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Qdrant Performance: Benchmarks, Latency & Limits 2026

Q: What is Qdrant's query latency at scale?

About 12ms p99 on a 10M-vector dataset, which is 25 to 33% faster than Weaviate, at around 16ms, and Milvus, at around 18ms, on the same workload, per a 2026 independent comparison.

Q: What throughput can Qdrant handle?

Up to 4x higher RPS than competitors on some datasets, in Qdrant's own benchmarks. Unlike SaaS products, there is no hard per-namespace cap. Throughput scales with the number of cluster nodes you provision.

Q: How much does binary quantization speed up search?

Up to 40x search speedup versus full float32 vectors, with a 32x memory reduction. Recall stays at 0.98 or higher for 1536-dimensional models like OpenAI ada-002 with 3 to 4x oversampling, but degrades below 1024 dimensions.

Q: What SLA does Qdrant cloud offer?

The standard cloud tier is 99.5% uptime. Premium single-zone is 99.9%. Premium Multi-AZ is 99.95%, the highest tier with a contractual guarantee.

Q: How does Qdrant handle node failures in a cluster?

It uses Raft consensus for cluster topology. If fewer nodes fail than the replication factor, reads and writes continue. Recovery requires more than 50% of nodes to be healthy. Setting write_consistency_factor to match the replication factor blocks writes when a replica is offline, which prevents divergence.

Qdrant hits ~12ms p99 at 10M vectors and up to 4x the RPS of open-source rivals. Binary quantization cuts memory 32x. Multi-AZ cloud carries a 99.95% SLA.

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Qdrant Performance verdict

Verified today·7 sources checked

Qdrant delivers about 12ms p99 at 10M vectors, and up to 4x the RPS of leading open-source alternatives.

Binary quantization cuts memory 32x with a 40x search speedup. Multi-AZ cloud carries a contractual 99.95% SLA.

How to size it

Pick Qdrant for self-hosted or hybrid-cloud RAG workloads where you want the lowest open-source p99 latency, aggressive quantization compression, and no per-namespace throughput cap. Budget for cluster sizing, since throughput scales with nodes, not a managed dial.

Honest limits

The benchmark latency numbers are a mix of vendor-published figures and independent analyses, with no fully independent third-party audit.
Self-hosted throughput scales with node count. There is no per-namespace cap, unlike Pinecone, but you own cluster sizing.
Binary quantization works best for embeddings with 1024 or more dimensions, and performance degrades below that threshold.

p99 latency (10M vectors): ~12 ms
RPS vs competitors: up to 4x
Binary quant speedup: 40x
Binary quant memory: 32x reduction
Multi-AZ SLA: 99.95%

View sources

Benchmark latency (1M to 10M vectors)

Dataset	Vectors	Dimensions	p99 latency
DBpedia OpenAI 1M	1M	1536	~20 ms
Deep-image 10M	10M	96	~12 ms
GIST 960-euclidean	1M	960	competitive
GloVe 100-angular	1.2M	100	competitive
DBpedia OpenAI 1M (vs Elasticsearch)	10M+	various	10x faster indexing
vs pgvector/pgvectorscale	1M	various	48% lower p99 (38.71 ms vs 74.60 ms)

Throughput and RPS profile

Scenario	Result
RPS gain vs competitors (1M OpenAI vectors)	up to 4x RPS
Benchmark server vCPUs / RAM	8 vCPUs / 32 GB RAM
Benchmark client vCPUs / RAM	8 vCPUs / 16 GB RAM
Memory ceiling per engine (benchmark)	25 GB
Elasticsearch indexing vs Qdrant (10M vectors)	10x slower (5.5 hrs vs 32 min)
Rust-based SIMD search engine	HNSW + SIMD on CPU

Quantization compression and speed gains

Method	Memory reduction	Speed gain	Recall (typical)
official_doc	4x	faster CPU ops	<1% error
official_doc	32x	up to 40x	0.98 @ ada-002
official_doc	32x	up to 40x	0.9966 @ 3x oversampling
official_doc	32x	up to 40x	0.98 @ 4x oversampling
official_doc	up to 64x	variable	lower, use when memory is top priority
official_doc	8x	similar to INT8	similar to scalar

