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You need retrieval for a retrieval-augmented generation (RAG) chatbot, but you do not automatically need to run a separate, dedicated vector database. For a first prototype, a managed retrieval API or PostgreSQL with pgvector may be enough. Choose by testing your own documents and questions, and by weighing data handling, integration, operating effort, and cost—not by assuming one architecture fits every project.
What “no vector database” actually means
RAG combines retrieved source material with a model’s response. Retrieval is essential to the pattern, but it can be implemented in different ways. A managed service can handle much of the retrieval workflow, or a database already used by your application can store and search embeddings. In the latter case, you are still using vector search; you are simply not adding a separate, dedicated vector-database service.
Semantic search can find content related to a query even when the wording differs. OpenAI describes its Retrieval API as surfacing semantically similar results “even when they match few or no keywords.” Retrieval can also use keyword similarity or combine semantic and lexical signals through hybrid search. Whether those settings help your chatbot depends on your documents and queries, so test them rather than assuming hybrid search is always better. OpenAI’s Retrieval API documentation explains these options.
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| Starting path | What it does | What to evaluate |
|---|---|---|
| Managed retrieval API | A provider manages a vector-store workflow. OpenAI documents automatic chunking, embedding, indexing, semantic retrieval, and hybrid-search controls. | Provider data handling, API and storage costs, service limits, retrieval quality on your test questions, and portability. |
| PostgreSQL with pgvector | Stores embeddings and performs vector search in PostgreSQL, which may already hold your application data. | Whether your PostgreSQL setup supports the extension and required behavior; your team’s operational experience; indexing, query performance, backups, and restore procedures. |
| Dedicated vector database service | Adds a specialized service for vector retrieval. | Whether its retrieval, filtering, hybrid-search, integration, reliability, operating model, and total cost address a demonstrated need. |
The table describes options to investigate, not measured winners. The cited materials do not establish a universal document count, request rate, latency, or other threshold at which a dedicated service becomes necessary.
#1 Best Overall
Managed retrieval for a quick prototype
A managed retrieval API can reduce the infrastructure you operate directly. In OpenAI’s documented workflow, files added to a vector store are automatically chunked, embedded, and indexed. This trades some direct control for reliance on a provider’s service, limits, data-handling terms, and pricing. Check those details against your requirements before uploading documents.
OpenAI’s Retrieval documentation, checked on October 7, 2026, lists up to 1 GB of vector-store storage across stores as free and storage beyond 1 GB at $0.10 per GB per day. This is OpenAI’s provider price, not a general market rate; verify current pricing and terms before relying on it.
Rank #2
PostgreSQL with pgvector when it fits your stack
If your application already depends on PostgreSQL, pgvector can keep application data and vector retrieval in a database your team already operates. AWS documents Aurora PostgreSQL with pgvector as one RAG architecture, and Google documents a PostgreSQL-based approach using AlloyDB. DigitalOcean’s vendor guidance also discusses pgvector in the context of using PostgreSQL as an application’s source of truth.
These examples show that PostgreSQL-based retrieval is a viable architecture, not that it will always be simpler, cheaper, faster, or more accurate for your workload. Confirm extension and version support in your actual environment, and account for indexing, query behavior, backups, and operational ownership.
When to evaluate a dedicated service
A dedicated vector database is an option to assess when testing or operational experience reveals a need that your current approach does not meet. Compare it using your real retrieval requirements—such as relevance, filtering, hybrid search, latency, integration, reliability, and total cost. The available sources do not provide a neutral head-to-head benchmark or a universal point at which switching is warranted.
Choose by testing your own chatbot’s retrieval
Start with the least operationally complicated approach that is compatible with your stack, documents, and data constraints. Then evaluate whether it retrieves evidence that supports the answers your chatbot gives.
Rank #4
- Build a representative question set. Include questions that use different wording from the documents, questions containing exact names or identifiers, and questions whose answers are absent from the source material.
- Inspect retrieved passages. For each question, check whether the passages are relevant and actually support an answer. A fluent model response is not evidence that retrieval worked.
- Check the absent-answer case. Confirm what the system does when its documents do not contain the answer; it should not present unsupported material as if retrieval supplied it.
- Compare the costs and responsibilities. Consider provider charges and data-handling requirements for a managed API, or database operations and integration work for PostgreSQL. Include the operational burden of any new service.
- Reassess only when you have a concrete reason. If the current path fails your relevance, performance, integration, or governance requirements, evaluate alternatives against the same test set and workload.
This evaluation is a practical way to make an architecture decision; it is not a benchmark showing that one option wins. Keep the questions and documents representative of the chatbot’s intended use, and repeat the checks when the corpus or retrieval configuration changes.
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AWS documentation describes an Aurora PostgreSQL knowledge-base workflow in which source text from S3 is chunked, converted to vectors, stored in PostgreSQL, and retrieved to augment model responses. Google’s reference architecture likewise demonstrates PostgreSQL-based vector retrieval. These examples support the point that RAG does not require a separately operated vector-database product; they do not establish that either cloud architecture is the right deployment for a small prototype.
AWS’s high-speed chatbot guidance also warns that AWS Cloud9 is no longer available to new customers. Treat its architecture as an example rather than copying setup instructions that depend on that development environment.
Decision in one sentence
For a first RAG chatbot, begin with managed retrieval if its data handling, cost, and limits fit, or with pgvector if PostgreSQL already suits your application; add a dedicated vector-database service only when your tests and requirements justify operating one.
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