# Evaluating internal data storage for RAG architectures [Skip to content](#lm-inhoud)Network/NL[EN](/en/)[Hubhub.llmnet.nlCompare models by task, language, cost and license.](https://hub.llmnet.nl/en/)[Communitycommunity.llmnet.nlPrompt techniques, patterns and system prompts.](https://community.llmnet.nl/en/)[APIapi.llmnet.nlLLMs robust in software: rate limits, routing, structured output.](https://api.llmnet.nl/en/)[Consultancyconsultancy.llmnet.nlIntroducing AI in an organization, from pilot to production.](https://consultancy.llmnet.nl/en/)[Newsnieuws.llmnet.nlDevelopments in AI, interpreted for the Netherlands.](https://nieuws.llmnet.nl/en/)[Benchmarkbenchmark.llmnet.nlMeasure AI quality yourself, for your own tasks.](https://benchmark.llmnet.nl/en/)[Jobsvacatures.llmnet.nlAI roles, salaries and career paths in the Netherlands.](https://vacatures.llmnet.nl/en/)[Learnleren.llmnet.nlAI concepts in plain language, from beginner to builder.](https://leren.llmnet.nl/en/)[Guidegids.llmnet.nlRun AI privately on your own Mac, PC, NAS or home server.](https://gids.llmnet.nl/en/)[Directorydirectory.llmnet.nlMapping the AI ecosystem: tools, models, companies.](https://directory.llmnet.nl/en/)[Radarradar.llmnet.nlSignals from X, research and communities for indie developers.](https://radar.llmnet.nl/en/)[Appsapps.llmnet.nlReviews of AI apps and open-source repos, with tips for people who build their own.](https://apps.llmnet.nl/en/)[llmnet.nl — main site](https://llmnet.nl/)[](https://x.com/intent/post?url=https%3A%2F%2Fconsultancy.llmnet.nl%2Fvectordatabase-selectie-enterprise&text=Evaluatie%20van%20interne%20gegevensopslag%20voor%20RAG-architecturen)[](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fconsultancy.llmnet.nl%2Fvectordatabase-selectie-enterprise)[](https://www.reddit.com/submit?url=https%3A%2F%2Fconsultancy.llmnet.nl%2Fvectordatabase-selectie-enterprise&title=Evaluatie%20van%20interne%20gegevensopslag%20voor%20RAG-architecturen)[](#)[](https://x.com/intent/post?url=https%3A%2F%2Fconsultancy.llmnet.nl%2Fvectordatabase-selectie-enterprise&text=Evaluatie%20van%20interne%20gegevensopslag%20voor%20RAG-architecturen)[](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fconsultancy.llmnet.nl%2Fvectordatabase-selectie-enterprise)[](https://www.reddit.com/submit?url=https%3A%2F%2Fconsultancy.llmnet.nl%2Fvectordatabase-selectie-enterprise&title=Evaluatie%20van%20interne%20gegevensopslag%20voor%20RAG-architecturen)[](#) # Evaluating internal data storage for RAG architectures By Ivo Donker — compiled with AI support · Last updated: August 7, 2026 ## Architecture Choice for Vector Storage within RAG Systems When implementing Retrieval-Augmented Generation (RAG) in enterprise environments, selecting the vector storage layer is a fundamental decision point. Organizations face the choice of adding a specialized, dedicated vector database to the stack, or using a hybrid extension on their existing relational database. Both approaches have distinct technical characteristics, operational implications, and financial consequences. A poorly considered choice can lead to excessive infrastructure costs, complex data synchronization, or unreliable latency at scale. The purpose of this article is to offer an objective, technical evaluation framework for software architects, lead engineers, and CTOs in the SME and scale-up segment. Without favoring specific vendors or brands, the two main types of storage infrastructure are broken down along seven critical dimensions: data volume, query latency, filter combinations, management overhead, data consistency, cost structure, and information security. For the fundamental working principles and basic concepts of vector embeddings and RAG pipelines, consult [the overview of RAG fundamentals on leren.llmnet.nl](https://leren.llmnet.nl/en/rag-voor-beginners). ## Evaluation Criteria for Storage Architectures The suitability of a database architecture is closely tied to the specific preconditions of the RAG application. Below, the seven core criteria are analyzed in detail to enable a structured trade-off. ### 1. Data volume and scalability The volume of vectors is determined by three factors: the number of source documents, the configured segment size (chunk size), and the dimensionality of the chosen embedding vector. An increase in the number of dimensions not only raises storage requirements per vector linearly, but also directly impacts memory usage during indexing. Dedicated vector databases are primarily designed for horizontal scalability (sharding) and efficient processing