Private AI infrastructure on your servers or your cloud.
Build and run AI-generated apps on-premises or in your AWS, Google Cloud, or Azure account, with self-hosted models, role-based access, and encrypted secrets.
On-Premises
Run the full platform inside your own data center. Application containers, databases, and storage stay entirely within your network perimeter.
Private Cloud
Deploy into your own AWS, Google Cloud, or Azure account. You keep ownership of the infrastructure and billing relationship. We provide the operational layer.
Hybrid
Split workloads across on-premises and cloud environments, with unified deployment, monitoring, and backup workflows across both.
Self-host the whole platform
Installs with a deployment kit on RHEL 9, CentOS Stream, Rocky Linux, or AlmaLinux. Runs on a single host, or splits core, API, web, and app worker hosts across machines. One configuration file; secrets left empty are generated on first run. Includes the reverse proxy with automatic TLS, PostgreSQL, Redis, and S3-compatible object storage.
Use models you control
Run the AI Builder agent on Claude, GPT, Gemini, or Grok with your own API keys. Point it at any OpenAI-compatible endpoint, including a model you host yourself. Prompts and code go only to the model endpoint you configure. Choose the provider and model per project.
Deployment
Deploy AI-generated apps to on-premises servers or your private cloud. Container-based runtime with no vendor-specific build steps. Automated builds from a prompt, Git repository, or existing source. Versioned releases with one-click rollback.
Databases
Managed PostgreSQL, MySQL, MongoDB, and Redis on infrastructure you control. Scheduled backups with downloadable, standard dump files. Restore in place or into another database of the same engine. Application data stays on infrastructure you run.
Networking
Custom domains and internal service networking. Private service-to-service communication by hostname. Private VPC and VNet databases on AWS and Azure. Deploy to the region you choose on Google Cloud, AWS, or Azure.
Monitoring
Unified build and runtime logs. Health checks and uptime monitoring. Container and database metrics, with email alerts when a deployment fails. Audit log of deploys, restores, database queries, and managed database changes.
Backups
Scheduled database backups with configurable retention. One-click restore to the same or another environment. Signed, access-controlled backup downloads. Disaster recovery without third-party services.
Security & Compliance
Role-based access control with six roles, including a read-only Auditor. Organization-level isolation for every project and deployment. Secrets and cloud credentials encrypted with AES-256-GCM. Two-factor authentication and scoped, expiring access tokens.
How it works
Generate: Describe the app or push existing code. NEXUS AI generates a production-ready project structure, container config, and runtime setup. Connect: Point the deployment at your own infrastructure: on-premises servers, a private cloud account, or a hybrid mix of both. Deploy: The platform builds the container, provisions databases and storage, configures networking, and rolls out the release. Operate: Monitor logs and health, scale replicas, run backups, and roll back releases, all from one control plane.
App plus database plus worker
A typical NEXUS AI deployment can run a web app, a PostgreSQL database, a Redis queue, and a background worker as one connected service group. The app and worker share the same deployment network, so application code can use internal hostnames such as postgresql and redis instead of hard-coded host ports or external database addresses.
Storage and recovery included
Production apps usually need more than compute. NEXUS AI supports persistent filesystem volumes for apps that write to paths such as /data, S3-compatible buckets for uploads and generated files, database backups, in-place restore, and restore into another compatible service in the same organization.
One workflow across interfaces
The same stack can be managed from the dashboard, CLI, REST API, or MCP tools. Developers can deploy from GitHub, add database services, attach storage, run workers, scale replicas, inspect logs, create backups, and recover data without switching between cloud consoles and hand-built scripts.
Public SaaS platforms vs NEXUS AI private infrastructure
| Capability | Public SaaS platforms | NEXUS AI private infrastructure |
|---|---|---|
| Where your data lives | Vendor-controlled cloud accounts | Your own on-premises or cloud infrastructure |
| Vendor lock-in | Proprietary build and runtime formats | Standard containers you can move at any time |
| Data residency | Fixed by the vendor's regions | You choose the region, country, or data center |
| Custom networking | Limited to vendor-exposed settings | Your own network, firewall, and ingress when you self-host |
| AI models | The vendor's models and data handling | Your keys, or a model endpoint you host yourself |
| Audit control | Shared infrastructure, limited visibility | Platform audit log plus the logs of infrastructure you operate |
| Cost model | Usage-based platform fees on top of compute | Run on infrastructure you already own or negotiate directly |
Frequently asked questions
Can NEXUS AI deploy to our own on-premises servers?
Yes. NEXUS AI Enterprise installs on your own Linux servers with a deployment kit and runs application containers, databases, and storage inside your network.
Do you support private cloud deployments in our own AWS, Google Cloud, or Azure account?
Yes. Connect your own Google Cloud, AWS, or Azure account and apps and databases deploy into it, so you keep ownership of the infrastructure and the billing relationship. Enterprise can also run the whole platform inside your cloud account.
How is data residency handled?
Because deployments run on infrastructure you own or control, you choose the region, country, or data center where your data is stored and processed.
What is the support and SLA model for enterprise deployments?
Enterprise deployments include a dedicated point of contact and a support agreement scoped to your infrastructure and uptime requirements. Contact sales to discuss SLA terms.
Can we migrate an existing application onto NEXUS AI private infrastructure?
Yes. Existing Git repositories and container images deploy onto the platform, and database dumps from other hosts can be uploaded and restored.
Can we use our own or self-hosted AI models?
Yes. The AI Builder runs on Claude, GPT, Gemini, or Grok with your own API keys, or on any OpenAI-compatible endpoint, including a model you host yourself. Prompts and code go only to the endpoint you configure, so data retention follows that endpoint's policy.
What does a self-hosted install need?
Linux hosts running RHEL 9, CentOS Stream, Rocky Linux, or AlmaLinux. A deployment kit installs the platform from one configuration file, on a single host or across separate core, API, web, and app worker hosts.
Which user roles are available?
Owner, Admin, Member, Deployment Manager, Auditor (read-only), and Billing Manager. Access tokens and AI agent connections carry their own scopes on top of the user's role.
Do you support single sign-on?
Today people sign in with email and password or with Google, with optional two-factor authentication. Tell us about your identity provider requirements when you contact sales.
What does the audit log record?
Deployments, database restores, database queries and schema fixes, and managed database changes, with the acting user and time. Self-hosted installs can also keep their own infrastructure logs.
Where can I find security and compliance details?
See the Security and HIPAA Compliance pages for details on access control, encryption, audit logging, and compliance posture.
About NEXUS AI
NEXUS AI is an agentic AI app builder and full-stack deployment platform. Explore the AI App Builder, learn more on the About page, read the documentation, or contact the team through nexusai.run/contact.
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