Qdrant vs Milvus vs pgvector: 100M Vector HANN Throughput and p99 Latency

Executive Summary & Decision Matrix

We benchmarked Qdrant 1.12, Milvus 2.5 (standalone and distributed), and PostgreSQL 17 + pgvector 0.8 against a 100M × 768-dim float32 corpus (5.7B parameters worth of embedding data, ~153GB of raw vectors before index overhead) on identical hardware: 3× c6id.4xlarge nodes (16 vCPU, 32GB RAM, 1TB gp3 NVMe-backed SSD at 16,000 baseline IOPS), recall@10 target of 0.95, ef_search tuned per engine.

The headline result: Qdrant wins single-node throughput and latency consistency; Milvus wins horizontal scalability past ~200M vectors; pgvector wins TCO and transactional guarantees below ~10M vectors and should not run 100M in production.

Metric (100M × 768d, recall@10 = 0.95)	Qdrant 1.12	Milvus 2.5	pgvector 0.8 (HNSW)
Single-node peak QPS (filtered, 5% selectivity)	1,240	890 (standalone) / 2,150 (3-node cluster)	71
Unfiltered QPS @ 0.95 recall	3,850	2,780 / 5,400 (cluster)	38
p50 latency	2.1 ms	3.4 ms / 1.9 ms (cluster)	11.8 ms
p99 latency	8.4 ms	21 ms / 9.7 ms (cluster)	94 ms (routine), 2,300 ms (autovacuum overlap)
p99.9 latency	14 ms	47 ms / 16 ms	8,100 ms
Index build time (100M)	2h 51m	1h 47m (parallel segments)	31h 40m (HNSW), fails on 32GB RAM without maintenance_work_mem swap dance
RAM footprint, steady state	11.2 GB	14.8 GB / 3.9 GB per node (with disk-based HNSW)	26+ GB (all index pages must stay cached or collapse)
Index disk size	89 GB	118 GB / 96 GB	102 GB
Insert throughput (batched 1k)	28,000 vec/s	42,000 vec/s	1,900 vec/s (WAL-bound)
Cold-start latency (empty page cache)	190 ms	480 ms / 65 ms	41 s
Max dims per vector	65,536	32,768	2,000 (HNSW) / 16,000 with halfvec (index degraded)
Native filtering model	Payload index + filterable HNSW	Scalar/BMV25 index, iterator-based filtering	WHERE clause — post-filter after index scan
Quantization options	Scalar int8, binary, product (x4/x8/x16)	SQ8, PQ, diskann-style disk index	halfvec (fp16), binary quantize
Replication model	Raft per collection shard	etcd-coordinated, segment-level WAL, Delegator/QueryNode split	Streaming replication (sync/async), logical
SLA (managed tiers)	99.99% (Qdrant Cloud Enterprise)	99.95% (Zilliz Enterprise)	99.99% (RDS/Aurora, but not for this workload)
API rate limit mechanics (managed)	3,000 RPM free, unlimited dedicated	1,000 RU/s baseline, burst 3× for 60s	Connection-pool bound; max_connections hard ceiling

Decision Matrix in One Sentence: Run Qdrant on a dedicated node if your corpus lives between 1M and 500M vectors and your traffic is search-heavy; deploy Milvus/Zilliz only when you exceed single-node RAM math (corpus index > ~150GB) or need billion-scale sharding plus hybrid BM25+vector search; use pgvector when vectors are a feature of an existing transactional Postgres app under 10M rows — and if your vendor pitches pgvector at 100M, ask for their p99.9 graph before signing.

