Indexing is what makes a vector database fast. The choice of algorithm shapes its speed, accuracy, and cost, so Pinecone makes that choice for you and adapts it automatically as your data grows. This page defines the algorithms Pinecone uses today, explains the adaptive per-slab model behind them, and covers why Pinecone builds its own indexing instead of relying on a general-purpose method like HNSW.

Why Indexing Is Adaptive

A vector database serves very different workloads at once: a few thousand vectors in one namespace, billions in another, some changing every second, some static for months. No single indexing algorithm is optimal across that whole range. A method tuned for a small, fast-changing slab is the wrong choice for a billion-vector slab. The reverse is true too.

Pinecone is serverless and stores data in immutable units called slabs. Because each slab is independent and immutable, Pinecone picks the best algorithm for each one and applies it during background compaction, with no downtime and no work on your side. As Pinecone develops new algorithms, they roll in the same way. The set is not fixed; it improves over time.

Slab size is the main factor in the choice. Pinecone weighs other factors too, so treat the thresholds below as guidelines, not hard limits.

Ananas (Small Slabs)

Used for small slabs, roughly up to 10,000 vectors. Ananas is Pinecone's implementation of SimHash: it randomly rotates the vectors (using the Fast Johnson-Lindenstrauss Transform, or FJLT) and keeps the sign bit of each coordinate. It builds fast and stays light on memory, which suits the many small slabs a live index produces.

PQFS (Medium Slabs)

Used for medium slabs, roughly 10,000 to 100,000 vectors. PQFS is Pinecone's implementation of fast-scan Product Quantization. It compresses vectors so the index stays accurate and memory-efficient as a slab grows.

IVF With PQFS (Large Slabs)

Used for large slabs, roughly over 100,000 vectors. IVF (Inverted File) clusters the vectors and, at query time, scans only the clusters most likely to hold matches instead of the whole slab. Each cluster is itself a PQFS index. This keeps queries fast as a slab scales toward billions of vectors.

New writes land in an in-memory memtable that is searched with an exact brute-force scan. It is small enough that an exact scan is the fastest option, which makes new data queryable within seconds.

Why Pinecone Built Its Own Algorithms

A great ANN algorithm is table stakes. A production vector database needs more: instant freshness as data changes, predictable latency at any scale, and no manual tuning. Pinecone designed purpose-built algorithms for its serverless slab architecture to deliver those together, instead of bolting a single general-purpose algorithm onto a system it was not designed for.

Pinecone has never used HNSW. It has always run its own indexing, selected automatically for each slab.

Frequently Asked Questions

What Algorithm Does Pinecone Use?

Pinecone uses a set of proprietary algorithms and selects one per slab automatically, based mainly on slab size: Ananas for small slabs, PQFS for medium, and IVF with PQFS for large. You never select or tune the algorithm.

Does Pinecone Use HNSW?

No. Pinecone has never used HNSW. It uses its own algorithms (Ananas, PQFS, and IVF), chosen automatically for each slab.

What Are Ananas, PQFS, and IVF?

Ananas is Pinecone's SimHash implementation for small slabs. PQFS is Pinecone's fast-scan Product Quantization for medium slabs. IVF handles large slabs, with a PQFS index inside each cluster. Each is matched to the slab size where it runs.

Do I Have to Choose or Tune the Algorithm?

No. Selection, tuning, and upgrades are automatic. Pinecone applies the best algorithm per slab during background compaction, including when it ships improved algorithms, with no reindexing or action from you.