Graph-Vector Database Performance Benchmarks (2025)

Benchmark results for HelixDB, Neo4j, and Postgres on a realistic graph dataset.

At HelixDB we’ve been focused on performance for the last few weeks. Here are our benchmarks for HelixDB, Neo4j, and Postgres (edges as joins) on a realistic graph dataset.

We’re still working on Vector benchmarks - stay tuned!

TL;DR

(10,000 users, 500,000 items, ~4M edges).

• HelixDB crushes graph workloads (5-20x faster) and is your best bet for production graph workloads (GraphRAG, recommendations, social graphs)
• Neo4j 5-20x slower than HelixDB
• Postgres is 10-80x slower

Dataset hash ffed7c34a46dc90e · Conducted November 2025 · Raw data in repo

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How to Read This

Term	Meaning
P50 / P95 / P99	50th / 95th / 99th percentile latency
ops/sec	Successful operations per second (throughput)
FixedConcurrency	Sustained load at N concurrent clients
FixedQPS	Constant request rate, measures latency stability

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Test Environment

Hardware: AWS c6g.2xlarge (eu-west-2) · 8 vCPUs (ARM Neoverse-N1) · 16 GB RAM · 500 GB gp3 EBS

Software: Ubuntu 24.04 LTS · HelixDB v2.1.0 · Neo4j 2025.09.0 (G1GC) · PostgreSQL 16.10

Benchmark: 2s warmup · 5s measurement window · FixedConcurrency (100/200/400/800) + FixedQPS (400/800/1600)

Dataset: 10k users across 25 countries · 500k items across 1k categories · ~4M edges (~400/user)

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Workloads Tested

ID	Name	Description	Example Use
1	PointGet	Retrieve entity by ID	Product detail fetch
2	OneHop	Traverse all user -> item edges	Watch history
3	OneHopFilter	Traverse + filter by category	”Action movies watched”

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Results Summary

Workload	Raw Winner	Performance Gap
PointGet	HelixDB	12x Postgres, 16x Neo4j
OneHop	HelixDB	5.9x Neo4j, 13x Postgres
OneHopFilter	HelixDB	4.2x Neo4j, 20x Postgres

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Detailed Results

1 · PointGet — Simple ID Lookup

Retrieve single item by ID (product detail, user profile).

Winner: HelixDB — 12x Postgres, 16x Neo4j

FixedConcurrency Results

Database	Concurrency	Throughput (ops/sec)	P50 Latency (ms)	P95 Latency (ms)	P99 Latency (ms)
HelixDB	100	90,238.4	1.07	1.29	1.41
Neo4j	100	11,706.0	8.43	9.11	11.30
Postgres	100	15,435.0	6.46	6.61	6.71
HelixDB	200	156,136.0	1.20	1.62	1.94
Neo4j	200	11,802.0	16.77	18.72	19.78
Postgres	200	15,307.4	13.05	13.34	13.47
HelixDB	400	186,642.4	1.92	2.81	3.44
Neo4j	400	11,592.2	34.12	37.61	38.86
Postgres	400	15,685.6	25.47	25.86	26.16
HelixDB	800	186,590.8	3.57	6.73	8.62
Neo4j	800	11,771.8	67.52	70.90	71.72
Postgres	800	15,670.2	51.05	51.95	52.94

FixedQPS Results

Database	Target QPS	Actual Throughput (ops/sec)	P50 Latency (ms)	P95 Latency (ms)	P99 Latency (ms)
HelixDB	400	400.2	0.87	0.90	0.97
Neo4j	400	400.2	0.87	0.91	0.93
Postgres	400	400.2	0.42	0.44	0.46
HelixDB	800	800.2	0.87	0.90	0.96
Neo4j	800	800.2	0.87	0.94	0.97
Postgres	800	800.0	0.42	0.50	0.53
HelixDB	1600	1,600.0	0.93	0.99	1.10
Neo4j	1600	1,600.0	0.92	0.99	1.03
Postgres	1600	1,600.0	0.71	1.33	1.36

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2 · OneHop — Graph Traversal

Fetch all items a user interacted with (~400 edges/user).

