LLMs vs Specialised Vision APIs: Image Processing Showdown
As AI continues to transform the way we process visual information, a new question arises: should you rely on powerful multimodal large language models or stick with specialised vision APIs? This blog post explores the strengths, weaknesses, and ideal use cases for both approaches — and reveals why the smartest strategy may be to combine them. From automated product tagging to content moderation and document analysis, discover how to build more accurate, scalable, and cost-effective image processing pipelines using the right tools for the job.
Cloud vs Edge: The AI Deployment Strategy for Image Processing in 2025
In 2025, the choice between cloud and edge computing isn’t just about technology — it’s about crafting a strategic AI deployment plan that balances speed, scalability and security. Cloud computing excels in handling complex, large-scale image processing tasks, while edge computing offers unparalleled speed for real-time applications. By combining the strengths of both in a hybrid approach, businesses can reduce latency, safeguard sensitive data and optimize costs. This blog explores how to leverage the cloud-edge synergy, adapt to evolving AI advancements and build a future-ready image processing strategy that drives innovation and growth.