of billions of vectors across distributed clusters. At scale, they rely on in-memory index structures combined with advanced quantization techniques to shrink the memory footprint. Hybrid relational extensions, by contrast, process vectors within the existing page-based memory architecture of the relational database engine. For datasets up to a few hundred thousand to a couple million vectors, hybrid solutions perform excellently. However, once the dataset exceeds tens of millions of high-dimensional vectors, the index of a hybrid extension can exceed the database server's available RAM, resulting in significant performance pitfalls. ### 2. Latency and search performance In RAG systems, search speed for k-nearest neighbor (k-NN) or approximate nearest neighbor (ANN) queries determines the total processing time. Search algorithms such as Hierarchical Navigable Small World (HNSW) and Inverted File Index (IVF) require specific trade-offs between accuracy (recall) and throughput. Specialized vector engines minimize search latencies by keeping all index structures continuously in working memory (RAM) and handling searches via optimized vector instruction sets (such as AVX-512 or SIMD). This puts sub-second search latencies (< 10 to 50 milliseconds) within reach even for very large datasets. Hybrid extensions must share the vector indexes with traditional B-tree indexes and transactional table data within the database's buffer pool. This can lead to higher I/O wait times when the vector index doesn't fully fit in RAM. Furthermore, building an HNSW index on a hybrid database requires considerable compute power, which can affect the database's overall performance during peak hours. Processing high-quality source data is essential for optimal search results; you can read more about preprocessing and structuring source data in [the guide on data quality for AI applications on consultancy.llmnet.nl](https://consultancy.llmnet.nl/en/datakwaliteit-voor-ai). ### 3. Filter combinations and hybrid query handling A common requirement in enterprise RAG is combining vector similarity (semantic search) with strict qualifying criteria (metadata filtering), such as tenant ID, publication date, authorization levels, or specific categories. There are three main strategies for combined search: - Post-filtering: First, a top-N semantic search is performed on the vector index, after which the results are filtered by metadata. If the filter condition is very restrictive, there's a risk that too few or no valid results remain. - Pre-filtering: The database first selects all entities that meet the metadata criteria, after which an exact or approximate vector search is performed on the filtered subset. With large datasets, this can lead to slow processing if the subset cannot be indexed efficiently. - Single-stage / in-index filtering: The filter criteria are applied directly during traversal of the vector index (for example, the HNSW graph). Solutions that support this offer the highest accuracy and predictable latency. Hybrid relational databases excel at hybrid queries where SQL expressions seamlessly combine relational joins, B-tree filter criteria, and vector distance metrics. The query optimizer can potentially choose the most efficient execution route. Dedicated vector databases, in turn, offer highly optimized in-index filtering for JSON or key-value metadata, but generally don't support complex relational joins between separate tables. ### 4. Management overhead and operational complexity Adding a dedicated vector database introduces a new component into the IT infrastructure. This requires specific expertise in monitoring, provisioning, backup/restore, monitoring index degradation, and security updates. It also creates a synchronization challenge: changes to the source data (such as deletions or updates in the primary system) must be propagated to the dedicated vector database via Change Data Capture (CDC) or ETL pipelines. When extending an existing relational database with a vector extension, the infrastructure footprint stays the same. Administrators use familiar procedures for database structure, storage management, high availability (HA), and disaster recovery. This significantly reduces the operational load on DevOps and SysAdmin teams. For a deeper analysis of hardware requirements and management boundaries, see [the article on on-premise LLM infrastructure requirements on consultancy.llmnet.nl](https://consultancy.llmnet.nl/en/on-premise-llm-infrastructuur-eisen). ### 5. Data consistency and transactional guarantees Enterprise