Vector Index Algorithms Compared: HNSW, IVF, ScaNN, DiskANN

Why the Algorithm Matters

Vector databases all expose similar APIs but use different indexing algorithms underneath. The algorithm decides recall, latency, memory cost, and how well the index handles updates. For most workloads the default works; for scale, latency, or cost-sensitive workloads the choice matters.

This piece compares the four major algorithms shipping in 2026 vector databases.

The Field

flowchart TB
    HNSW[HNSW: graph-based] --> Strong1[Strong: in-memory, fast, default everywhere]
    IVF[IVF: inverted file] --> Strong2[Strong: simpler, predictable]
    Sca[ScaNN: quantized + tree] --> Strong3[Strong: Google scale, high recall at compression]
    Disk[DiskANN: SSD-friendly] --> Strong4[Strong: very large corpora, lower memory]

HNSW (Hierarchical Navigable Small World)

The dominant algorithm in 2026. Graph-based: each vector is a node; edges connect nearest neighbors. Search starts at the top and descends through layers.

• Strengths: fast (sub-millisecond at moderate scale); high recall; widely supported
• Weaknesses: memory-heavy (entire graph in RAM); deletes are tricky; index size limits in-memory workloads
• Best for: most workloads under 100M vectors with sufficient RAM

Implementations: pgvector, Qdrant, Weaviate, Milvus, Pinecone, FAISS, and many more all default to HNSW.

IVF (Inverted File)

Cluster vectors; at query time, find the nearest cluster centers and search within those clusters.

• Strengths: simpler; predictable; good for moderate-scale on-disk workloads
• Weaknesses: lower recall than HNSW at the same compute; needs tuning
• Best for: workloads where simplicity matters; legacy systems

Less common as a primary algorithm in 2026 but still used in FAISS configurations and some specialized stores.

ScaNN (Scalable Nearest Neighbors)

Google's algorithm. Combines tree-based partitioning with anisotropic quantization. Designed for very large corpora.

• Strengths: high recall at high compression; Google-scale tested
• Weaknesses: less mainstream; tooling outside Google ecosystem is limited
• Best for: very large corpora where compression matters

Used in Vertex AI Vector Search and a handful of other deployments.

DiskANN

SSD-friendly graph algorithm. Stores most of the graph on SSD, keeps only a working set in RAM.

• Strengths: handles billion-scale corpora with modest RAM; cost-efficient at very large scale
• Weaknesses: higher latency than in-memory HNSW; less mainstream
• Best for: very large corpora where storage cost matters more than latency

Used in some Microsoft tooling and emerging in open-source projects.

Side-by-Side at 10M Vectors, 1024-dim

Approximate 2026 numbers:

Algorithm	Recall@10	p99 Latency	Memory
HNSW	95-98%	5-15ms	~12 GB
IVF (100 lists)	88-92%	10-30ms	~6 GB
ScaNN	95-97%	8-20ms	~3 GB (compressed)
DiskANN	92-95%	30-80ms	~3 GB RAM + SSD

Numbers shift with parameters. Run your own benchmark.

Choosing

flowchart TD
    Q1{Vectors over 100M?} -->|Yes| Q2{RAM-bounded?}
    Q1 -->|No| HNSW2[HNSW: default]
    Q2 -->|Yes| Disk2[DiskANN]
    Q2 -->|No| Sca2[ScaNN or HNSW with sharding]

For most teams in 2026, HNSW is the right answer. Reach for the others only at scale or with specific RAM/SSD constraints.

Tuning HNSW

Two key parameters:

• M: graph connectivity (8-64, typically 16-32)
• efConstruction: build-time accuracy (100-500, higher = slower build, better quality)
• efSearch: query-time accuracy (10-200, higher = slower query, better recall)

Higher M and ef values trade index size and latency for recall. Defaults are usually fine; tune if your workload demands.

What Surprises Engineers

• HNSW deletes are often "soft": the vector is marked as deleted but the graph still includes it. Periodic rebuild needed for clean deletion.
• Updates change the graph; high update rates can degrade recall over time
• Memory cost includes both the vectors AND the graph (more than naive vector storage)
• The "index size" reported by stores often does not include the vector data itself

Sources

• HNSW paper Malkov + Yashunin — https://arxiv.org/abs/1603.09320
• ScaNN paper Google — https://arxiv.org/abs/1908.10396
• DiskANN paper — https://www.microsoft.com/en-us/research
• FAISS documentation — https://github.com/facebookresearch/faiss
• "ANN benchmark" — https://github.com/erikbern/ann-benchmarks

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