Reliability and distributed architecture

Raft consensus protocol maintains cluster topology and collection structure across all nodes
write_consistency_factor sets how many replicas must ack a write; defaults to 1, max equals replication_factor
Cluster continues accepting updates if at least one replica per shard is online (default write_consistency_factor=1)
Raft quorum recovery requires more than 50 percent of nodes to be healthy
Multi-AZ Premium clusters carry a contractual 99.95 percent uptime SLA
Standard cloud tier SLA is 99.5 percent; Premium single-zone is 99.9 percent
Zero-downtime upgrades and automated backups are included in all managed cloud tiers

Scale ceilings and cluster limits

Dimension	Ceiling / rule
Multi-AZ node scaling	multiples of 3 (3, 6, 9...)
GPU indexing	AWS clusters only
Cloud providers	AWS, GCP, Azure
Free tier cluster	0.5 vCPU / 1GB RAM / 4GB disk
Disk speed tiers	Balanced 32GB+, Performance 256GB+ (AWS)
Update queue per shard	1,000,000 pending

Benchmarked results vs peers

On its own benchmark suite Qdrant reports the highest RPS and lowest latencies in almost all scenarios against Weaviate, Milvus, Elasticsearch and Redis
It delivers up to 4x the requests per second of competitors on a 1M OpenAI-vector dataset
An independent 2026 analysis measured ~12ms p99 latency at 10M vectors, ahead of Weaviate (~16ms) and Milvus (~18ms)
The same independent test recorded 48% lower p99 than Postgres with pgvector and pgvectorscale (38.71ms vs 74.60ms)
Against Elasticsearch it indexes roughly 10x faster, 32 minutes versus 5.5 hours for 10M vectors
Benchmarks ran on an 8 vCPU / 32GB server against an 8 vCPU / 16GB client with a 25GB per-engine memory ceiling

Qdrant Performance FAQ

What is Qdrant's query latency at scale?

About 12ms p99 on a 10M-vector dataset, which is 25 to 33% faster than Weaviate, at around 16ms, and Milvus, at around 18ms, on the same workload, per a 2026 independent comparison.

What throughput can Qdrant handle?

Up to 4x higher RPS than competitors on some datasets, in Qdrant's own benchmarks. Unlike SaaS products, there is no hard per-namespace cap. Throughput scales with the number of cluster nodes you provision.

How much does binary quantization speed up search?

Up to 40x search speedup versus full float32 vectors, with a 32x memory reduction. Recall stays at 0.98 or higher for 1536-dimensional models like OpenAI ada-002 with 3 to 4x oversampling, but degrades below 1024 dimensions.

What SLA does Qdrant cloud offer?

The standard cloud tier is 99.5% uptime. Premium single-zone is 99.9%. Premium Multi-AZ is 99.95%, the highest tier with a contractual guarantee.

How does Qdrant handle node failures in a cluster?

It uses Raft consensus for cluster topology. If fewer nodes fail than the replication factor, reads and writes continue. Recovery requires more than 50% of nodes to be healthy. Setting write_consistency_factor to match the replication factor blocks writes when a replica is offline, which prevents divergence.

Sources & verification

Verified by ComparEdgeMethod: Vendor docs, official pages, and selected independent sources

Source	What was checked	Last checked
Qdrant Official	Official product page	July 10, 2026
Digitalapplied Blog Vector Databases For Ai Agents Pinecone Q	Independent reference	July 10, 2026
Qdrant Articles Binary Quantization	Articles Binary Quantization	July 10, 2026
Qdrant Benchmarks	Benchmarks	July 10, 2026
Qdrant Cloud Create Cluster	Cloud Create Cluster	July 10, 2026
Qdrant Documentation Cloud Premium	Documentation Cloud Premium	July 10, 2026
Qdrant Guides Distributed Deployment	Documentation Distributed Deployment	July 10, 2026

Every fact on this Qdrant page is tied to a named source and a verification date. Freshness-sensitive figures trace to the sources above; verify against the vendor before relying on them.