> ## Documentation Index > Fetch the complete documentation index at: https://helix-claude-document-return-objects-rxi6v.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # 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](https://github.com/helixdb/graph-vector-bench)* *** ## 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 | *** ## 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) *** ## 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" | *** ## 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 | *** ## Detailed Results ### 1 · PointGet — Simple ID Lookup Retrieve single item by ID (product detail, user profile). **Winner:** HelixDB — 12x Postgres, 16x Neo4j PointGet Performance PointGet FixedQPS #### 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 | *** ### 2 · OneHop — Graph Traversal Fetch all items a user interacted with (\~400 edges/user). **Winner:** HelixDB — 5.9x Neo4j, 13x Postgres OneHop Performance OneHop FixedQPS #### 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 | *** ### 3 · OneHopFilter — Filtered Traversal Find items a user interacted with in a specific category. **Winner:** HelixDB — 4.2x Neo4j, 20x Postgres OneHopFilter Performance OneHopFilter FixedQPS #### 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 | *** ## 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 | *** ## 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) *** ## 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. *** *Benchmark data + scripts: [github.com/helixdb/graph-vector-bench](https://github.com/helixdb/graph-vector-bench)*