applications often impose hard requirements on ACID properties (Atomicity, Consistency, Isolation, Durability). When a document is retracted or updated, that change must be reflected immediately in the RAG system's search results to prevent leaking outdated or confidential information. Hybrid relational databases offer full transactional guarantees. Adding a document and its corresponding embedding happens within the same atomic transaction; if the transaction is rolled back, the vector indexing disappears with it. Dedicated vector databases, on the other hand, often use an eventual consistency model. Indexing a new vector or rebuilding index graphs takes time, which can cause a brief delay between storing the data and it becoming searchable. ### 6. Cost structure and resource utilization Total Cost of Ownership (TCO) consists of licensing or hosting costs, infrastructure costs (RAM, CPU, storage), and operational labor costs. A rule of thumb for cost models is that HNSW indexes should reside almost entirely in working memory for optimal performance. Since high-quality RAM is more expensive than NVMe storage, infrastructure costs rise quickly as datasets grow. Dedicated vector engines often offer more efficient memory compression (such as Scalar Quantization or Product Quantization), allowing more vectors to be stored per gigabyte of RAM. Hybrid relational databases, by contrast, can be more cost-efficient for small to medium datasets, since no additional server instances need to be rented or managed. Always verify the specific licensing and infrastructure costs for your own situation based on expected query volumes and dataset size. ### 7. Security, compliance, and data governance Data security encompasses row-level access control (Row-Level Security - RLS), encryption at rest and in transit, audit logging, and compliance with privacy legislation (such as GDPR). In a hybrid relational database, vector storage directly benefits from existing security mechanisms, such as mature RLS policies, existing IAM integrations, and consolidated audit trails. With a dedicated vector database, security and authorization models must be set up separately and synchronized with central user management. This raises the risk of authorization errors, where users gain access via the RAG interface to vector chunks from documents they don't have read permissions for in the primary system. ## Architectural Comparison: Dedicated versus Hybrid The choice between a dedicated vector database and a hybrid relational extension is rarely a matter of absolute superiority, but of weighing architectural trade-offs. The analysis below highlights the practical functional characteristics of both directions. ### The hybrid relational extension In a hybrid approach, the existing relational database serves as the central hub for both the transactional data and the vector embeddings. The vector data is stored as a specific data type within regular tables. Advantages: - Single source of truth: No need for ETL pipelines or complex data synchronization between different databases. - Strong consistency: Direct ACID guarantees on vector updates and deletions. - Simplified governance: RLS rules and authorization models apply universally to both metadata and vectors. - Low initial barrier: No new infrastructure components or additional management domains needed. Drawbacks: - Resource contention: Heavy vector searches or index building can exhaust CPU and RAM resources, at the expense of regular OLTP transactions. - More limited scalability: Scaling mainly requires vertical scaling (larger database servers) or complex database sharding. - Less specialized algorithms: Advanced techniques for index compression or specific distance metrics are sometimes less readily available or less refined than in dedicated engines. For a detailed overview of the general characteristics and market segmentation of different storage models, you can visit [the vector database comparison page on directory.llmnet.nl](https://directory.llmnet.nl/en/vector-databases-vergeleken). ### The dedicated vector database A dedicated vector engine is built from the ground up with one primary goal: processing high-dimensional vector searches as fast and efficiently as possible at scale. Advantages: - Superior search performance: Optimized for high throughput (QPS) and low latencies with very large datasets. - Independent scalability: Vector storage can be scaled horizontally, isolated from the primary database. - Advanced indexing methods: Support for the latest quantization and graph-based