Case Study: Innoplexus achieves 20x faster AI training, 20x higher crawl throughput and 80% cost reduction with Google Cloud Platform

Innoplexus relying on GCP to capture and analyse life sciences data

Innoplexus is a Germany‑headquartered AI company (with offices in India) that built iPlexus to automate the collection and analysis of billions of public life‑sciences data points to speed drug development. Faced with exploding data volumes (from 10 TB to 200+ TB), high manual curation costs, and the need for an always‑on, scalable platform that could vary resources per task, the company needed to move off hosted infrastructure and improve crawling, extraction and model training performance.

Innoplexus migrated ~90% of its workload to Google Cloud Platform, using BigQuery, Cloud Bigtable, Dataflow, Kubernetes Engine and Cloud Machine Learning Engine with TensorFlow/Keras, plus G Suite for collaboration. The move cut costs by 80% versus hosted physical infrastructure and 20% versus another cloud, increased crawling capacity from 1,000 to 20,000 pages/sec, accelerated model training by 20×, and supported an eightfold increase in scalability.

Innoplexus

Gaurav Tripathi

Co-founder and Chief Technology Officer