Saved 55% on Recommendation Costs: XGBoost 2.0 vs TensorFlow 2.15 for 1M User Datasets
A benchmark comparing XGBoost 2.0 and TensorFlow 2.15 on a 1 million user recommendation dataset showed XGBoost achieved 55% lower inference costs with comparable accuracy, delivering higher throughput and faster training. The cost reduction translates to $22,000 monthly savings on AWS for a mid-sized recommendation system. XGBoost's efficiency gains are attributed to superior per-vCPU performance and native multi-threading. These results position XGBoost 2.0 as a cost-effective alternative for scalable, latency-sensitive recommendation workloads.
- ▪XGBoost 2.0 achieved 1420 inferences per second per vCPU versus TensorFlow 2.15's 640 on AWS c7g.2xlarge instances.
- ▪Inference cost per day for 1M daily active users was $0.18 with XGBoost and $0.40 with TensorFlow on AWS Fargate.
- ▪XGBoost 2.0 reduced model size by 75% after INT8 quantization and cut training time from 11.7 minutes to 4.2 minutes.
- ▪Both models achieved similar NDCG@10 scores, with XGBoost at 0.781 and TensorFlow at 0.779.
- ▪Twelve surveyed enterprise adopters plan to switch to XGBoost 2.0 for latency-sensitive recommendation systems by Q3 2024.
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