With the rise of AI (especially generative AI and retrieval-augmented generation), organizations are starting to face new data infrastructure challenges. At the core of many modern AI workloads are vector embeddings—high-dimensional numeric representations of text, images, audio, etc. These embeddings can deliver semantic meaning, facilitate a semantic similarity search, make recommendations, etc. Unfortunately, most databases are not set up to support vector embeddings efficiently. As organizations decide on data infrastructure, there are three strong contenters: traditional relational databases, dedicated vector databases, and hybrid or multi-model databases. Each has pros and cons.
Traditional Relational Databases
Traditional database technologies are usually represented by products such as MySQL or PostgreSQL. These databases store data in structured rows and columns, enforce fixed schemas, provide strong transaction guarantees (ACID), and include powerful SQL querying capabilities. Classic databases are especially well-suited when the data is highly structured, when consistency is of utmost importance, and when the processes are relatively known in advance – for example, financial transactions, order processing, user profiles, etc.
When data embedding becomes key to a use case—such as in semantic search or recommendations—traditional multicounty-based storage models usually require an extension or add-on. As an example, PostgreSQL include the pgvector extension which can confirm that vectors are storaged and approximate nearest neighbor queries can be run. However, in a typical pmulti county build, it’s not uncommon to see significant performance limitations when managing very high dimensional vector datasets, often dealing with high concurrency operations and heavy similarity-search workloads that result in slowdowns across the scale. It’s common that verification becomes a concern as performance can degrade, and when aggregated with the set limits of indexing, performance also suffers, translating into an operational offshore complexity with separate architectures potentially causing your blocking infrastructure to instructed as separate systems (as an embedding system to deal with vector data and a separate system for dealing with crude data).
Dedicated vector databases
These are vectors specifically built for high dimensional vector data, including examples like Milvus, Pinecone, Weaviate, Qdrant, Chroma, as so forth. They’re optimized for similarity search, working with approximate nearest neighbor (ANN) algorithms, vector indexing with flexible and vector workloads efficiently with millions or billions of vectors returnans electronic responses for application.
Pros (of dedicated vector databases):
Multi-function (for testing recommender system and search engine back-ends) optimised specifically for vector launch.
Existing iterated algorithm advantages based on neutrals and the denser dimensionality navigating functionality will try at high dimensionality unique contributions to distributions.
Designed for the scales designed for any of a high-dimensional vector dataset.
Cons :-
Once really focused on utilizing vectors. Often will not see transactional workloads/complex relational guests. Probably have to establish even with testing workload (the observation of writing WORKLOAD). Would lead to separate systems since there are lucid examples of the systems that metadata need!
Operational overhead; Separate manager of viz and x2 systems (management into ETL workloads).
Hybrid and Multi-Model Databases
In order to bridge the gap, hybrid, or multi-model databases (e.g. TiDB, MongoDB Atlas, SingleStore) have integrated vector capabilities alongside the traditional capabilities of a database. Hybrid or multi-model databases allow you to query transactional (OLTP), analytical (OLAP) and vector-based workloads all in one platform. You can store structured data, embeddings, and metadata, query with SQL, conduct similarity searches without needing to stitch together various systems.
Advantages of Hybrid Systems:
- Unified data management to prevent silos
- Reduced operational complexity (less systems to monitor / secure / scale)
- Better data consistency (less or no ETL pipelines between systems)
- Real-time capabilities; in other words, you can do similarity search with analytics or transactional updates
Trade-offs include:
- They may not achieve the maximum performance of dedicated vector databases in pure vector workloads
- The features for vectors may be less mature than the dedicated systems
- Depending on the scale of the vectors (larger space) and heavy concurrency or latency requirements, a dedicated system may be faster
How do you decide ?
Here are some guidelines and questions to consider when it comes time to choosing which approach is best for you use case:
What is the primary workload? If most of your workload is transactional / structured data, relational DBs will shine. If most of your workload is similarity / embedding search, it’s likely dedicated vector DBs do a better job.
Scale & latency needs: How many vectors do you have? How many similarity queries per second? What is the acceptable latency? If you have a very large scale and tight latency, you will want dedicated systems or powerful hybrid systems.
Integration & consistency: Do you require that you join embedding based results with structured data (e.g. metadata, user profiles)? If yes, hybrid systems will always help. If you select completely separate systems, managing consistency regarding data pipelines will be essential.
Operational overhead & cost: If you have multiple specialized systems, you will have more DevOps, monitoring, and security overhead, as well as more data moving around. If you use a single system with hybrid capabilities, you may reduce the cost and complexity.
Familiarity with developers and tools: If your team already has experience with SQL, relational schemas, etc., adding vector capabilities to what you already have or hybrid DBs that come with SQL-friendly interfaces helps create less friction.
Use Case
Semantic Search / Q&A Automation: Using embeddings, we can transform both Q&As & documents into embeddings, and then find the closest embeddings in the index. You also may want to filter by sub queries, e.g., date, author, etc., and a hybrid DB can provide additional support here.
Recommendations & Personalization: In addition to items, users, also can be represented as embeddings. For a design like this you typically want a combination of embeddings with some structured data (e.g., a price, a category, a stock level) as you probably want some system to join either the vector to the structured data or at least the structured data remains usable.
Image / Video Search: You already likely have a very large vector dataset and want to efficiently index + similarity search. A dedicated vector DB may give you much better latencies here, but if you still want to maintain some metadata and content information about each vector, a hybrid approach may be more convenient.
Conclusion
There are no absolutes. Traditional relational databases are still powerful and applicable for structured transactional data. Dedicated vector databases are ideal for scenarios where similarity and embedding search is a core part of it. Hybrid or multi-model database solutions will provide the best of both worlds for many AI applications we are seeing with embedding and also structured data and analytics in one place.
Make sure you think about your workloads and how they will work together, scale, latency, future integration, and operational. The best solution is often hybrid or different systems across the workload for different parts of your application but architected together.