Monolithic to Microservices
Your software's speed is your business's edge Adapt. Succeed.
Scalability Challenges: Monolithic applications can become bottlenecks as AI features demand more computational resources and specialized scaling.
Agility & Deployment Speed: Large codebases hinder rapid iteration and deployment of new AI models or features, slowing down time-to-market.
Technology Stack Limitations: Integrating diverse AI frameworks and tools is cumbersome in a tightly coupled monolith.
Resilience & Fault Isolation: A failure in one part of a monolith can bring down the entire AI application, impacting critical operations.
Team Autonomy & Parallel Development: Monoliths often lead to team dependencies, whereas microservices enable independent development and deployment of AI components.
Discover what becomes possible with the right architecture
Independently scale specific AI services like recommendation engine, natural language processing unit based on demand.
Accelerate development cycles for new AI features, allowing for quicker experimentation and deployment.
Use the best language and framework for each AI service, optimizing performance and development efficiency.
Isolate failures to individual microservices, preventing system-wide outages and ensuring continuous AI operation.
Smaller, focused codebases simplify debugging, testing, and updating
individual AI components.
Microservices Migration
Real results from organizations that transformed their monolithic architectures into scalable microservices solutions.
Migrated 1000+ Pages in Just 1 Month
For a Leading EdTech Platform.
The ChallengeA prominent EdTech platform in India faced critical limitations with their monolithic architecture.
Slow feature deployment hindering user experience
Scaling issues for personalized learning AI during peak usage
High maintenance costs eating into growth investments
Growing user base demanding more agile solutions
Strategic migration from monolithic system to microservices architecture, focusing initially on critical user-facing features and AI-driven content recommendations.
The migration allowed for parallel development and deployment, drastically reducing the time required to refactor and launch new features. The modular nature of microservices also led to optimized resource utilization, potentially reducing long-term operational costs.
The EdTech company was able to rapidly iterate on new AI-powered features, such as adaptive assessments and smart tutoring modules, deploying updates weekly instead of monthly.
Specific services, like the content delivery network and AI recommendation engine, could be scaled independently to handle peak loads during exam periods, ensuring a smooth user experience.