indexing techniques that greatly reduce memory usage. - Isolated workloads: Spikes in search queries have no impact on the primary OLTP system. Drawbacks: - Complexity in the data pipeline: Need to build and maintain robust synchronization mechanisms (such as CDC) to prevent data drift or stale indexes. - Dual authorization model: Access management must be correctly implemented and maintained in both the source database and the vector engine. - Higher operational TCO: Additional licensing, hosting, and management costs for an extra infrastructure layer. If you're considering setting up an isolated environment locally for testing or development purposes, you'll find step-by-step instructions in [the guide for setting up a local vector database on gids.llmnet.nl](https://gids.llmnet.nl/en/lokale-vector-database-opzetten). ## Practical Decision Logic and Quantitative Thresholds To determine which option best fits a specific case, organizations can use a number of rules of thumb based on data volume, query frequency, and team capacity: - Dataset size < 1 million vectors: As a rule, choose a hybrid extension on the existing relational database. The overhead of a dedicated system rarely outweighs the gain in milliseconds of latency. - Dataset size 1-10 million vectors: The choice depends on hardware capacity and query complexity. If there's sufficient RAM available on the database node and complex relational filters are heavily used, a hybrid solution remains attractive. When search latencies are critical (< 20ms) and query throughput is high, the advantage shifts to a dedicated engine. - Dataset size > 10 million vectors: Consider a dedicated vector database. At this scale, the costs of memory management, sharding, and index reconstruction in hybrid databases often become bottlenecks for overall system performance. - Strict security and Row-Level Security: When access to individual document chunks is highly dynamic and depends on complex organizational roles, the hybrid relational database offers superior reliability and management simplicity. Architecture note: Avoid premature optimization. Many RAG projects start with fewer than 100,000 document chunks. At that stage, avoiding an extra infrastructure layer delivers faster iteration cycles and lower management costs, without noticeable quality loss for the user. ## Decision Matrix for Vector Infrastructure The decision matrix below serves as a concrete tool for architects and engineering teams to make an informed choice based on their specific preconditions. Evaluation criterion | Characteristic of the case | Recommended storage architecture | Explanation & considerations | Data volume | < 1 million vectors (1536d / 3072d) | Hybrid Relational Extension | The in-memory index comfortably fits in the standard database buffer pool; minimal operational overhead. | Data volume | > 10 million vectors with high growth | Dedicated Vector Database | Offers better horizontal scalability, dedicated cluster management, and more efficient memory quantization. | Query Latency | SLA requires < 20 ms at high concurrency | Dedicated Vector Database | In-memory processing and SIMD-optimized search algorithms prevent I/O bottlenecks. | Query Latency | SLA tolerates 50-200 ms | Hybrid Relational Extension | More than sufficient for most B2B applications, provided the database hardware is properly sized. | Filtering & Joins | Frequent joins with OLTP tables and dynamic RLS | Hybrid Relational Extension | Prevents duplicated authorization management and gives the query optimizer the ability to pre-filter. | Filtering & Joins | Simple metadata filters (e.g., tenant_id, date) | Dedicated Vector Database | Integrated in-index filtering on JSON/key-value metadata delivers excellent performance. | Data Consistency | Strict ACID requirements and immediate processing of deletes | Hybrid Relational Extension | Transactional guarantees ensure that deleted data is immediately invisible to the vector index. | DevOps & Management | Limited team capacity, no dedicated DB specialist | Hybrid Relational Extension | No extra infrastructure layer, monitoring systems, or CDC pipelines to set up and maintain. | Cost structure | Limited budget, existing database infrastructure already in place | Hybrid Relational Extension | Makes use of server resources already paid for; avoids extra licensing or cloud hosting costs. | Cost structure | Large RAM memory requirement at scale | Dedicated Vector Database | Advanced index compression (e.g., Product Quantization) limits the increase in required working memory. |