After I first wrote “Vector databases: Shiny object syndrome and the case of a lacking unicorn” in March 2024, the business was awash in hype. Vector databases had been positioned because the subsequent massive factor — vital infrastructure layer for the gen AI period. Billions of enterprise {dollars} flowed, builders rushed to combine embeddings into their pipelines and analysts breathlessly tracked funding rounds for Pinecone, Weaviate, Chroma, Milvus and a dozen others.
The promise was intoxicating: Lastly, a technique to search by which means moderately than by brittle key phrases. Simply dump your enterprise data right into a vector retailer, join an LLM and watch magic occur.
Besides the magic by no means totally materialized.
Two years on, the actuality examine has arrived: 95% of organizations invested in gen AI initiatives are seeing zero measurable returns. And, lots of the warnings I raised again then — in regards to the limits of vectors, the crowded vendor panorama and the dangers of treating vector databases as silver bullets — have performed out nearly precisely as predicted.
Prediction 1: The lacking unicorn
Again then, I questioned whether or not Pinecone — the poster baby of the class — would obtain unicorn standing or whether or not it could turn into the “lacking unicorn” of the database world. In the present day, that query has been answered in essentially the most telling means attainable: Pinecone is reportedly exploring a sale, struggling to interrupt out amid fierce competitors and buyer churn.
Sure, Pinecone raised massive rounds and signed marquee logos. However in follow, differentiation was skinny. Open-source gamers like Milvus, Qdrant and Chroma undercut them on value. Incumbents like Postgres (with pgVector) and Elasticsearch merely added vector assist as a characteristic. And prospects more and more requested: “Why introduce a complete new database when my present stack already does vectors properly sufficient?”
The consequence: Pinecone, as soon as valued close to a billion {dollars}, is now searching for a house. The lacking unicorn certainly. In September 2025, Pinecone appointed Ash Ashutosh as CEO, with founder Edo Liberty transferring to a chief scientist function. The timing is telling: The management change comes amid growing stress and questions over its long-term independence.
Prediction 2: Vectors alone gained’t lower it
I additionally argued that vector databases by themselves weren’t an finish answer. In case your use case required exactness — l ike looking for “Error 221” in a handbook—a pure vector search would gleefully serve up “Error 222” as “shut sufficient.” Cute in a demo, catastrophic in manufacturing.
That pressure between similarity and relevance has confirmed deadly to the parable of vector databases as all-purpose engines.
“Enterprises found the exhausting means that semantic ≠ right.”
Builders who gleefully swapped out lexical seek for vectors rapidly reintroduced… lexical search at the side of vectors. Groups that anticipated vectors to “simply work” ended up bolting on metadata filtering, rerankers and hand-tuned guidelines. By 2025, the consensus is evident: Vectors are highly effective, however solely as a part of a hybrid stack.
Prediction 3: A crowded discipline turns into commoditized
The explosion of vector database startups was by no means sustainable. Weaviate, Milvus (by way of Zilliz), Chroma, Vespa, Qdrant — every claimed delicate differentiators, however to most patrons all of them did the identical factor: retailer vectors and retrieve nearest neighbors.
In the present day, only a few of those gamers are breaking out. The market has fragmented, commoditized and in some ways been swallowed by incumbents. Vector search is now a checkbox characteristic in cloud knowledge platforms, not a standalone moat.
Simply as I wrote then: Distinguishing one vector DB from one other will pose an growing problem. That problem has solely grown tougher. Vald, Marqo, LanceDB, PostgresSQL, MySQL HeatWave, Oracle 23c, Azure SQL, Cassandra, Redis, Neo4j, SingleStore, ElasticSearch, OpenSearch, Apahce Solr… the record goes on.
The brand new actuality: Hybrid and GraphRAG
However this isn’t only a story of decline — it’s a narrative of evolution. Out of the ashes of vector hype, new paradigms are rising that mix the perfect of a number of approaches.
