range_192.168.x.x is not used anywhere in your network, you may assign this 192.168.x.x range to Docker. See here for the possible LAN IP ranges.
Port Requirements
External Access Ports
These ports must be accessible from outside the WME platform network — from developer machines, DevOps teams, and admin machines.
Ports 443 on the Platform, AppDeployment, and Observability instances are accessed through a DNS name or load balancer, not directly via IP:port. Ensure the DNS entries (see DNS Mapping) resolve to the respective instances and that traffic on port 443 can reach them through your network or load balancer.
| Port | Instance | DNS Name | Accessed By | Purpose |
|---|---|---|---|---|
| 443 | Platform | wavemakerai.[mycompany].com | Developer machines | HTTPS access to WaveMaker Studio |
| 443 | AppDeployment | wmai-apps.[mycompany].comwmai-stage.[mycompany].comwmai-live.[mycompany].com | Developers / end users | Access to deployed WaveMaker applications |
| 443 | Observability | wmai-analytics.[mycompany].com | DevOps machines | AI observability UI — traces and analytics |
| 5050 | Observability | IP-based, no DNS required | DevOps machines | Fallback access when DNS is not configured for the Observability instance |
| 8080 | Platform | IP-based | Admin machines | WaveMaker config portal |
| 22 | All instances | IP-based | Admin machines | SSH access for installation and management |
Internal Communication Ports
All communication listed here is between WME instances within the platform's private network. None of these ports need to be accessible from outside the WME network.
Recommended: Allow unrestricted communication between all WME instances within the platform's private network.
If your security policy requires restricting traffic to specific ports, open only the ports listed in the following tables.
Open on the Platform Instance — for access from StudioWorkspace and AppDeployment instances:
| Port | Purpose |
|---|---|
| 443 | HTTPS access to the Platform Instance |
| 5000 | Platform services |
| 8500 | Service discovery |
| 22 | SSH access |
| 8081 | Platform communication |
| 2200 | Container SSH access |
| 8100 | StudioWorkspace and AppDeployment communication |
| 9200 | Search and observability services |
| 8000-8020 | Platform-managed application services |
| 8094 | AI service communication |
| 8079 | AI service communication |
| 5432 | Database connectivity |
| 5433 | Vector database access for AI features |
| 8083 | AI Studio and agent-server LiteLLM proxy communication |
| 8086 | AI Studio and agent-server key management |
Open on StudioWorkspace and AppDeployment instances — for access from the Platform Instance:
| Port | Purpose |
|---|---|
| 22 | SSH access |
| 2375 | Docker API access |
| 80 | HTTP access |
| 5000 | Platform service communication |
| 8100 | StudioWorkspace and AppDeployment communication |
| 8888 | Workspace service communication |
| 9101, 9102, 9100 | Metrics collection |
| 9404 | Metrics export |
| 2200-2299 | Container SSH access |
| 8001-8099 | Platform-managed application services |
| 3300-3399 | Database and service communication |
| 9500-9599 | Platform-managed service communication |
| 3000 | Routing traffic to AI Studio |
| 3001 | Routing traffic to AI Studio NGINX |
| 3002 | Routing traffic to agent-server |
| 5010 | Backend MCP |
| 5020 | UI MCP |
Open on the Observability Instance — for access from the Platform and all StudioWorkspace instances:
| Port | Purpose |
|---|---|
| 3000 | Langfuse — AI trace data forwarding from the Platform and StudioWorkspace instances to the Observability instance |
Network Communication
WME instances communicate in two ways:
- External access — Developer machines access WaveMaker Studio and deployed applications via port 443 using DNS names. DevOps teams access the Observability UI via port 443 (DNS) or port 5050 (IP fallback). Admin machines connect to all instances over port 22 (SSH) and to the Platform over port 8080 (config portal). See External Access Ports.
- Internal communication — All WME instances communicate with each other within the platform's private network over the ports listed in Internal Communication Ports. None of these are exposed externally.
The following diagram shows the network communication between all WME instances and external access points.
Capacity Planning
WME AI capacity scales horizontally — add more StudioWorkspace or AppDeployment instances to support more concurrent developers or deployments.
Studio Workspace — each 32 GB StudioWorkspace Instance supports the following number of concurrent developer logins, depending on app type:
| Application Type | Concurrent developer logins per instance |
|---|---|
| WEB | 18 |
| App-Preview-ESBuild | 18 |
| App-Preview-expo | 4 |
AppDeployment — each 32 GB AppDeployment Instance supports up to 20 concurrent app deployments.
Capacity is also governed by your license terms — the number of apps that can be developed or deployed cannot exceed what your license allows, regardless of infrastructure size. Add separate instances for each stage in your release pipeline.
WME AI Setup Artifacts
WaveMaker provides the installation artifacts — installer files and images — required to set up WME AI. Before running the installer, ensure each machine is prepared with the OS, Docker, and other software listed in the system requirements above.