By Sagar Shankaran, Founder of CallSphere
The four major vector index algorithms in 2026 — HNSW, IVF, ScaNN, DiskANN — and which one fits your scale, recall, and latency budget.
Key takeaways
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.
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]
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.
Implementations: pgvector, Qdrant, Weaviate, Milvus, Pinecone, FAISS, and many more all default to HNSW.
Cluster vectors; at query time, find the nearest cluster centers and search within those clusters.
Less common as a primary algorithm in 2026 but still used in FAISS configurations and some specialized stores.
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Google's algorithm. Combines tree-based partitioning with anisotropic quantization. Designed for very large corpora.
Used in Vertex AI Vector Search and a handful of other deployments.
SSD-friendly graph algorithm. Stores most of the graph on SSD, keeps only a working set in RAM.
Used in some Microsoft tooling and emerging in open-source projects.
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.
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.
Two key parameters:
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Higher M and ef values trade index size and latency for recall. Defaults are usually fine; tune if your workload demands.
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.
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.
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Written by
Sagar Shankaran· Founder, CallSphere
Sagar Shankaran is the founder of CallSphere, where he builds production AI voice and chat agents deployed across healthcare, hospitality, real estate, and home services. He writes about agentic AI, LLM engineering, and shipping voice agents that handle real calls in production.
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