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

When I first wrote “Vector databases: Shiny object syndrome and the case of a missing unicorn” In March 2024 there was hype in the industry. Vector databases were positioned as the next big thing – an essential infrastructure layer for the genetic AI era. Billions of venture dollars flowed, developers rushed to integrate embeds into their pipelines, and analysts breathlessly followed the funding rounds for them Pine cones, Weave, Chroma, The dragon and a dozen others.

The promise was intoxicating: finally a way to search for meaning instead of brittle keywords. Simply load your business knowledge into vector storage, plug in an LLM, and watch the magic happen.

Except the magic was never fully realized.

Two years later, the Reality check has arrived: 95% of organizations investing in genetic AI initiatives do not see measurable returns. And many of the warnings I raised back then—about the limitations of vectors, the crowded vendor landscape, and the risks of treating vector databases as a panacea—have played out almost exactly as predicted.

Prediction 1: The Missing Unicorn

At the time, I wondered whether Pinecone – the flagship 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: pine cones it is is said to be considering a salestruggles to prevail amid fierce competition and customer churn.

Yes, Pinecone has raised big donations and signed big logos. In practice, however, the differentiation was small. 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 can already handle vectors well enough?”

The result: Pinecone, once valued at nearly 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 taking on the role of chief scientist. The timing is telling: The leadership change comes amid increasing pressure and questions about his long-term independence.

Prediction 2: Vectors alone are not enough

I also argued that vector databases alone are not a final solution. If your use case requires precision – such as searching for “Error 221” in a manual – a pure vector search would happily return “Error 222” as “close enough”. Cute in a demo, disastrous in production.

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

“Companies found out the hard way that semantics ≠ is correct.”

Developers who had happily replaced lexical search with vectors quickly reintroduced lexical search in conjunction with vectors. Teams that expected vectors to “just work” ended up focusing on metadata filtering. Reanchor 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 a commodity

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

Very few of these players break out these days. The market is fragmented, commercialized and in many ways has been swallowed up by incumbents. Vector search is now a checkbox feature in cloud data platforms rather than a standalone moat.

Just as I wrote back then: distinguishing one vector DB from another will be an increasing challenge. This challenge has only become more difficult. Community, Margo, 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 is not just a story of decline – it is a story of evolution. From the ashes of the vector hype, new paradigms are emerging that combine the best of several approaches.

Hybrid Search: Keyword + Vector is now the default for serious applications. Companies have learned that they need both precision and vagueness, exactness and semantics. Tools like Apache Solr, Elasticsearch, pgVector and Pinecone’s own “Cascading Retrieval” take this into account.

GraphRAG: The hottest buzzword at the end of 2024/2025 is GraphRAG – Graph-Enhanced Retrieval Augmented Generation. By connecting vectors to knowledge graphs, GraphRAG encodes the relationships between entities, which are flattened using embeddings alone. The payoff is dramatic.

Benchmarks and evidence

• Amazon’s AI Blog cites benchmarks Lettriawhere hybrid GraphRAG increased answer correctness from around 50% to over 80% in financial, healthcare, industrial and legal test datasets.

• The GraphRAG bench Benchmark (released in May 2025) provides a rigorous evaluation of GraphRAG compared to vanilla RAG for reasoning tasks, multi-hop queries, and domain challenges.

• A OpenReview review of RAG vs. GraphRAG found that each approach has its strengths depending on the task – but hybrid combinations often achieve the best performance.

• Blog reports from FalkorDB When schema accuracy is important (structured domains), GraphRAG can outperform vector retrieval by a factor of ~3.4 on certain benchmarks.

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

What this means for the future

The verdict is: vector databases were never the miracle. They were an important step in the development of search and retrieval. But they are not the end game and never have been.

The winners in this area will not be those who sell vectors as a standalone database. They will be the ones who embed vector search into broader ecosystems – integrating graphics, metadata, rules and context engineering into cohesive platforms.

In other words, the unicorn is not the vector database. The unicorn is the fetch pile.

Looking ahead: what’s next?

• Unified data platforms will combine vector + chart: Expect major DB and cloud providers to offer built-in fetch stacks (vector + graphics + full text) as built-in features.

• “Retrieval Engineering” will emerge as an independent discipline: As MLOps mature, so will the practices around embedding tuning, hybrid ranking, and charting.

• Metamodels learn to query better: Future LLMs can learn to orchestrate which retrieval method to use per query and dynamically adjust the weighting.

• Temporal and multimodal GraphRAG: Researchers are already extending GraphRAG to be time-aware (T-GRAG) and multimodally standardized (e.g. connection of images, text, video).

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

From shiny objects to important infrastructure

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

The original warnings were correct. Purely vector-based hopes often fail due to the depths of precision, relational complexity and entrepreneurial constraints. But the technology was never wasted: it forced the industry to rethink research, combining semantic, lexical and relational strategies.

If I were to write a sequel in 2027, I suspect it would portray vector databases not as unicorns, but as legacy infrastructure – basic but dwarfed by smarter orchestration layers, adaptive retrieval controllers, and AI systems that select dynamically which The retrieval tool matches the query.

The real battle right now isn’t vector vs. keyword – it’s about the indirection, blending and discipline in building retrieval pipelines that reliably anchor genetic AI in facts and domain knowledge. This is the unicorn we should be chasing now.

Amit Verma is the head of engineering and AI labs at Neuron7.

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