There were some changes to the enterprise RAG in Q1 2026. VB Pulse data from January to March tells a consistent story: The market stopped adding recovery layers and started fixing the ones already in place. Call it recovery reconstruction.
The survey involved three consecutive monthly waves from organizations with 100 or more employees, with between 45 and 58 qualified respondents per month across platform adoption, buyer intent, architectural approach, and evaluation criteria. Data should be considered directional.
Enterprises intend to see hybrid recovery triple from 10.3% to 33.3% in a single quarter – even as 22% of qualified enterprise respondents reported they had no production RAG systems. For data engineers and enterprise architects building agentic AI infrastructure, the data reveals a market in active transition: Most enterprises building RAG architectures are not what they expect to be running by the end of the year.
Hybrid recovery has become the consensus enterprise strategy. Unlike single-method RAG pipelines, which rely only on vector similarity, hybrid retrieval combines dense embeddings with sparse keyword search and re-ranking layers, trading simplicity for the retrieval accuracy and access control that production agent workloads require.
The standalone vector database category is under pressure. Vivinet, Milvus, Pinecone and Quadrant each lost adoption share during the quarter, according to VB Pulse data. Custom stacks and provider-native recoveries are absorbing their displaced share.
A growing minority of enterprises are moving away from RAG altogether – a sign that there are meaningful exceptions to the market maturity narrative.
Organizations working extensively on RAG in 2025 are reaching the same failure point: The architecture built for document retrieval does not scale agentively.
The enterprises that rapidly expanded RAG are now paying to rebuild it
The two biggest intent movements in Q1 are directly linked – enterprises are facing recovery quality problems at scale, and hybrid recovery is emerging as the consensus answer.
Investment priorities shifted in parallel. The budget intention peaked at 32.8% in January and fell to 15.6% by March due to evaluation and relevance testing. Recovery optimization went in the opposite direction, up from 19.0% to 28.9% – overtaking valuations as the top growth investment sector for the first time.
Steven Dickens, vice president and practice lead at Hyperframe Research, described the operational burden on enterprise data teams in a VentureBeat interview in March on Oracle’s Agentic AI data stack. "Data teams are tired of fragmentation fatigue," Dickens said. "Managing a separate vector store, graph database, and relational systems just to power a single agent is a DevOps nightmare."
That fatigue shows up directly in the platform data. The 35.6% increase in custom stacks is not a rejection of managed recovery – many organizations run both. It is a consolidation response from engineering teams that have reached the limit of assembling too many components.
Not every enterprise has been able to reach this far. The VB Pulse data includes one signal that complicates the market’s overall growth story: 22.2% of eligible respondents reported no RAG production as of March, up from 8.6% in January. The report credits this group of organizations that have "Not yet committed to any recovery infrastructure, or have stopped programs" – Concentrated in healthcare, education and government, the same sectors showing the highest rates of flat budgets.
Standalone vector databases are losing the adoption argument but winning reliability
Recent reporting from VentureBeat explains why a dedicated recovery layer still matters in production.
Two enterprises building on Quadrant show why purpose-built Vector infrastructure still wins in production.
&AI builds patent litigation infrastructure and runs semantic search across millions of documents. Including each result in the actual source document is not optional – patent attorneys will not work on AI-generated text. That need explains the architectural selection.
"The agent interface is," &AI founder and CTO Herbie Turner told VentureBeat in March. "The vector database is the ground truth."
GlassDollar, a startup that helps Siemens and Mahle evaluate startups, runs an agentic retrieval pattern on a corpus of close to 10 million indexed documents. A single user drives multiple parallel queries, each retrieving candidates from a different angle before combining and re-ranking the results. That query volume and precision requirement is what drives the choice of purpose-built vector infrastructure.
"We measure success by memory," Kamen Kanev, head of product at GlassDollar, told VentureBeat in March. "If the results don’t include the best companies, nothing else matters. The user loses trust."
VB Pulse data shows that framing – retrieval as ground truth rather than feature – is gaining traction in the broader enterprise market, even as standalone vector database adoption is declining.
Why enterprises say they need a dedicated vector layer shifted significantly in Q1. The top reasons in January were access control complexity (20.7%) and retrieval precision (19.0%). By March, operational reliability at scale had increased to 31.1% – more than doubling and surpassing everything else. Enterprises are no longer maintaining vector infrastructure primarily for precision. They’re keeping it because it’s the part of the stack they can rely on when query volumes scale.
How enterprises are redefining what a good recovery means
How enterprises assess their recovery systems has changed notably in Q1 – and the direction of that change points to the market becoming more sophisticated about what a good recovery actually means.
In January, response accuracy dominated the evaluation criteria at 67.2% – much higher than anything else. By March, response accuracy (53.3%), retrieval accuracy (53.3%) and answer relevance (53.3%) had become exactly the same. Getting the correct answer is not enough if the answer comes from the wrong document or misses the context of the question.
Answer: Relevance was the only parameter that increased over the quarter, gaining five percentage points. It is also the hardest to measure – whether the retrieved context is actually the correct context for that specific query requires purpose-built evaluation infrastructure, not just pass-or-fail correctness checks. Its growth indicates that a meaningful portion of enterprise buyers have moved beyond basic RAG testing altogether.
The market’s verdict: RAG is not dead. the original architecture is
"RAG is dead" The narrative had real momentum in 2026. It was based on two claims. First: Long-context windows – models capable of processing hundreds of thousands of tokens in a single prompt – will make dedicated recovery unnecessary. Second: an agentic memory system that stores what an agent learns across sessions, rather than retrieving it anew each time, would completely absorb the problem of knowledge access.
VB Pulse Data is the answer to the first claim of the enterprise market. Long-term-as-dominant-architecture status fell from 15.5% in January to 3.5% in February and partially recovered to 6.7% in March. The January sample was weighted heavily on technology and software respondents – this segment was most exposed to long-term model announcements in late 2025. As the sample diversified, the condition disappeared.
On the memory question, Databricks chief AI scientist Jonathan Frankel laid out the architecture clearly in a March interview with VentureBeat: A vector database with millions of entries sits at the base of the agentic memory stack, which is too big to fit in the context. The LLM Reference window is located at the top. Between them, new caching and compression layers are emerging – but none of them replace the retrieval layer at the base. New agentic memory systems like Hindsight developed by Vectorize, and observational memory approaches like the Maestra framework address session persistence and agent context over time – a different problem than high-recall searching across millions of changing enterprise documents.
The most consequential signal: the share of respondents who did not expect large-scale RAG deployment by the end of the year increased from 3.4% to 15.6% – almost 5 times. This is not a judgment against recovery. This is a decision against the recovery architecture that most enterprises had previously made.
Recovery rebuild is not optional
Recovery reconstruction is the cost of scaling a RAG without first deciding which architecture can actually support it.
If your organization is one of the 43.1% that has entered into a plan to expand RAG into more workflows, VB Pulse data suggests that the plan has already changed for many of your peers – and may need to change for you. Hybrid recovery is the consensus destination. The growth of custom stacks by 35.6% reflects teams building recovery infrastructure around needs that off-the-shelf products do not fully address.
RAG is not dead. The architecture most enterprises use to implement it. The data shows that reconstruction is not a future decision. For 33% of enterprises, reconstruction is already a declared priority.
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