Introducing AI OS for Business & School — our most powerful products yet.Explore →
App Corp
Full-service software engineering
Engineering your experience…
App Corp
Full-service software engineering
Engineering your experience…
Principal Cloud Architect
James is an AWS Certified Solutions Architect with 12 years of infrastructure excellence. He has architected systems for healthcare, fintech, and enterprise SaaS clients processing billions of dollars in transactions annually.
The average startup wastes 38% of cloud budget. Our playbook consistently delivers 40–65% savings in the first 90 days.
AI development costs range from $30K for a proof of concept to $500K+ for production systems. Here's what drives the price and how to budget accurately.
Should you build custom AI or buy a SaaS tool? The answer depends on your competitive advantage, data sensitivity, and operational maturity.
AI agent costs range from $5K for a simple prototype to $200K+ for production systems. Here's what determines the price and how to budget.
AI agent costs add up fast. Here are the specific techniques that reduce operating costs 40-70% while maintaining output quality.
Enterprise RAG has requirements that consumer RAG doesn't: compliance, access control, multiple data sources, and scale. Here's how to build it.
Private RAG keeps your data on your infrastructure. Here's the architecture for on-premise RAG when cloud APIs are not an option.
RAG costs are predictable if you understand the components. Here's the complete cost breakdown for production RAG deployments.
Healthcare RAG has stricter requirements than general RAG. Here's the architecture for HIPAA-compliant, clinically accurate RAG systems.
AI SaaS has different unit economics than traditional SaaS. Here's the complete cost model for AI SaaS products.
AI SaaS infrastructure must handle variable inference loads, per-tenant isolation, and cost-efficient scaling. Here's the architecture.
AI inference is the largest ongoing cost for AI products. Here are the specific techniques that reduce inference costs 40-70%.
Should you run AI models locally or use cloud APIs? The answer depends on scale, privacy requirements, and operational maturity.
Caching AI requests is the easiest cost optimisation to implement. Here are the caching strategies that reduce costs 20-40%.
RAG costs can be reduced 40-60% with the right optimisations. Here are the specific techniques for each RAG pipeline stage.
AI agent costs add up across LLM tokens, tool calls, and compute. Here are the specific techniques that reduce agent costs 40-60%.
SaaS infrastructure costs are optimisable 40-60% with the right techniques. Here's the playbook for SaaS cost reduction.
Healthcare AI security requires HIPAA compliance, PHI encryption, audit logging, and access controls that go beyond standard application security.
Multi-tenancy is the hardest architectural decision in SaaS. Here's how we used Supabase Row-Level Security to enforce tenant isolation at the database layer for an education platform.
Real-time matching sounds simple until you need to handle 500 concurrent users with sub-second response times. Here's the queue-based architecture that made it work.