AI SaaS Development
AI SaaS Development
Full-cycle development of AI SaaS products: from idea and AI architecture to a scalable, multi-tenant platform with billing, auth and production infrastructure.
Who it's for
- Startups building an AI product from scratch
- Companies bringing an internal AI tool to market
- Founders with an AI service idea and no technical team
Outcome
- An AI SaaS product: from concept to launch
- Scalable architecture: multi-tenancy, billing, auth
- A product ready to acquire customers
Problems it solves
An AI idea does not become SaaS on its own: you need product strategy, AI architecture, multi-tenant infrastructure and monetization. We walk the entire path systematically.
Who it's for
You have an idea but no architecture or product
The AI prototype is not ready for dozens of users
No billing, authentication or access management
LLM call costs eat the margin
It's unclear how to position and sell the AI product
When you need it
You want to launch an AI service as a commercial product
You need an MVP with an architecture that can scale
A full stack is required: frontend, API, database, infrastructure
You are looking for a technical partner for AI SaaS
Approach
We build AI SaaS as a product: strategy and architecture, then an MVP with a production foundation, then launch and growth.
01
Discovery
Research, requirements and AI opportunity analysis
02
Architecture
AI system design: agents, RAG, data layer
03
MVP
Working product delivered in weeks, not months
04
Production
Deployment, monitoring, scaling and iteration
Technical expertise
The stack we build AI SaaS products on.
OpenAI / Claude / Gemini
The product's AI core: generation, analysis, agents
OpenRouter
Multi-provider support and LLM cost control
AI Agents
Product AI functionality
RAG
Working with user data
React / Next.js
Frontend and API in one stack
PostgreSQL
Data, multi-tenancy, analytics
Expert articles
How we approach this in our own products — from the knowledge base.
FAQ
What does AI SaaS product development include?
Product strategy, AI architecture, the full stack (frontend, API, database), multi-tenancy, authentication, billing, deployment and monitoring. Plus LLM work: model selection, agents, cost control.
How much does it cost to build an AI SaaS?
An AI SaaS MVP starts from $5,000-10,000 depending on the complexity of AI functionality. A full product with billing and scaling is discussed after idea analysis.
How are LLM costs controlled in the product?
We build routing through OpenRouter: choosing the optimal model per request, caching, per-user limits and cost analytics. This protects the product margin.
Do you do development only, or strategy too?
Both. We start with product strategy and AI architecture, then move to development. You get not just code, but a product with a clear monetization plan.
Will the code be ours?
Yes. Code, infrastructure and documentation are fully transferred to you. If you wish, we can continue support and development after launch.
How fast can an MVP be launched?
A typical AI SaaS MVP takes 6-12 weeks. Timelines depend on AI functionality and the number of integrations. A first working prototype usually takes 3-4 weeks.