Custom Deployment: Your Infrastructure, Your Rules
The Problem with Off-the-Shelf SaaS
Most automation tools assume you’ll:
- Put your data in their cloud
- Accept their security model
- Use their hosting infrastructure
- Trust their compliance certifications
But many organizations have strict infrastructure rules:
- Financial services: Data must stay in specific regions
- Healthcare: HIPAA compliance requires on-premises or specific cloud providers
- Government: Airgapped environments with no internet access
- Enterprise: Everything must run on AWS or Google Cloud only (not vendor’s infrastructure)
- Privacy-conscious: Need to audit all code and control all data flows
Off-the-shelf SaaS doesn’t work for these requirements. Enterprise contracts offer limited deployment options, usually at 10x the price.
Our Approach: Build for Your Infrastructure
We package and deploy automation solutions to match your exact requirements. Here are real examples:
Example 1: Financial Services—AWS Only, Specific Regions
Client Requirement:
- Everything must run in AWS US-East-1 (regulatory compliance)
- No data can touch servers outside this region
- Need to use their existing AWS account and VPC
- Must integrate with their Active Directory for authentication
What We Delivered:
- Full automation platform deployed via Infrastructure as Code (Terraform)
- Runs entirely in their AWS account
- All data stored in RDS and S3 within US-East-1
- Integrated with their existing AWS SSO and Active Directory
- Automated deployment pipeline (they can redeploy from scratch if needed)
Result: They control everything. If they want to take it fully in-house, they can. No vendor lock-in.
Example 2: Healthcare—On-Premises, Airgapped
Client Requirement:
- HIPAA-regulated patient data cannot leave their data center
- Infrastructure is airgapped (no internet access)
- Must run on their own hardware
- Need to audit all code before deployment
What We Delivered:
- Docker containers packaged with all dependencies
- Complete offline installation package (no internet required)
- Full source code in their internal GitLab
- Documentation for their IT team to deploy and maintain
- Training sessions for their ops team
LLM Requirements: They wanted natural language queries for medical reports, but couldn’t send data to OpenAI/Anthropic.
Solution: We deployed open-source LLMs (Llama) on their hardware:
- Model runs entirely on-premises
- No external API calls
- Patient data never leaves their network
- Full audit trail of every query and response
Result: They get natural language features with complete data sovereignty.
Example 3: Manufacturing—Hybrid Cloud + Edge
Client Requirement:
- Factory floor devices have intermittent connectivity
- Need edge processing for real-time decisions
- Historical data analysis in cloud (Google Cloud only)
- Must work when internet goes down
What We Delivered:
- Edge nodes in factories (process data locally)
- Google Cloud backend for analytics
- Automatic sync when connectivity restored
- Graceful degradation (keeps working offline)
Architecture:
- Edge: Lightweight Python services on factory hardware
- Real-time anomaly detection
- Local data buffering
- Autonomous operation during outages
- Cloud: Google Cloud Platform
- BigQuery for analytics
- Cloud Functions for automation
- Monitoring and alerting
Result: Real-time decisions at the edge, long-term analytics in cloud, resilient to connectivity issues.
Example 4: Government—Airgapped, Complete Isolation
Client Requirement:
- Classified data, fully airgapped network
- Zero internet connectivity
- Must deploy entirely from physical media
- Need to train models on sensitive data that can never leave the facility
What We Delivered:
- Complete offline installation on USB drive
- All dependencies, models, documentation included
- Setup scripts that don’t require internet
- Local model training pipeline (bring your own data)
Process:
- We develop and test in isolated environment
- Package everything on USB drive
- Client’s security team reviews source code
- Client deploys on airgapped network
- Client trains models on their own data (never leaves their facility)
Result: They get AI and automation capabilities while maintaining complete information security.
What “Custom Deployment” Actually Means
When we say we deploy on your infrastructure, here’s what you get:
1. Infrastructure as Code
- Terraform, CloudFormation, or Kubernetes manifests
- Version controlled
- Repeatable deployments
- You can tear down and rebuild anytime
2. Your Cloud Account or Your Hardware
- We deploy to your AWS/GCP/Azure account
- Or your on-premises infrastructure
- Or hybrid (some services on-prem, some in cloud)
- You control the infrastructure, we deliver the automation
3. Complete Source Code
- Everything version-controlled in Git
- You can fork, modify, extend
- No proprietary black boxes
- You own it
4. Documentation for Your Team
- Architecture diagrams
- Deployment runbooks
- Monitoring and troubleshooting guides
- API documentation
- Training for your ops team
5. Support Options (You Choose)
- Full handoff: We deploy, document, train your team, then you own it
- Monitoring retainer: We monitor and alert, you handle routine ops
- Managed service: We run it end-to-end, you just use it
Technology Choices Based on Your Requirements
Cloud Deployments:
- AWS: Lambda, ECS, RDS, S3, EventBridge
- Google Cloud: Cloud Functions, Cloud Run, BigQuery, Pub/Sub
- Azure: Functions, App Service, Cosmos DB, Event Grid
On-Premises:
- Docker containers
- Kubernetes (if you already run it)
- Traditional VMs (if that’s your standard)
- Edge devices (Raspberry Pi, industrial PCs, etc.)
Data Storage:
- Your preferred database (PostgreSQL, MySQL, SQL Server, MongoDB, etc.)
- Your existing data warehouse (Snowflake, BigQuery, Redshift, etc.)
- On-premises storage (NAS, SAN, object storage)
LLMs and AI:
- Cloud APIs (OpenAI, Anthropic) if you’re comfortable with that
- Self-hosted open-source models (Llama, Mistral, etc.) if you need data privacy
- Hybrid (use cloud for non-sensitive, self-hosted for sensitive data)
Real Talk: What This Costs
Custom deployment isn’t free. Here’s the cost breakdown:
One-Time Costs:
- Initial development and integration
- Infrastructure setup and configuration
- Documentation and training
- Security review and compliance work
Ongoing Costs (Your Choice):
- DIY: You run it yourself, no ongoing cost to us (just your infrastructure costs)
- Monitoring: We monitor and alert, you handle incidents (~10-20% of initial cost annually)
- Managed: We run everything (~30-50% of initial cost annually)
Trade-Off:
- SaaS is cheaper upfront, more expensive long-term (monthly fees forever)
- Custom deployment is more upfront, but you own it (no recurring vendor fees)
For most clients, custom deployment pays for itself in 18-36 months compared to SaaS pricing.
Who This Is For
You need custom deployment if:
- You have strict data residency requirements
- You need to audit all code for security/compliance
- Off-the-shelf SaaS doesn’t meet your infrastructure rules
- You want to own the solution long-term (not rent forever)
- You have sensitive data that can’t touch vendor infrastructure
You probably don’t need it if:
- You’re comfortable with standard SaaS hosting
- You don’t have regulatory restrictions
- You want the vendor to handle all infrastructure
Let’s Talk About Your Requirements
No sales pitch. No pressure to buy something you don’t need.
Show us your infrastructure constraints. Your compliance requirements. Your deployment preferences.
We’ll tell you honestly:
- Whether custom deployment makes sense for your use case
- What it would take to deploy on your infrastructure
- Realistic timeline and cost
- Long-term support options