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Vector Search Throughput Estimator

This estimator calculates sustainable vector-search throughput from replica count, concurrent query slots, measured query latency, and an operating utilization target. It translates a latency benchmark into total queries per second and an optional daily query volume.

The tool supports planning for semantic search, recommendation retrieval, nearest-neighbor lookup, and other vector-index services. It also shows throughput per replica, which makes it easier to compare hardware or index configurations on a common basis. The calculation assumes stable latency at the entered concurrency and no external bottleneck in embedding, network, storage, or reranking. Because recall settings, filters, cache state, and index growth can shift performance, use benchmark data that represents the intended production workload rather than an isolated best-case test.

Inputs

replicas
slots
ms
%
hours
Result
—
Sustainable vector-search QPS
Throughput per replica—
Estimated daily queries—
Modeled active slots—
Service time—

1. Count replicas
Enter replicas actively serving the same query workload.

2. Enter effective concurrency
Use the number of query slots each replica can sustain at the measured latency.

3. Add measured service time
Use end-to-end vector-search latency for the chosen index, top-k, filters, and recall target.

4. Set utilization and hours
Leave headroom below saturation and choose the number of hours represented in the volume estimate.

5. Review sustainable throughput
Use per-replica and total QPS to evaluate scaling options and planned traffic.

Query service time (seconds) = latency milliseconds / 1,000 Throughput per replica = concurrent slots × utilization / service time Total throughput = replicas × throughput per replica Daily queries = total throughput × operating hours × 3,600

The formula assumes each slot continuously completes one query per entered service-time interval at the selected utilization.

What the result means

The main result estimates the steady-state query rate supported by the entered vector-search replica pool.

Burst capacity and percentile latency require queue-aware load testing and may be lower than the steady-state estimate.

Given: 6 replicas, 24 slots per replica, 55 ms service time, 70% utilization, and 24 operating hours.

Calculation: Service time = 55 / 1,000 = 0.055 seconds. Throughput per replica = 24 × 0.70 / 0.055 = 305.45 QPS. Total throughput = 6 × 305.45 = 1,832.73 QPS. Daily queries = 1,832.73 × 24 × 3,600 = 158,347,636.

Result: Estimated sustainable throughput is about 1,832.73 QPS, or roughly 158.35 million queries during a full day at the modeled load.

What latency value should I enter?

Use a measured end-to-end search service time at representative concurrency. Include the operations that are part of the vector-search tier being modeled.

Are concurrent slots the same as CPU threads?

Not necessarily. A slot is an effective in-flight query position that sustains the benchmarked latency. Thread count alone may overstate useful concurrency.

How do filters affect throughput?

Selective or complex filters can alter candidate generation and memory access patterns. Benchmark with the same filter distribution expected in production.

Can I multiply single-query QPS by replicas?

Only when replicas scale independently and no shared bottleneck limits them. This calculator assumes near-linear scaling across the entered replica count.

Why keep utilization below 100%?

Operating at saturation leaves no room for query variability or bursts and can cause rapidly growing queues. Headroom improves stability and tail latency.