Vector Databases Compared: Speed, Recall, and Cost Under Load

When you’re deciding which vector database fits your workload, you can’t ignore how each option handles speed, recall, and costs—especially once the system’s under real pressure. Redis stands out not just for its efficiency but also for offering a strong balance of accuracy and affordability. But how does it truly compare against other tools, both specialized and general-purpose, as the load grows? Let’s examine what really sets these solutions apart.

Benchmarking Principles for Vector Databases

When benchmarking vector databases, it's important to concentrate on search performance metrics, including recall rates, latency at various percentiles (P50, P95, P99), and queries per second (QPS). These metrics are crucial for assessing which solutions align with your specific requirements.

Adhere to established benchmarking principles by conducting scalability assessments using large vector datasets. This approach enables accurate measurement of how different platforms handle increased loads and concurrent queries.

In addition, it's important to incorporate hybrid workloads in your evaluation. This includes filtered searches that merge vector and metadata queries, offering a more comprehensive assessment of performance in practical scenarios.

Throughout the testing process, monitoring resource utilization—such as CPU and memory consumption—is advisable in order to gain insights into the efficiency of the database under different conditions.

Utilizing advanced benchmarking tools, such as VDBBench, can facilitate the simulation of production workloads that include streaming and multi-modal data. Such tools are beneficial in replicating real-world use cases, thereby providing valuable information for decision-making regarding vector database solutions.

Core Metrics: Speed, Recall, and Cost

When assessing vector databases, the key metrics to consider are speed, recall, and cost.

Speed is quantified in terms of queries per second (QPS) and latency. In this context, Redis demonstrates significant advantages over Amazon Aurora PostgreSQL, with performance metrics showing up to 9.5 times higher QPS and 9.7 times lower latency.

Recall, which measures the accuracy of search results, is another important parameter. Redis achieves a recall rate of 0.98, indicating a high level of accuracy in search results compared to other databases.

Cost efficiency also plays a crucial role, particularly during indexing operations. In comparative analyses, Redis has shown to be up to 2.8 times faster than Milvus in this regard.

Furthermore, when querying high-dimensional datasets, Redis exhibits superior throughput based on benchmark evaluations.

Performance Showdown: Redis vs. Qdrant, Milvus, and Weaviate

In the realm of vector databases, Redis is recognized for its strong performance in terms of throughput and efficiency. It achieves a query performance metric that's 3.4 times higher than that of Qdrant, Milvus, and Weaviate, which is significant for tasks requiring high-throughput retrieval-augmented generation.

Additionally, Redis exhibits an average latency that's four times lower than Qdrant and demonstrates faster indexing capabilities, being 2.8 times quicker than Milvus. This combination of low search delay and rapid data preparation contributes to its effectiveness.

Moreover, Redis maintains a recall rate of 0.98 across various datasets, indicating a balance between retrieval accuracy and performance speed. Its operational complexity is also reduced, yielding consistently favorable performance metrics when subjected to real-world workloads.

Therefore, Redis emerges as a noteworthy candidate among vector databases based on these performance indicators.

General-Purpose Databases: PostgreSQL, MongoDB, and OpenSearch

When assessing general-purpose databases for vector workloads, PostgreSQL, MongoDB, and OpenSearch each present unique advantages and limitations.

PostgreSQL, with the pgvector extension, achieves a query performance of 1,589 queries per second (QPS) at 90% recall, making it a strong contender compared to some specialized vector databases. As a relational database, PostgreSQL features solid operational capabilities and effective indexing, although it may require longer index build times when compared to Qdrant.

MongoDB offers good integration features; however, it isn't specifically optimized for high-scale vector workloads, resulting in lower recall rates relative to PostgreSQL.

OpenSearch is capable of handling vector search, but it has demonstrated limitations in both QPS and reliability when under heavy demand.

Cloud Provider Comparisons: MemoryDB and MemoryStore

While general-purpose databases like PostgreSQL, MongoDB, and OpenSearch can handle vector workloads, cloud-native solutions such as Redis offer distinct advantages in terms of scalability and performance.

Benchmark data indicates that Redis achieves significantly higher query per second (QPS) rates and lower latency when compared to Amazon MemoryDB, specifically reporting up to 3.9 times higher QPS and 4.1 times lower latency while maintaining accuracy in vector searches.

In comparisons with GCP MemoryStore, Redis demonstrates a performance edge approximately 2.5 times greater and faster indexing capabilities.

These findings highlight the importance of database architecture in influencing query throughput, positioning Redis as a strong option for demanding vector workloads that prioritize speed and reliability at scale.

Redis Query Engine: Enhancements and Scalability

As Redis develops its query engine, it has introduced notable enhancements in speed and efficiency, including a 16-fold performance increase on the gist-960 dataset with one million vectors.

Performance benchmarks indicate a 3.4 times higher queries per second (QPS) compared to Qdrant, along with an average latency that's four times lower than that of Milvus.

The Redis query engine now accommodates concurrent access, thereby improving throughput. However, scalability for complex queries remains constrained by single-threaded processing limitations.

Vertical scaling reveals that multi-threading solutions are essential for performance improvement; specifically, achieving a 2x throughput necessitates a 3x increase in thread count, underscoring the importance of optimal configurations for maximizing performance.

Effective Benchmarking Tools and Methodologies

Benchmarking vector databases requires a comprehensive approach that goes beyond basic speed assessments. Effective benchmarking frameworks, such as VDBBench, are essential as they're designed to simulate real-world queries, focusing on data authenticity, workload patterns, and a range of performance metrics.

The visual dashboard of VDBBench allows for the analysis of queries per second (QPS) and recall under varying operational conditions.

In addition, employing tools like VectorDBBench and ANN-Benchmarks is advisable for examining the performance of approximate nearest neighbor (ANN) algorithms.

These tools facilitate the assessment of critical factors such as latency, throughput, and scalability through both serial and concurrent test phases. This multifaceted evaluation provides a clearer understanding of database performance as well as the requirements of production environments.

Recommendations for Diverse Workloads and Use Cases

When selecting a vector database suitable for various workloads and use cases, flexibility is paramount. Redis is recognized for its strong vector search capabilities and consistently high recall rates. It achieves a recall rate of 0.98, accompanied by fast indexing and lower latency, making it a compelling choice for applications requiring demanding recall and approximate nearest neighbor (ANN) queries.

Additionally, in production environments where scaling is necessary, Redis can reduce costs effectively, demonstrated by indexing times that can be up to 2.8 times lower than those of Milvus.

While Milvus and Qdrant are capable of managing substantial computational loads, Redis tends to offer better speed and operational efficiency.

Conclusion

When you're choosing a vector database, don’t just settle for what’s familiar—prioritize performance. Redis stands out with its impressive speed, high recall, and cost-effectiveness, especially under heavy loads. General-purpose databases like PostgreSQL and MongoDB simply can’t match its efficiency. By focusing on the right metrics and benchmarking tools, you'll make smarter decisions for your workloads. Ultimately, the right specialized solution ensures your applications stay fast, accurate, and economical, no matter the demand.