WebCrafters
LANCER
Lancer :
Ideal for quick, focused engagements
Team: ≤ 2 resources
Duration: ≤ 3 weeks
Indicative Budget: ≤ $3,000 USD*
Use Cases: MVPs, prototypes, simple websites, bug-fix sprints
Indicative Pricing: ~$15–$25/hr per resource*
Growth :
Best suited for growing products or structured builds
Team: 2–5 resources
Duration: Up to 4 months
Indicative Budget: Up to $30,000 USD*
Use Cases: Feature-rich web apps, integrations, custom dashboards
Indicative Pricing: ~$15–$35/hr per resource*
Enterprise :
Tailored for high-scale, complex or long-term initiatives
Team: 5+ resources
Duration: More than 4 months
Indicative Budget: Starts from $30,000+ USD*
Use Cases: End-to-end platforms, SaaS products, compliance-heavy systems
Indicative Pricing: ~$25–$50/hr depending on roles*
(Development)
Project Description
Webcrafters, a company aimed at redefining digital imaging for healthcare and wellness solutions. With growing user demand, the firm needed a robust backend to support high-traffic image uploads and real-time processing. The client approached Oodles to enhance performance, modularize the backend, and enable seamless scalability through Django optimization.
Scope Of Work
The client sought to improve response times, reduce image processing latency, and create a modular backend for continuous scaling. The focus areas included API performance, database optimization, asynchronous task handling, and automated deployment. The project aimed to enhance speed, maintainability, and readiness for growth while ensuring a stable, developer-friendly backend.
Our Solution
To address the performance challenges, Oodles initiated the engagement with a detailed audit of the Django-based architecture.
Key Features Implemented:
- Asynchronous Task Queuing: Integrated Celery with Redis to manage high-volume image processing asynchronously.
- Modular API Design: Used Django REST Framework to decouple major service endpoints for better scalability and maintainability.
- Optimized Query Handling: Fine-tuned ORM queries and database indexing to cut down processing delays.
- CI/CD Integration: Established GitHub-based deployment pipelines using Docker and GitHub Actions to reduce release cycles and manual dependencies.
- Load Handling: Implemented load testing benchmarks and adjusted worker concurrency to maintain stable throughput under peak usage.
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