Translated from the Korean original. Some screenshots show the original Korean interface.
More and more companies are adding AI chatbots, recommendations, and search to their services. Most of these features run on a vector DB, which converts text or images into numeric vectors (embeddings), stores them, and quickly finds data with similar meaning. Adopting one is not hard, but operating it raises many questions.
For example, you may find that writes stop even though there is plenty of disk space, or that load keeps piling onto one side even after you add nodes. These problems were relatively unfamiliar in traditional RDB operations. They come from the distributed architecture of vector DBs and a search structure that depends heavily on memory. That is why, when you run a vector DB, you need to regularly check memory limits and per-node load rather than free disk space.
In this post, we'll walk through why AI services need a vector DB, how operations change, and how to monitor them.
1. Why AI services need a vector DB
To understand why a vector DB is needed, start with how AI generates a response. During training, past data is gathered and processed all at once, so it is fine if this takes a while. But once a trained model is deployed to a real service, things change. Every time a user asks a question, relevant data has to be found right away and passed to the model, and if that step slows down, the answer reaches the user that much later.
This is where vector search comes in. It converts text or images into numeric vectors, then calculates the distance between vectors to quickly find data with similar meaning. RAG (retrieval-augmented generation) chatbots, AI agents that need knowledge search, and real-time recommendation services are all built on vector search.
A vector DB is a database that stores embeddings and quickly finds data with similar meaning. Because AI has to pull the material it needs instantly while answering a user's question, search speed determines how fast users get a response.
As vector search has become a core technology for AI services, demand for vector DBs is growing fast. Market research firm MarketsandMarkets forecasts that the global vector DB market will grow from $2.6 billion in 2025 to $8.9 billion in 2030, a CAGR of 27.5%. Some products are dedicated to vector search alone, but a growing number of integrated products add vector capabilities to existing databases.
2. After adoption, how do operations change?
Not all vector DBs are the same. Some, like Pinecone and Milvus, are dedicated products that handle only vector search. Others are integrated, where an existing database also handles vector search, such as SingleStore or PostgreSQL with pgvector. Either way, they depend heavily on memory for fast search and split data across multiple nodes to process it in parallel. Search gets faster, but some of the ways you ran disk-based DBs have to change. There are 3 main changes.
2-1. Memory fills up before disk
Because data and indexes depend heavily on memory, writes are blocked once memory hits its limit, even if plenty of disk space remains. This is a serious enough operational issue that SingleStore's official documentation covers it separately as the 'Maximum Table Memory' error. Deletes work the same way. Deleting rows does not free memory right away. Deleted data is reclaimed only when a background cleanup job (garbage collection) runs, and long-open transactions can delay that reclamation. So in a vector DB, you need to treat not only free disk space but also per-node memory usage as a key capacity metric.
2-2. Load piles onto one node
If you choose the wrong shard key (the distribution key), data and load concentrate on specific nodes, which is called data skew. When that happens, that one node drags down the performance of the whole cluster. Looking only at the cluster-wide average, it is hard to tell which node the load is concentrating on. You need to break the cluster down by node and look at the variance between nodes to spot hot spots in time.
2-3. Multiple workloads share the same resources
Because transactions, analytical queries, and vector search share the same memory and CPU, a single heavy analytical query can slow down real-time responses as well. Each metric on its own looks normal, which makes it tricky to connect symptoms to causes. Overall metrics are not enough. You need to check how much of each resource each workload uses to narrow down the cause.
These causes are not unique to any one product. Any database with an in-memory, distributed architecture can show similar patterns. So when evaluating adoption, choosing a product matters, but so does whether your operations team can catch these 3 issues in time. In a survey Gartner published in April 2026, only 28% of AI deployments were a 'complete success' that reached ROI. In other words, adopting a technology does not automatically bring operational results.
3. Monitoring shifts to the cluster and node level
When what you need to watch in operations changes, monitoring has to change too. If with traditional DBs you mainly watched queries and disks, with vector DBs you also need to watch nodes, memory, and how multiple workloads affect each other. The 3 points above become your checklist.
First, to see at a glance which node is carrying the load, the whole cluster has to fit on one screen. In-memory, distributed databases run with Aggregator nodes that distribute queries and Leaf nodes that store and compute data. In this architecture, you need to group the status of individual nodes at the cluster level to see where load is concentrating.
You also need to keep watching per-node metrics. Checking CPU, memory, and running SQL on each node lets you catch early signals such as resource bottlenecks, abnormal SQL, and session spikes. If you track how close each node is to its memory limit, you can prepare in advance for writes being blocked while disk space is still available.
You also need to look back at the moment an incident occurred. Lay out key metrics in time order for the moment the problem started, then narrow down to the top nodes, databases, and queries (Top N) where load concentrated to find the cause. When multiple workloads share the same resources, you can end up with every individual metric looking normal while overall response slows. In these cases, you need to narrow the time window and scope step by step to find the exact cause.
Here is a summary of how vector DB operations differ from traditional operations.
Category | Traditional RDBMS operations | Vector DB (in-memory, distributed) operations |
Capacity metric | Free disk space | Memory (writes rejected at the limit, reclamation delayed after deletes) |
Unit of observation | A single instance | Whole cluster + per-node variance |
Where bottlenecks occur | Slow queries, missing indexes | Data skew across nodes, multiple workloads crowding one resource |
Screens you need | Query response time, disk I/O | Integrated cluster monitoring, real-time per-node metrics, incident-time analysis |
If you can see your existing databases and vector DBs together in one monitoring environment, the burden on the operations team goes down. If every new database means learning a separate monitoring tool and way of operating from scratch, that burden falls entirely on the operators.
4. At SingleStore AI NOW in SEOUL 2026
These operational challenges are not unique to any one company. EXEM took part as a sponsor in 'SingleStore AI NOW in SEOUL 2026', held at El Tower in Yangjae, Seoul on July 7. The operators we met there talked about the same concerns covered above: memory limits, load concentrating on specific nodes, and multiple workloads crowding the same resources. These are the practical challenges teams face when adding a vector DB to AI services.
Under the theme 'Real-Time Enterprise AI', the event shared case studies and know-how from companies running real-time AI. Sessions included ultra-low-latency fraud detection (FDS) and large-scale RAG running on hybrid search that combines vector search and keyword search. What they had in common was the need to finish both search and decision-making within the brief moment a user waits. The integrated database powering this kind of real-time processing is SingleStore, described earlier.
We gave a live demo of monitoring SingleStore and several other databases on a single screen with EXEM's integrated monitoring solution, exemONE. Many visitors were especially interested in how a SingleStore cluster can be configured as one group, so the entire cluster, including Aggregator and Leaf nodes, can be viewed on a single screen.
Database performance is decided not at adoption but during operations. No matter how good the benchmark numbers are, they mean nothing if that performance does not hold up in production. That is why you need to look at the state of memory and load on each node, and whether multiple workloads sharing the same resources are affecting each other.
exemONE, which manages major databases including SingleStore from a single dashboard, helps you see and respond to these operational challenges on one screen. If you are considering a vector DB or already running one, take a moment to review your monitoring setup as well.
Sources
Vector Database Market - Global Forecast to 2030 - MarketsandMarkets (2025) Gartner Says AI Projects in Infrastructure and Operations Stall Ahead of Meaningful ROI Returns - Gartner (2026.04) Addressing the Maximum Table Memory Error - SingleStore Docs Detecting and Resolving Data Skew - SingleStore Docs
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