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

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

Vector Index Algorithms Compared: HNSW, IVF, ScaNN, DiskANN is also a cost-per-conversation problem hiding in plain sight. Once you instrument tokens-in, tokens-out, tool calls, ASR seconds, and TTS seconds against booked-revenue per call, the right tradeoff between Realtime API and an async ASR + LLM + TTS pipeline becomes obvious — and it's almost never the same answer for healthcare as it is for salons.

Broader technology framing

The protocol layer determines what's possible: WebRTC for browser-side widgets, SIP trunks (Twilio, Telnyx) for PSTN voice, WebSockets for the Realtime API streaming session. Each has its own jitter buffer, its own ICE/STUN dance, and its own failure modes when a customer's corporate firewall is hostile.

Front-end is Next.js 15 + React 19 for the marketing surface and the in-app dashboards, with server components used heavily for the SEO-critical pages. Backend splits across FastAPI for the AI worker, NestJS + Prisma for the customer-facing API, and a thin Go gateway that does auth, rate limiting, and routing — letting each service scale on its own characteristics.

Datastores: Postgres as the source of truth (per-vertical schemas like healthcare_voice, realestate_voice), ChromaDB for RAG over support docs, Redis for ephemeral session state. Postgres RLS enforces tenant isolation at the row level so a misconfigured query can't leak across customers.

FAQ

What's the right way to scope the proof-of-concept? Setup runs 24 hours, the trial is 7 days with no credit card, and pricing tiers are $49, $99, and $149 — so a vertical-specific pilot is a same-week decision, not a quarterly project. For a topic like "Vector Index Algorithms Compared: HNSW, IVF, ScaNN, DiskANN", that means you're not starting from scratch — you're configuring an agent template that's already been hardened across thousands of conversations.

How do you handle compliance and data isolation? Day one is integration mapping (scheduler, CRM, messaging) and prompt tuning against your top 20 real call transcripts. Day two through five is shadow-mode running, where the agent transcribes and recommends but a human still answers, so you can compare side-by-side. Go-live is the moment your eval pass-rate clears your internal bar.

When does it make sense to switch from a managed model to a self-hosted one? The honest answer: it scales until your tool catalog gets stale. The agent is only as good as the integrations it can actually call, so the operational discipline is keeping schemas, webhooks, and fallback paths green. The platform handles the rest — observability, retries, multi-region routing — without your team owning the GPU layer.

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