Hybrid Search: Key phrase + vector is now the default for critical functions. Firms discovered that you just want each precision and fuzziness, exactness and semantics. Instruments like Apache Solr, Elasticsearch, pgVector and Pinecone’s personal “cascading retrieval” embrace this.
GraphRAG: The most popular buzzword of late 2024/2025 is GraphRAG — graph-enhanced retrieval augmented technology. By marrying vectors with data graphs, GraphRAG encodes the relationships between entities that embeddings alone flatten away. The payoff is dramatic.
Benchmarks and proof
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Amazon’s AI weblog cites benchmarks from Lettria, the place hybrid GraphRAG boosted reply correctness from ~50% to 80%-plus in take a look at datasets throughout finance, healthcare, business, and legislation.
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The GraphRAG-Bench benchmark (launched Might 2025) offers a rigorous analysis of GraphRAG vs. vanilla RAG throughout reasoning duties, multi-hop queries and area challenges.
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An OpenReview analysis of RAG vs GraphRAG discovered that every method has strengths relying on job — however hybrid mixtures usually carry out greatest.
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FalkorDB’s weblog reviews that when schema precision issues (structured domains), GraphRAG can outperform vector retrieval by an element of ~3.4x on sure benchmarks.
The rise of GraphRAG underscores the bigger level: Retrieval just isn’t about any single shiny object. It’s about constructing retrieval programs — layered, hybrid, context-aware pipelines that give LLMs the proper data, with the proper precision, on the proper time.
What this implies going ahead
The decision is in: Vector databases had been by no means the miracle. They had been a step — an necessary one — within the evolution of search and retrieval. However they aren’t, and by no means had been, the endgame.
The winners on this house gained’t be those that promote vectors as a standalone database. They would be the ones who embed vector search into broader ecosystems — integrating graphs, metadata, guidelines and context engineering into cohesive platforms.
In different phrases: The unicorn isn’t the vector database. The unicorn is the retrieval stack.
Trying forward: What’s subsequent
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Unified knowledge platforms will subsume vector + graph: Count on main DB and cloud distributors to supply built-in retrieval stacks (vector + graph + full-text) as built-in capabilities.
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“Retrieval engineering” will emerge as a definite self-discipline: Simply as MLOps matured, so too will practices round embedding tuning, hybrid rating and graph building.
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Meta-models studying to question higher: Future LLMs might be taught to orchestrate which retrieval technique to make use of per question, dynamically adjusting weighting.
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Temporal and multimodal GraphRAG: Already, researchers are extending GraphRAG to be time-aware (T-GRAG) and multimodally unified (e.g. connecting pictures, textual content, video).
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Open benchmarks and abstraction layers: Instruments like BenchmarkQED (for RAG benchmarking) and GraphRAG-Bench will push the group towards fairer, comparably measured programs.
From shiny objects to important infrastructure
The arc of the vector database story has adopted a traditional path: A pervasive hype cycle, adopted by introspection, correction and maturation. In 2025, vector search is not the shiny object everybody pursues blindly — it’s now a crucial constructing block inside a extra refined, multi-pronged retrieval structure.
The unique warnings had been proper. Pure vector-based hopes usually crash on the shoals of precision, relational complexity and enterprise constraints. But the expertise was by no means wasted: It pressured the business to rethink retrieval, mixing semantic, lexical and relational methods.
If I had been to jot down a sequel in 2027, I think it could body vector databases not as unicorns, however as legacy infrastructure — foundational, however eclipsed by smarter orchestration layers, adaptive retrieval controllers and AI programs that dynamically select which retrieval device matches the question.
As of now, the true battle just isn’t vector vs key phrase — it’s the indirection, mixing and self-discipline in constructing retrieval pipelines that reliably floor gen AI in details and area data. That’s the unicorn we must be chasing now.
Amit Verma is head of engineering and AI Labs at Neuron7.
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