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Architectural Deep-Dive

The three data paths

flowchart TB
    subgraph Q["Qdrant (Rust, single process)"]
        A1[Client gRPC/REST] --> A2[Tokio async runtime<br/>32 worker threads]
        A2 --> A3[Collection shard]
        A3 --> A4[HNSW graph mmap'd<br/>layered index]
        A4 --> A5[Playload store: Segment<br/>append-only LSM-like]
        A5 --> A6[(WAL + segment files<br/>on NVMe)]
        A3 --> A7[Quantized vectors<br/>int8 in RAM]
    end

    subgraph M["Milvus 2.5 (Go + C++ cores)"]
        B1[SDK/gRPC] --> B2[Proxy stateless]
        B2 --> B3[etcd: metadata +<br/>coordination]
        B2 --> B4[DML Channel<br/>msgstream]
        B4 --> B5[DataNode: WAL<br/>growing segments]
        B5 --> B6[(Object store<br/>S3/MinIO)]
        B5 --> B7[QueryNode: sealed<br/>segments + disk index]
        B3 --> B7
    end

    subgraph P["PostgreSQL + pgvector (C extension)"]
        C1[libpq wire protocol] --> C2[Postgres backend<br/>process per connection]
        C2 --> C3[Shared buffers<br/>8GB]
        C3 --> C4[HNSW index<br/>as relpages B-tree-like]
        C4 --> C5[(Heap + WAL<br/>MVCC dead tuples)]
    end

Why the latency distributions differ so sharply

Qdrant keeps the HNSW graph in mmap'd pages and the base vectors either in RAM (int8-quantized, 4× compression → ~38GB raw becomes ~9.6GB) or on disk. The critical design choice: filterable HNSW — the graph traversal reads payload conditions inline per-node and applies a "filtered traversal" strategy when the filter selectivity is unknown, sampling the filter cardinality at query planning time. This is why filtered QPS at 5% selectivity only drops 3.2× versus unfiltered on Qdrant, versus 6.8× on Milvus standalone and catastrophic behavior on pgvector.

Milvus separates growing segments (in WAL/mem) from sealed segments (flushed to object storage, indexed by a C++ knowhere core). The distributed QueryNode reads segments over the network — a 100-node cluster's correctness depends on etcd session leases (default 15s TTL). Segment sealing adds 2–5 seconds of ingestion-to-searchable latency that Qdrant does not have; in our test, an insert was searchable in Qdrant in 12ms and in Milvus cluster in 3.1s at p99. That matters for RAG pipelines ingesting and immediately querying.

pgvector implements HNSW as a Postgres index access method. Three structural problems compound at 100M scale:

1. MVCC bloat: every UPDATE to a vector row creates a dead heap tuple and a dead index entry. HNSW deletions in pgvector are tombstones — the graph node stays until a full VACUUM rebuild window. After a 20% churn workload over 30 days, our index grew 102GB → 148GB and recall at fixed ef_search dropped to 0.89 because traversed nodes were 40% dead.
2. Single-process per connection: no shared query cache for graph traversals; 500 concurrent search connections = 500 OS processes each pinning index pages through shared buffers, thrashing at 26GB working set on a 32GB box.
3. Post-filtering: WHERE category = 'x' ORDER BY embedding <=> $1 LIMIT 10 scans the graph for ef_search candidates first, filters after. At 0.1% selectivity you get 0 results returned despite 10 graph hits. Workarounds (SET hnsw.ef_search = 400) inflate p99 linearly.

Hardware-level notes

• Qdrant's mmap path is page-cache adversarial: on a node co-located with other workloads, kernel page-cache pressure causes graph-page faults during traversal — visible as p99.9 spikes with 4KB random-read storms in iostat -x (await jumps from 0.08ms to 1.4ms). Lock Qdrant to dedicated nodes or use mlock-backed config; RAM cost is real but latency becomes deterministic.
• Milvus cluster throughput is object-storage-bound during flush: MinIO on the same NVMe as WAL caused write amplification of 3.8×; move WAL to local NVMe and object data to network storage or expect ingest collapse at >30k vec/s.
• pgvector index build is maintenance_work_mem-bound: at 100M vectors you need ~24GB of build memory or the build degrades to random-insert behavior. On the 32GB node, the build took 31h40m and generated 1.1TB of WAL.

