From shiny object to sober reality: The vector database story, two years later

When I first wrote “Vector databases: Shiny object syndrome and the case of a missing unicorn” in March 2024, the industry was awash in hype. Vector databases were positioned as the next big thing — a must-have infrastructure layer for the gen AI era. Billions of venture dollars flowed, developers rushed to integrate embeddings into their pipelines and analysts breathlessly tracked funding rounds for Pinecone, Weaviate, Chroma, Milvus and a dozen others.

The promise was intoxicating: Finally, a way to search by meaning rather than by brittle keywords. Just dump your enterprise knowledge into a vector store, connect an LLM and watch magic happen.

Except the magic never fully materialized.

Two years on, the reality check has arrived: 95% of organizations invested in gen AI initiatives are seeing zero measurable returns. And, many of the warnings I raised back then — about the limits of vectors, the crowded vendor landscape and the risks of treating vector databases as silver bullets — have played out almost exactly as predicted.

Prediction 1: The missing unicorn

Back then, I questioned whether Pinecone — the poster child of the category — would achieve unicorn status or whether it would become the “missing unicorn” of the database world. Today, that question has been answered in the most telling way possible: Pinecone is reportedly exploring a sale, struggling to break out amid fierce competition and customer churn.

Yes, Pinecone raised big rounds and signed marquee logos. But in practice, differentiation was thin. Open-source players like Milvus, Qdrant and Chroma undercut them on cost. Incumbents like Postgres (with pgVector) and Elasticsearch simply added vector support as a feature. And customers increasingly asked: “Why introduce a whole new database when my existing stack already does vectors well enough?”

The result: Pinecone, once valued near a billion dollars, is now looking for a home. The missing unicorn indeed. In September 2025, Pinecone appointed Ash Ashutosh as CEO, with founder Edo Liberty moving to a chief scientist role.  The timing is telling: The leadership change comes amid increasing pressure and questions over its long-term independence.  

Prediction 2: Vectors alone won’t cut it

I also argued that vector databases by themselves were not an end solution. If your use case required exactness — l ike searching for “Error 221” in a manual—a pure vector search would gleefully serve up “Error 222” as “close enough.” Cute in a demo, catastrophic in production.

That tension between similarity and relevance has proven fatal to the myth of vector databases as all-purpose engines. 

“Enterprises discovered the hard way that semantic ≠ correct.”

Developers who gleefully swapped out lexical search for vectors quickly reintroduced… lexical search in conjunction with vectors. Teams that expected vectors to “just work” ended up bolting on metadata filtering, rerankers and hand-tuned rules. By 2025, the consensus is clear: Vectors are powerful, but only as part of a hybrid stack.

Prediction 3: A crowded field becomes commoditized

The explosion of vector database startups was never sustainable. Weaviate, Milvus (via Zilliz), Chroma, Vespa, Qdrant — each claimed subtle differentiators, but to most buyers they all did the same thing: store vectors and retrieve nearest neighbors.

Today, very few of these players are breaking out. The market has fragmented, commoditized and in many ways been swallowed by incumbents. Vector search is now a checkbox feature in cloud data platforms, not a standalone moat.

Just as I wrote then: Distinguishing one vector DB from another will pose an increasing challenge. That challenge has only grown harder. Vald, Marqo, LanceDB, PostgresSQL, MySQL HeatWave, Oracle 23c, Azure SQL, Cassandra, Redis, Neo4j, SingleStore, ElasticSearch, OpenSearch, Apahce Solr… the list goes on.

The new reality: Hybrid and GraphRAG

But this isn’t just a story of decline — it’s a story of evolution. Out of the ashes of vector hype, new paradigms are emerging that combine the best of multiple approaches.

Hybrid Search: Keyword + vector is now the default for serious applications. Companies learned that you need both precision and fuzziness, exactness and semantics. Tools like Apache Solr, Elasticsearch, pgVector and Pinecone’s own “cascading retrieval” embrace this.

GraphRAG: The hottest buzzword of late 2024/2025 is GraphRAG — graph-enhanced retrieval augmented generation. By marrying vectors with knowledge graphs, GraphRAG encodes the relationships between entities that embeddings alone flatten away. The payoff is dramatic.

Benchmarks and evidence

• Amazon’s AI blog cites benchmarks from Lettria, where hybrid GraphRAG boosted answer correctness from ~50% to 80%-plus in test datasets across finance, healthcare, industry, and law.  

• The GraphRAG-Bench benchmark (released May 2025) provides a rigorous evaluation of GraphRAG vs. vanilla RAG across reasoning tasks, multi-hop queries and domain challenges.  

• An OpenReview evaluation of RAG vs GraphRAG found that each approach has strengths depending on task — but hybrid combinations often perform best.  

• FalkorDB’s blog reports that when schema precision matters (structured domains), GraphRAG can outperform vector retrieval by a factor of ~3.4x on certain benchmarks.  

The rise of GraphRAG underscores the larger point: Retrieval is not about any single shiny object. It’s about building retrieval systems — layered, hybrid, context-aware pipelines that give LLMs the right information, with the right precision, at the right time.

What this means going forward

The verdict is in: Vector databases were never the miracle. They were a step — an important one — in the evolution of search and retrieval. But they are not, and never were, the endgame.

The winners in this space won’t be those who sell vectors as a standalone database. They will be the ones who embed vector search into broader ecosystems — integrating graphs, metadata, rules and context engineering into cohesive platforms.

In other words: The unicorn isn’t the vector database. The unicorn is the retrieval stack.

Looking ahead: What’s next

• Unified data platforms will subsume vector + graph: Expect major DB and cloud vendors to offer integrated retrieval stacks (vector + graph + full-text) as built-in capabilities.

• “Retrieval engineering” will emerge as a distinct discipline: Just as MLOps matured, so too will practices around embedding tuning, hybrid ranking and graph construction.

• Meta-models learning to query better: Future LLMs may learn to orchestrate which retrieval method to use per query, dynamically adjusting weighting.

• Temporal and multimodal GraphRAG: Already, researchers are extending GraphRAG to be time-aware (T-GRAG) and multimodally unified (e.g. connecting images, text, video).

• Open benchmarks and abstraction layers: Tools like BenchmarkQED (for RAG benchmarking) and GraphRAG-Bench will push the community toward fairer, comparably measured systems.

From shiny objects to essential infrastructure

The arc of the vector database story has followed a classic path: A pervasive hype cycle, followed by introspection, correction and maturation. In 2025, vector search is no longer the shiny object everyone pursues blindly — it’s now a critical building block within a more sophisticated, multi-pronged retrieval architecture.

The original warnings were right. Pure vector-based hopes often crash on the shoals of precision, relational complexity and enterprise constraints. Yet the technology was never wasted: It forced the industry to rethink retrieval, blending semantic, lexical and relational strategies.

If I were to write a sequel in 2027, I suspect it would frame vector databases not as unicorns, but as legacy infrastructure — foundational, but eclipsed by smarter orchestration layers, adaptive retrieval controllers and AI systems that dynamically choose which retrieval tool fits the query.

As of now, the real battle is not vector vs keyword — it’s the indirection, blending and discipline in building retrieval pipelines that reliably ground gen AI in facts and domain knowledge. That’s the unicorn we should be chasing now.

Amit Verma is head of engineering and AI Labs at Neuron7.

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