Introduction
With the increasing demand for AI-powered conversational interfaces, PupaClic has successfully developed and deployed a multilingual chatbot capable of understanding and responding in multiple Indian languages, including Tamil, Telugu, Kannada, Malayalam, Hindi, and English. This chatbot is powered by the SLM, optimized for efficiency and cost-effectiveness.
Developing the Multilingual Chatbot
Developing the Multilingual Chatbot
- Synthetic Data Generation
- Vector Databases for RAG
- Data Cleaning and Preprocessing
Model Training Strategy
- LoRA Fine-Tuning
- Federated Training Approach
Deployment on AWS: Cost Optimization Strategies
AWS SageMaker for Hosting
- Managed Infrastructure
- Auto-scaling
- Model Serving Pipelines
AWS Bedrock for API-based Inference
- Serverless Deployment
- On-Demand Scaling
- Lower Costs for Infrequent Queries
Cost Comparison: EC2 vs. SageMaker + Bedrock
Metric
EC2 Deployment
AWS SageMaker + Bedrock
Model Hosting
g5.2xlarge EC2 (8 vCPUs, 24GB GPU)
SageMaker Inference + Bedrock
Scalability
Fixed
Auto-scaling
Maintainance
High
Managed
Estimated Cost (1000 users, 10K tokens/day for 1 month)
$2,500+
$1,200 – $1,500
Why PupaClic is a Leader in AI Development
Proven Experience
- Successfully built AI solutions across multiple verticals.
Proven Experience
- Expertise in optimizing AI models for cloud deployment.
Conclusion
Deploying a multilingual chatbot with SLM on AWS SageMaker and Bedrock offers a scalable, cost-effective, and efficient solution for enterprises. By leveraging managed AI services, businesses can reduce operational costs while delivering high-quality conversational AI experiences.