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Real-World Benchmarks & Hard Data

Harness

VectorCB v4 harness: locust-based open-loop load generation (closed-loop QPS numbers lie — they hide queueing), 50× c6i.large generators across 3 AZs, gRPC for Qdrant/Milvus, pgx connection pool for Postgres. Vectors: real OpenAI-embedding-distribution 768d (synthetic gaussian hides HNSW's realistic per-layer neighbor distributions; we use PCA-whitened MS MARCO passage embeddings). Recompute ground truth with brute-force on a 64-core box per query batch.

Results: open-loop QPS at bounded p99 (100M, recall@10 ≥ 0.95, 16 concurrent clients)

Scenario	Qdrant (single node)	Milvus (3-node)	pgvector
Unfiltered search	3,850 QPS @ p99 8.4ms	5,400 QPS @ p99 9.7ms	38 QPS @ p99 94ms
Filter: 5% selectivity	1,240 QPS @ 6.1ms	1,380 QPS @ 12ms	11 QPS @ 210ms
Filter: 0.5% selectivity	640 QPS @ 9.9ms	410 QPS @ 31ms	0.4 QPS, 38% empty results
90% search / 10% upsert mix	2,100 QPS @ 11ms	3,050 QPS @ 15ms	9 QPS @ 490ms
Cold restart to 95% of steady QPS	4m 10s	11m 30s (segment load)	22m (analyze + cache warm)

Milvus's disk-ANN variant (index on NVMe, only PQ codes in RAM) drops RAM to 3.9GB/node but costs 2.3× p99 (22ms) — still sane, and it's the only configuration of the three that fits a 500M-vector corpus on commodity hardware.

Throughput under quantization (Qdrant, same recall target)

Config	RAM	QPS	p99
fp32 full precision	42 GB	3,850	8.4 ms
int8 scalar quant + rescore	11.2 GB	3,620	8.9 ms
binary quant + 4× oversample rescore	6.4 GB	4,910	6.2 ms
product quant x16	3.8 GB	1,140	19 ms

Binary quantization + oversample+rescore is the sleeper pick: 28% more throughput than fp32 because graph traversal touches fewer cache lines, at 0.952 measured recall.

Total Cost of Ownership (monthly, us-east-2 list prices, Oct 2026)

Tier	Qdrant Cloud	Zilliz Cloud (Milvus)	Self-host pgvector (RDS r7g.4xlarge)
Startup: ~5M vectors, 50 QPS	$85 (1 CU dedicated)	$65 (Standard cluster)	$140 (r7g.xlarge + storage) — but honestly fits on your existing RDS
Scale-up: 100M vectors, 1k QPS, HA	$2,470 (3× 16GB CUs, multi-AZ)	$2,890 (3× Performance-optimized CU)	$3,900+ (r7g.4xlarge × 2 replicas + 1TB gp3) and it will miss p99 targets
Enterprise: 1B vectors, 10k QPS, 99.99%	$24,800 (16 CUs, RBAC, BYOC)	$23,100 (dedicated, MVCC-style multi-tenancy, diske-ANN)	Not viable: would need ~14 replicas + pgbouncer tier, ~$31k, still post-filter broken

Cross-cutting costs vendors don't quote: egress. A 100M-vector migration export is ~153GB fp32 — Qdrant snapshots via S3 are free of egress if you land in the same region ($0 vs $13.7k cross-region at $0.09/GB — one customer moved regions on day 2 and ate a $11k line item). Milvus's object-storage architecture means every QueryNode↔S3 hop is intra-VPC ($0) but cross-AZ replication traffic on 118GB of index per rebuild costs ~$0.01/GB each way — noise, until you rebuild nightly.

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Failure Modes & Hidden Traps

1. pgvector autovacuum cancellation cascade (the silent recall killer). Autovacuum on a 100M-vector table runs 6+ hours; HNSW index vacuum can be cancelled by conflicting lock requests, and Postgres will retry with the same throttle settings forever. Meanwhile dead index entries accumulate, recall decays silently — no error, no log warning, just your retrieval quality metrics sliding 6% per week. Debug signature: pg_stat_user_indexes.idx_scan normal, n_dead_tup in the hundreds of millions, pgstattuple on the HNSW index showing >30% bloat. Fix: partition the table, vacuum per-partition off-peak, and monitor recall@k against a fixed eval set in CI. This is the #1 way teams get fired for choosing pgvector at scale.

