No. Retrieval-augmented generation (RAG) needs a way to find relevant information and pass it to a language model; it does not always need a separate, dedicated vector database. PostgreSQL with pgvector and search platforms such as Elasticsearch can also support RAG retrieval. The right setup depends on the workload and the systems your team already operates.
What RAG needs from its retrieval layer
RAG grounds a model’s response in information retrieved from an external source. The application finds relevant material, adds it to the model’s context, and asks the model to generate an answer. Elastic describes retrieval using full-text, vector, or hybrid search, illustrating that the required capability is useful retrieval—not one particular database product category. See Elastic’s RAG documentation.
Vector search is one way to find relevant material: embeddings represent content numerically so a system can identify similar items. But RAG can also use lexical search, or combine lexical and vector methods. Whether embeddings are needed, and how they should be stored and searched, depends on the retrieval design.
RAG architecture options
| Pattern | What it can do | Questions to weigh |
|---|---|---|
| PostgreSQL with pgvector | Cloud SQL for PostgreSQL documents storing, indexing, and querying embeddings with pgvector, including without a separate vector database. EDB also describes pgvector as a PostgreSQL extension for storing, querying, and indexing vectors. | Would keeping embeddings alongside existing data, using SQL joins, and applying existing database operations suit the workload? Does the database meet measured retrieval and operational requirements? |
| Existing search platform | Elasticsearch documents RAG retrieval with full-text, vector, semantic, or hybrid search across deployment types. | Would lexical or hybrid search, filtering, access controls, aggregations, or existing indices help? Which deployment-specific guidance applies? |
| Dedicated managed vector search | Google describes Vector Search as managed infrastructure optimized for very large-scale vector-similarity matching. | Do measured scale or latency requirements justify a specialized serving layer? What are the operational, security, integration, and cost tradeoffs in your environment? |
| Managed RAG or custom workflow | AWS guidance considers both managed and custom RAG approaches. | How much workflow customization is needed? Which skills, company policies, existing systems, latency needs, or graph-query requirements affect the choice? |
For the database-extension pattern, Google Cloud’s Cloud SQL guidance on generative AI applications explicitly says embeddings can be stored in Cloud SQL without a separate vector database. EDB provides an independent description of pgvector as an open-source PostgreSQL extension commonly used for semantic search and RAG.
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For the search-platform pattern, Elasticsearch’s general RAG documentation describes multiple retrieval methods. Its guidance for RAG on Elastic Cloud Serverless specifically recommends an Elasticsearch Vector Database project. Treat that as guidance for that deployment, not as a requirement for every RAG system or every Elasticsearch deployment.
For dedicated managed search, Google’s RAG infrastructure reference architecture presents Vector Search for very-large-scale similarity matching and points to AlloyDB or Cloud SQL when a managed database with vector-store capabilities is preferred. This is a documented product option, not evidence that it is universally faster or better.
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How to choose without guessing at a threshold
- Define the retrieval job. Identify whether the application needs vector similarity, lexical search, hybrid retrieval, or a combination, along with relevant filters and access controls.
- Start with systems you already use. If PostgreSQL or a search platform is already part of the architecture, check whether its documented retrieval capabilities fit before adding another service.
- Measure the actual workload. Evaluate retrieval quality, latency, scale, operational effort, security, integration, and cost in your own environment. The cited materials do not establish a universal corpus-size, latency, or vector-count point at which a dedicated service becomes necessary.
- Account for people and policy. AWS’s RAG architecture guidance includes ease of implementation, organizational skills, company policies, workflow customization, existing vector databases, latency, graph queries, and existing PostgreSQL among selection considerations.
- Adopt specialized infrastructure when the evidence supports it. A dedicated managed vector service is a valid option, particularly when measured requirements and operational tradeoffs justify it; it is not a prerequisite imposed by RAG itself.
There is no comparative benchmark or universal crossover threshold established by these sources. Choose based on measured requirements rather than an assumed rule that every RAG application needs a separate vector database.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the evidence does—and does not—establish
The cited vendor documentation shows that RAG retrieval can be implemented with different product patterns: PostgreSQL using pgvector, an Elasticsearch search platform, or dedicated managed vector search. It does not establish that one is always cheaper, faster, or more accurate, nor does it provide a general threshold for switching between them.
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Product recommendations and availability can change. Google’s AlloyDB RAG reference architecture was last reviewed on 2026-02-04; the AWS architecture guide history lists an initial publication date of 2024-10-28. Elastic’s Serverless project recommendation is specific to that deployment. Check current product documentation and regional availability when choosing a service.
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