Winner: HelixDB — 5.9x Neo4j, 13x Postgres

FixedConcurrency Results

Database	Concurrency	Throughput (ops/sec)	P50 Latency (ms)	P95 Latency (ms)	P99 Latency (ms)
HelixDB	100	14,964.6	6.09	11.87	15.30
Neo4j	100	2,555.0	38.75	42.96	46.08
Postgres	100	1,138.2	87.84	89.38	90.38
HelixDB	200	15,129.2	12.72	18.74	22.21
Neo4j	200	2,574.0	77.53	81.04	82.36
Postgres	200	1,148.4	174.13	176.07	177.03
HelixDB	400	15,122.8	25.96	32.00	35.50
Neo4j	400	2,560.0	156.40	160.08	161.52
Postgres	400	1,148.4	348.39	350.02	351.22
HelixDB	800	14,996.8	52.84	58.88	62.44
Neo4j	800	2,562.2	311.93	317.19	318.83
Postgres	800	1,144.6	696.85	698.79	699.19

FixedQPS Results

Database	Target QPS	Actual Throughput (ops/sec)	P50 Latency (ms)	P95 Latency (ms)	P99 Latency (ms)
HelixDB	400	400.2	1.70	2.51	2.65
Neo4j	400	400.2	3.52	3.97	4.09
Postgres	400	400.2	1.27	1.37	1.44
HelixDB	800	800.2	1.51	2.45	2.67
Neo4j	800	800.2	3.62	4.19	4.72
Postgres	800	800.2	1.70	2.70	2.84
HelixDB	1600	1,600.0	1.53	2.43	2.73
Neo4j	1600	1,600.2	4.06	5.09	5.90
Postgres	1600	1,143.8	349.81	351.85	352.76

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3 · OneHopFilter — Filtered Traversal

Find items a user interacted with in a specific category.

Winner: HelixDB — 4.2x Neo4j, 20x Postgres

FixedConcurrency Results

Database	Concurrency	Throughput (ops/sec)	P50 Latency (ms)	P95 Latency (ms)	P99 Latency (ms)
HelixDB	100	32,030.2	2.94	5.19	7.54
Neo4j	100	7,826.0	12.56	14.80	16.85
Postgres	100	1,654.4	60.44	61.32	61.61
HelixDB	200	33,277.0	5.84	8.96	12.93
Neo4j	200	7,660.2	25.87	28.49	29.41
Postgres	200	1,663.6	120.12	121.57	122.07
HelixDB	400	33,540.8	11.67	16.31	19.85
Neo4j	400	7,832.0	50.68	53.65	54.84
Postgres	400	1,653.0	240.47	254.54	256.58
HelixDB	800	33,438.6	23.64	28.32	32.03
Neo4j	800	7,935.8	100.48	104.09	105.07
Postgres	800	1,659.8	482.84	485.47	486.47

FixedQPS Results

Database	Target QPS	Actual Throughput (ops/sec)	P50 Latency (ms)	P95 Latency (ms)	P99 Latency (ms)
HelixDB	400	400.2	1.16	1.21	1.23
Neo4j	400	400.2	1.20	1.25	1.31
Postgres	400	400.2	0.99	1.13	1.31
HelixDB	800	800.2	1.09	1.15	1.19
Neo4j	800	800.0	1.22	1.28	1.32
Postgres	800	800.0	1.00	1.62	1.67
HelixDB	1600	1,600.2	1.09	1.28	1.38
Neo4j	1600	1,600.0	1.20	1.35	1.68
Postgres	1600	1,600.0	4.63	6.50	7.40

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Performance Highlights

Database	Best At	Latency (P50)
HelixDB	Graph operations	0.9-2.5 ms
Neo4j	Graph operations	2.0-2.1 ms
Postgres	SQL	5-40 ms

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Limitations & Reproducibility

What we didn’t test:

• Cold-start latency
• Insertion times
• Memory footprint during ingestion
• Operational complexity

How to reproduce:

• Dataset hash: ffed7c34a46dc90e
• Raw JSON results, configs, and benchmark driver in repo
• Tests: November 2025, AWS c6g.2xlarge (eu-west-2)

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The Bottom Line

For graph workloads (GraphRAG, Agentic systems, recommendations, social graphs): -> Use HelixDB. The 5-20x graph advantage dominates in real-world scenarios where traversals are frequent.

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Benchmark data + scripts: github.com/helixdb/graph-vector-bench