Turbopuffer vs. Zilliz Cloud: A Performance and Cost Benchmark for Multi-Tenant Vector Search

In a comprehensive evaluation, Turbopuffer and Zilliz Cloud, two serverless vector databases, were compared for their performance and cost efficiency in multi-tenant AI applications. The study revealed significant differences in areas like search accuracy, query latency, and cost management. Turbopuffer, despite its attractive initial pricing model, struggled with search accuracy under multi-tenant filtering, resulting in lower recall rates compared to Zilliz Cloud, which maintained a consistent 0.99+ recall. Moreover, Turbopuffer exhibited higher cold query latencies across all tenant sizes and faced write ingestion stalls under load, while Zilliz Cloud demonstrated more stable performance with lower latencies and no interruptions. The billing structure of Turbopuffer, which charges based on the total namespace size rather than the actual data queried, led to unexpectedly high costs, especially at scale, whereas Zilliz Cloud offered free writes and queries, making it more cost-effective in the long run. Additionally, Turbopuffer's rate limiting posed challenges under high concurrency, affecting the scalability of applications reliant on it. These findings suggest that while Turbopuffer might appear cost-effective initially, Zilliz Cloud provides superior performance and scalability for production-scale multi-tenant vector search applications.

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