Evaluate vector databases by running the same representative data and queries on each candidate, then comparing recall, latency, throughput, resource use, and cost at a shared quality target. There is no universal best database: the right choice depends on your query patterns, data lifecycle, deployment constraints, and required service levels.
Define the workload before choosing a benchmark
Start by writing down what the application will actually ask the database to do. This turns a vague speed test into a comparison that can inform a production decision.
- Data: corpus size and expected growth, vector dimensions, data types, and any non-vector fields used for filtering.
- Queries: query mix, top-k, filter predicates and their selectivity, tenant distribution, and whether searches are hybrid or multimodal.
- Traffic: expected concurrency, sustained query volume, and the rates of inserts, updates, and deletes.
- Service requirements: acceptable retrieval quality, latency, freshness after writes, availability, and deployment model.
- Resources and budget: the compute, memory, storage, and operational capacity available to meet those requirements.
Use the same corpus, query set, top-k, filters, and resource envelope for each candidate. A synthetic approximate-nearest-neighbor (ANN) benchmark can screen options, but it cannot stand in for application-relevant inputs. If the application uses multimodal, multi-vector, or compound queries, include those query types only when they are part of the real workload; BigVectorBench discusses these more heterogeneous evaluation scenarios.
Set a retrieval-quality target and measure recall
For a sample of representative queries, compare each candidate’s approximate nearest-neighbor results with exact nearest-neighbor results. Report recall at the application’s chosen k (Recall@k), preferably showing variation across queries rather than only one aggregate. Set the minimum acceptable quality before comparing speed; otherwise, a faster result may simply be returning fewer of the relevant neighbors.
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Test filtered searches using the application’s real predicates and selectivity. An unfiltered test does not establish filtered performance. For example, MongoDB’s 2025 benchmark describes a Pet Supplies filter matching about 500,000 of 15.3 million items—roughly 3%—and reports that more candidate exploration was needed to reach the same recall. That is a workload-specific vendor observation, not a general performance guarantee. See MongoDB’s benchmark results for the configuration and context.
If search supports an application such as retrieval-augmented generation (RAG), evaluate retrieval or answer quality in that application as well. Database recall measures agreement with an exact-neighbor reference; it does not by itself establish whether users receive useful answers.
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Compare speed at the same quality target
For each system, vary the relevant index and search settings, then report quality and performance together. Record median and tail latency, plus sustained throughput under the expected concurrency pattern. A single peak-QPS figure is not meaningful if it was measured at poor recall, on a different query mix, or with a different resource budget.
Keep build time and memory in view alongside query speed. NVIDIA cuVS recommends reporting recall, latency, throughput, build time, and memory together; its example frames a comparison at a fixed quality level: “At 95% recall, model A builds 3x faster than model B, but model B has 2x lower latency.” Read this as a way to express trade-offs, not as a result for any particular database. See NVIDIA cuVS benchmarking guidance.
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Test filters, quantization, and the data lifecycle
ANN index settings can change the balance among recall, latency, and resource use. HNSW exploration settings, for instance, affect that trade-off in Apache Doris’s documented vector-retrieval tests; results from those tests apply to Doris and their stated setup, not automatically to another system. MongoDB’s benchmark material also illustrates that quantization choices, candidate counts, and filters interact. Scalar quantization from 32-bit floating-point vectors to 8-bit integers is described in its benchmark overview as reducing vector representation memory fourfold, with a possible precision penalty. Binary quantization’s latency, throughput, and memory effects should likewise be measured at the recall target you need.
Include the full data lifecycle, not just an isolated search against a prebuilt index. Measure bulk ingestion and index-build time, incremental writes, updates and deletes, and how quickly new or changed data becomes searchable. Record memory, disk, and CPU or GPU use where relevant, along with compaction, replication, and scale-out behavior if they matter to your service.
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NVIDIA distinguishes tests of a standalone index, a local partition, a globally partitioned index, and the full database system. An index-only benchmark cannot answer operational questions about freshness, memory, disk, compaction, or scale-out. Choose the test scope that matches the decision you are making.
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Estimate the complete configuration each candidate needs to sustain the agreed recall, latency, throughput, storage, and availability targets. Include compute and storage, replicas or other availability provisions, ingestion, and operational overhead. Compare the same traffic, data, and quality target; do not transfer one vendor’s cost result to a different cloud, region, data shape, or traffic profile.
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MongoDB reports that binary quantization used about one fourth the index-serving price in its own 2025 benchmark context. That vendor-reported figure is not a general price ratio: remeasure cost with your infrastructure and the quality target you require. MongoDB describes its published benchmark configurations as starting points that may need adjustment for a team’s data and queries.
Use a repeatable comparison scorecard
| Evaluation area | What to record | How to compare |
|---|---|---|
| Retrieval quality | Recall@k against exact results, including query-level variation | Set the acceptance threshold before comparing performance |
| Query performance | Median and tail latency; sustained throughput at target concurrency | Use the same recall target, workload, and resource envelope |
| Filters and hybrid queries | Real predicates, selectivity, tenant conditions, and query types | Do not infer filtered or hybrid behavior from unfiltered tests |
| Ingestion and updates | Initial load and build time, ongoing writes, update/delete behavior, searchable freshness | Test the lifecycle the application will use |
| Resource use | Memory, disk, CPU/GPU where relevant, and scaling behavior | Count the resources required to meet quality and service targets |
| Cost | Compute, storage, availability, ingestion, and operations | Compare total cost for the same data, traffic, and quality target |
| Operations | Deployment, durability, availability, observability, maintenance, and scale-out needs | Include system constraints, not only ANN index speed |
Document the corpus, query set, software versions, index settings, hardware or cloud configuration, warm-up method, and cost assumptions. Run enough representative queries to observe variability and keep the conditions consistent across candidates. The resulting report should make each trade-off traceable and repeatable.
Interpret published benchmarks as evidence about their test setup
Vendor results can help identify configurations worth testing, but they do not establish a universal winner. MongoDB’s 2025 report describes 90–95% reported accuracy with query latency below 50 ms for its Vector Search configuration on 15.3 million 2,048-dimensional Voyage AI voyage-3-large embeddings with quantization. Those are vendor-reported results for that specific setup, not a promise for another corpus or a direct comparison with every competing database.
Likewise, MongoDB’s benchmark guide says its pages aim to “significantly reduce friction for your first vector test at scale (>10M vectors) when evaluating performance for MongoDB Vector Search.” That explains the guide’s purpose; it is not an independent endorsement. Use published figures to understand what was tested, then test each contender against your workload.
Make the selection against explicit constraints
Compare a specialized vector database with vector search embedded in an existing database if both are plausible fits. Apply the same quality target, workload, and fair resource envelope, and test the full system where operational needs matter. Choose the candidate that meets the application’s quality and service requirements at acceptable total cost and operational effort—not the one with the best isolated headline metric.
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