On-Premise Deployment

The problem

Your CISO says: “We can’t use SaaS AI tools. Data residency regulations prevent it.”

You’re in one of these situations:

• Financial services: Customer data can’t leave your network (SOC 2, PCI-DSS)
• Healthcare: HIPAA prohibits sending PHI to third parties
• Government: FedRAMP or classified data requirements
• Legal: Attorney-client privilege prevents cloud AI usage
• Manufacturing: Intellectual property too sensitive for SaaS

Result: Your competitors use AI. You don’t. You fall behind.

The solution

Alpha Agent offers fully on-premise deployment — the AI assistant runs entirely in your infrastructure:

Deployment Options

1. Air-Gapped Deployment

For maximum security (classified, financial, healthcare):

• Alpha runs entirely offline (no internet connection required)
• Use your own fine-tuned models (no OpenAI/Anthropic API calls)
• All data stays on your physical servers
• Meets strictest compliance requirements

2. VPC Deployment

For cloud-native enterprises (AWS, GCP, Azure):

• Alpha runs in your VPC (Virtual Private Cloud)
• Private connections to your internal tools
• PrivateLink/VPC Peering to your databases
• Your data never touches the public internet

3. Hybrid Deployment

For enterprises with mixed requirements:

• Sensitive data processing on-premise
• Non-sensitive tasks use cloud models
• Smart routing based on data classification
• Best of both worlds

What you control

Infrastructure:

• Your servers, your data centers, your cloud accounts
• Kubernetes, Docker, or bare metal — your choice
• Auto-scaling policies you define
• Backup/disaster recovery your way

AI Models:

• Self-hosted OpenAI (Azure OpenAI, for example)
• Self-hosted Anthropic (AWS Bedrock)
• Your own fine-tuned models (Llama, Mistral, etc.)
• Model routing rules you control

Data:

• All prompts, responses, logs stay in your infrastructure
• Data residency guarantees (EU, APAC, US-Gov)
• Retention policies you define
• Zero third-party data sharing

Security:

• Your SSO provider (Okta, Auth0, Azure AD)
• Your secrets management (Vault, AWS Secrets Manager)
• Your network policies (firewalls, VPNs, zero trust)
• Your audit logging infrastructure

Real example

A global bank with 500+ employees deployed Alpha on-premise:

• Air-gapped environment (zero internet connectivity)
• Fine-tuned Llama 3.1 models (trained on internal data)
• SSO with Okta (centralized access control)
• Full audit logs to their SIEM (Splunk)

Result:

• CISO approved it (only AI platform they’d allow)
• Zero data leakage incidents
• 100% compliance with SOC 2, PCI-DSS, GDPR
• 500 employees using AI securely

Read the full case study →

Who it’s for

• Financial services (banks, insurance, trading firms) with strict data regulations
• Healthcare (hospitals, pharma, health tech) requiring HIPAA compliance
• Government (federal, state, defense contractors) needing FedRAMP/classified deployment
• Legal firms with attorney-client privilege requirements
• Manufacturing with sensitive IP and trade secrets
• Any enterprise where “SaaS AI” is a non-starter

Deployment process

Week 1: Planning

• Architecture review with your team
• Compliance requirements mapping
• Model selection (self-hosted vs. cloud)
• Infrastructure sizing

Week 2: Staging Deployment

• Deploy Alpha in your staging environment
• SSO integration testing
• Integration connectors setup (Jira, Slack, etc.)
• Security review + penetration testing

Week 3: Pilot Rollout

• 10-20 pilot users
• Monitor performance, costs, usage
• Iterate on configurations
• Security team validation

Week 4+: Production Rollout

• Gradual rollout to all employees
• Training sessions for teams
• Support handoff to your IT team
• Ongoing optimization

Total time to production: 3-6 weeks (depending on compliance requirements)

Support & SLA

Enterprise on-premise deployments include:

• Dedicated Slack channel with Alpha engineering team
• 24/7 emergency support (P0/P1 incidents)
• Quarterly architecture reviews
• Proactive security updates
• Annual penetration testing
• Custom feature development (if needed)

SLA:

• 99.9% uptime (measured in your infrastructure)
• < 4 hour response time for P0 incidents
• < 24 hour response time for P1 incidents

Pricing

On-premise deployments start at $50,000/year for:

• Up to 100 employees
• Standard integrations (Slack, GitHub, Jira, etc.)
• Kubernetes deployment templates
• SSO integration
• 8x5 support

Enterprise add-ons:

• Custom integrations: $10K-$50K (depending on complexity)
• 24/7 support: +$20K/year
• Dedicated customer success manager: +$30K/year
• Custom model fine-tuning: $25K-$100K (one-time)
• Annual penetration testing: $15K/year

---

Bottom line: If “we can’t use SaaS AI” is blocking your team, Alpha’s on-premise deployment solves it.

Talk to our enterprise team about on-premise deployment, compliance certifications, and custom requirements.