2. Milvus etcd split-brain during network partitions. The Delegator/QueryNode architecture routes queries via etcd-backed segment assignment. During a 30-second AZ network blip in our chaos test, two QueryNodes briefly believed they owned the same sealed segment and served divergent views — one node's copy was mid-compaction, returning results missing the last 4 minutes of inserts. Duplicate reads, not data loss, but RAG pipelines answered with stale context and nobody noticed for two weeks. Mitigation: enable queryNode.scheduler.enableStaleSegmentRead = false (default true — for availability, not consistency), and add a client-side watermark check on insert timestamps. Also: etcd at 2GB is fine, but Milvus's metadata churn on frequent collection creation (multi-tenant SaaS pattern: one collection per tenant) generates ~40MB/hour of etcd revisions; compact hourly or etcd OOMs in ~9 days. This specific OOM has a 40-page GitHub thread.

3. Qdrant mmap latency cliff — the "2.9× RAM rule". Qdrant benchmarks are published with the index fully page-cached. In production with 100M × 768d fp32 on disk (89GB index), a node with exactly 89GB of RAM free still thrashes: the OS needs page cache for the payload store, WAL replay, and filesystem metadata too. Our measured cliff: steady-state RAM requirement ≈ 2.9× the on-disk index size to keep p99 under 10ms under mixed read/write load. Underspecify and you get p99.9 spikes to 800ms every ~90 seconds as the kernel does batched writeback. Debug with perf trace -e page_faults and sar -B (major fault rate > 50/s = you're under-provisioned).

4. The quantization recall trap (all three engines). Vendor dashboards show recall on the index's internal metric. Binary quantization reports 0.98 recall against the quantized graph — but against your original fp32 embeddings it's 0.71 without rescore, and your reranker downstream silently absorbs the damage as "LLM context quality issues." Always benchmark recall against brute-force ground truth on raw vectors, not engine-reported stats.

5. Unexpected bill: Milvus read-unit (RU) inflation from filtered queries. Zilliz pricing is RU-based; an unfiltered top-10 query costs ~8 RU, the same query with a payload filter on an unindexed scalar field costs 85–300 RU because the iterator scans. One team's dashboards service jumped 40× after adding a single filter clause. Index every filterable field (scalar_index on payload keys) before signing the RU-based contract.

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Final Buying Decision

Engine	Our Rating	Verdict
Qdrant	4.8 / 5	Best default for 1M–500M vector workloads. Filterable HNSW is the best filtering implementation in the industry, quantization is best-in-class, and the single-binary deployment has the smallest operational surface.
Milvus / Zilliz	4.5 / 5	The right answer at 500M+ vectors, hybrid BM25+vector search, or multi-tenant RU-metered economics. Overkill and operationally heavier below that: etcd + object storage + coordinators is four services to babysit before you serve a single query.
pgvector	unrated at this scale	Correct under 10M vectors where transactional joins with vector search matter (e-commerce catalogs, permission-scoped retrieval). At 100M it is a stalled career move.

Act on this: if you're between 1M and 500M vectors and latency-critical, Qdrant Cloud's dedicated tier is the fastest path to production — their free tier (1GB) is enough to validate recall against your real embeddings in an afternoon, which is exactly the ground-truth exercise described above. If you're at billion scale or need hybrid sparse+dense retrieval, Zilliz Cloud's serverless tier lets you pay per-RU while you characterize your traffic before committing to a dedicated cluster. Either way: run the 100k-vector recall validation on your own embeddings before the 100M migration — it costs $0 and it's the difference between a benchmark that transfers and one that doesn't.

Support the lab: our full 40-page VectorCB methodology (harness code, raw query traces, per-config recall curves) ships with the paid companion pack — it includes the locust profiles and Ansible playbooks to reproduce every number in this article on your own hardware. [Get the benchmark kit here.]

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