Introducing Native Vector Search With HCL Informix® VectorBlade

HCL Informix® VectorBlade is Here

I told you HCL Informix® wasn’t done. Today, it’s free, it’s shipping, and it’s yours.

A few months ago, I wrote that you don’t need another database for AI. You need the one you already have to do more.

Today, thanks to the impressive efforts of the HCL Informix® engineering team, I get to stop saying “coming soon.”

The HCL Informix VectorBlade is available now, free for HCL Informix 15.0.1.9 and above. No sidecar to deploy. No second system to secure. No new licensing line on the invoice. You turn it on, and your database speaks vectors.

Here’s what you actually get:

• Full read/write vectors. Insert, update, and delete embeddings like any other column.
• HNSW and IVF indexing, shipping today. Not an early preview. Not a line on a roadmap. Today.
• Similarity search in the SQL you are already familiar with. Want a per-tenant centroid? SELECT tenant_id, AVG(embedding) FROM docs GROUP BY tenant_id. That’s it. No new query language. No user-defined function ceremony.
• ACID on vectors. Your embeddings are as consistent as the transaction that wrote them.
• Replication, backup, restore. All of it, working on vectors, on day one.
• RAG without the plumbing. Embeddings live next to the rows they describe. One transaction. One security model. Nothing to sync. Nothing can drift.

So what does that make possible?

Product discovery gets smarter without leaving the catalog. A shopper can ask for a “comfortable headset for long work calls” and find the right SKU even if the product description says “over-ear ANC headphones.” The embedding lives beside price, inventory, and availability, so semantic search can still join against the facts that matter.

Industrial operations can search by pattern, not just threshold. Embed a window of sensor readings, alarm text, or maintenance notes, and you can ask whether today’s vibration signature looks like the bearing failure you saw last March. Time series and similarity search meet in the same engine.

Support teams can stop solving the same problem five different ways. A new ticket can be matched to older tickets that described the same issue in a different language, routed to the team that solved it before, and grounded in resolutions that actually worked.

Regulated records become searchable by meaning while staying under the same controls. Clinical notes, claims, contracts, KYC files, AML records, and case histories can be searched semantically without giving the most sensitive data in the business a second home.

Telecom teams can match alarms, logs, call detail records, and fraud patterns against historical incidents. Rules catch what you already know how to describe. Similarity search helps catch the variant that looks familiar but does not match the rule exactly.

Different industries, same pattern: the value shows up when vectors live beside the operational data they describe. Product records beside inventory. Sensor signatures beside time series. Tickets beside resolutions. Regulated documents beside their policies. Network events beside the systems that act on them.

That is the point: vectors are not a science project off to the side. They belong in the operational database, next to the rows, transactions, policies, and history they describe.

And here’s the part I love: there is no engine fork, no out-of-database copy. Your vectors travel through the same buffer pool, locks, and logs as every other row in the system. That is not a slide. That is the Informix architecture we love.

None of this is new territory for HCL Informix. Back in the late 1990s, the Excalibur Image DataBlade pulled feature vectors from images using neural networks and ranked results by vector distance within a relational database, before anyone thought to call it a “vector database.” So no, the VectorBlade is not a bold new direction. It is Informix coming home.

They say you can’t teach an old dog new tricks. They still haven’t met this one (and having older dogs, I can tell you, we can!)

Informix is a trademark of IBM Corporation in at least one jurisdiction and is used under license.

About Author

About Jean-Georges Perrin

Jean-Georges Perrin has been part of the Informix community for nearly 30 years. Elected to the Board of Directors of the International Informix Users Group (IIUG) in 2002, he served for 15 years and was the first non-US citizen elected to that board. A Lifetime IBM Champion, a distinction he earned through 16 consecutive years of recognition starting in 2009, when he became the first French citizen to receive the title, Jean-Georges has authored two ebooks on Informix, presented at IIUG conferences on topics ranging from 4GL modernization to proving that Informix is not just for legacy applications, and contributed to the Informix community across three continents. He serves in Actian’s CTO Office, focused on data standards and AI strategy. But when HCL Informix does something worth writing about, old habits kick in. He chairs the Linux Foundation's Bitol project, where he leads the development of open standards for data contracts (ODCS) and data products (ODPS). He is the author of several books, including Implementing Data Mesh (O'Reilly) and Spark in Action, 2nd edition (Manning), and is currently writing Building Data Products for O'Reilly. Working on databases in college made him hate them. Then karma introduced him to Informix. He never left.