Actian VectorAI DB Now Supports IVF Indexing and Vector Quantization

Key Takeaways

Actian VectorAI DB now supports IVF indexing and vector quantization alongside HNSW for more flexible memory, recall, and latency trade-offs.
IVF reduces index size and update cost by clustering vectors, while product and scalar quantization compress vector storage further.
HNSW remains the better choice when high recall at low latency matters most and memory is not a constraint.
IVF is better suited to memory-constrained, frequently updated, disk-based, and edge deployments where a graph index may not fit.
VectorAI DB supports IVF and quantization across cloud, on-premises, edge, and air-gapped environments so teams can tune indexing to each workload.

Actian VectorAI DB already supports HNSW (hierarchical navigable small world) indexing, which remains the right default when recall at low latency matters most and memory is not a constraint.

But memory often is. That’s why Actian VectorAI DB now supports inverted file (IVF) indexing and vector quantization alongside HNSW, helping users work around memory constraints. You pick the memory, recall, and latency profile that fits each deployment target, rather than running one profile everywhere.

IVF shrinks the index and makes updates less expensive, quantization compresses each vector further, and together they fit production vector search onto hardware a graph index would not run on. A semantic search index on a mobile device or an industrial gateway, an e-commerce catalog that changes frequently, or a collection that has outgrown its memory budget: you pick the memory, recall, and latency profile per deployment target instead of running one profile everywhere.

Why IVF and Product Quantization Together Matter More Than Either Alone

Quantization controls how much space each vector takes. IVF controls how much of the index a query has to search and how much has to be rebuilt when data changes. Used together, they address footprint and per-query compute in the same engine.

That combination changes what use cases are viable:

Memory-constrained and edge deployments: A smaller index per vector is what lets semantic search or retrieval-augmented generation run on a mobile device, an industrial gateway, or an IoT sensor with a fixed memory budget. A graph index of the same collection may not fit on the device at all.

Write-heavy and frequently refreshed data: Cluster assignments are cheaper to rebuild or partially update than a graph. E-commerce catalogs with constant inventory turnover and high-volume anomaly detection both benefit here.

Infrastructure cost at scale: A smaller index for the same dataset means fewer or smaller nodes. At hundreds of millions of vectors, memory per vector becomes a node count question and therefore a budget question.

Choosing Between HNSW and IVF

Feature	HNSW	IVF
Recall at low latency	Higher	Lower for the same latency
Index size	Larger (graph plus vectors)	Smaller (vectors plus cluster assignments)
Build time	Longer	Shorter
Update and delete cost	Graph rebuild, compute-intensive	Computationally cheaper to rebuild or partially update
Beyond memory	Needs to fit in memory	Stays performant on disk-based storage

Keep HNSW where recall at low latency is the priority and memory is not the constraint. Reach for IVF when the index has to fit a smaller footprint, when the data changes constantly, or when the collection is growing past what memory can hold.

Whichever you pick, measure it on your own data. A vector database cannot report recall on its own because it has no way to know the correct results for a query. You need ground truth, a test set of queries with known correct answers, and a recall score averaged across hundreds of them. Industry benchmarks are a reasonable starting point for comparison, but only your own query patterns will tell you how a system performs for your workload.

Existing Indexing in VectorAI DB

Until this October 2026 release, VectorAI DB shipped only HNSW. HNSW is a multi-layer graph where each vector is a node connected to nearby neighbors. Searches start with large jumps across a sparse top layer and refine downward until only a small number of candidates need comparing. This delivers high recall at low latency, necessary for recommendation systems, retrieval-augmented generation over a document set, and semantic search across a catalog.

The overhead for HNSW indexing is the graph itself. HNSW stores connections on top of the raw vectors, so the index is meaningfully larger than the vectors alone. Building those connections also takes a significant amount of time.

In cloud-managed vector databases, the service absorbs that memory overhead. However, for on-premises or at the edge, there is no elasticity to absorb it. The overhead becomes a constraint.

What is IVF?

IVF partitions vectors into clusters instead of building a graph. Each cluster has a “centroid,” a synthetic point at the average position of the vectors assigned to it. Every vector is assigned to the cluster with the nearest centroid.

Search runs in two stages. The query is first compared against the centroids and the most promising clusters are identified. This stage is inexpensive because there are far fewer clusters than vectors. The query then searches inside a subset of them. The size of this subset is configurable: the amount is controlled by a parameter. Low values searching a smaller subset returns results faster, while searching a larger subset raises recall at the cost of latency.

Three properties follow from that structure:

The index is smaller, because it holds vectors and cluster assignments rather than a graph.
Recall (accuracy) is tunable at query time, so you can dial it against a specific latency target without rebuilding anything.
The cluster layout stays performant against disk-based storage, so the index is not capped by available memory.

The tradeoff is tuning effort. To match HNSW’s recall, IVF has to search more clusters, which increases latency. IVF parameter changes also affect recall, so dialing it in takes more deliberate testing than HNSW’s defaults.

What is Vector Quantization?

Quantization compresses each vector into a smaller representation, trading some numeric precision for a smaller data footprint. A full-precision vector stores every dimension at full width. A quantized vector approximates it in a fraction of the space.

VectorAI DB supports three IVF variants:

Flat: Uncompressed vectors in clusters, with no quantization applied. This approach gives the most precision but also has a full vector footprint.
Product quantization: Each vector is split into subvectors and encoded against a learned codebook, which gives the largest footprint reduction
Scalar quantization: Each dimension’s precision is reduced, typically preserving more accuracy, but offering less memory reduction than product quantization.

Quantization answers the “our index is too big to fit in memory” problem directly by trading index size for speed of recall. You choose the balance by picking the compression level that clears the accuracy bar of your use case.

Run IVF Anywhere You Can Run a Container

Index choice is table stakes for a vector database. The range of places that index can run is where VectorAI DB is different.

Regulated industries, sovereign AI mandates, disconnected sites, and privacy-sensitive on-device applications all need vector search inside a boundary they control. A lower-footprint index extends how much of your workload can stay there. That matters most where local inference is needed.

VectorAI DB is a local-first vector database. It deploys as a portable container or an embedded edge runtime, with no cloud service behind it and no data leaving your environment. The same engine can run on a cloud node, an on-premise server, an industrial gateway, or inside an air-gapped facility. IVF and quantization are how VectorAI DB makes these smaller targets viable, bringing the index down to a footprint that fits the hardware.

In other solutions, deploying locally usually means accepting lower throughput. But VectorAI DB minimizes this tradeoff: it’s benchmarked at 22 times faster than the most common open-source engine.

Try IVF and Quantization on Your Own Data

Download the Actian VectorAI DB Community Edition and build an IVF index for free. For existing VectorAI DB users, there are no new dependencies and nothing to provision. Run your own recall and latency comparison against HNSW, then talk to Actian about a production contract when you are ready.