Vector database setup
We set up a vector database (Pinecone, Weaviate, Qdrant, pgvector etc.) — the foundation for semantic search, RAG and AI assistants: it stores 'meanings' (embeddings) and quickly finds the similar. Honestly upfront: a vector DB is technical INFRASTRUCTURE, a component, not a ready solution and not 'magic' by itself; it gives value only together with embeddings (894) and an application (search 874 / RAG 893); it has hosting and maintenance costs, and the choice of a specific DB is trade-offs for your task.
Vector database setup — overview
Vector database setup is deploying and configuring a vector (embedding) store for your tasks: choosing a suitable solution (cloud like Pinecone or self-hosted like Qdrant/Weaviate/pgvector), schema, indexes, similarity metrics, integration with your application and data pipeline. A vector DB is what enables fast searching for 'semantically similar' in large volumes. Honestly about the role, this is key: a vector DB is an infrastructure COMPONENT, not a standalone product and not 'AI that can do something'. By itself it brings no value to the user — it is needed as a foundation for semantic search (874), RAG assistants (893), recommendations. We honestly say: it is a foundation, value appears together with embeddings (894) and the application on top. Honestly about choice and trade-offs: 'the best vector DB for everyone' does not exist — cloud ones are simpler to start but mean costs and vendor dependency; self-hosted are cheaper at scale but require maintenance; different DBs are strong in different things (scale, filters, price). We pick for your task and honestly show the trade-offs rather than impose a trendy name. Honestly about costs and maintenance: storage and queries cost money (hosting/cloud), and the base requires support (updates, backups, scaling). 'Set and forget' is not quite so. Honestly about data: search quality over the base is determined by the quality of embeddings and data, not the DB itself. Honestly about the effect: it gives fast and scalable semantic search as a foundation, but business value is created by the application on top. Honestly about access: data and infrastructure/access are needed. An important boundary: this is a vector DB; embeddings — 894; semantic search — 874; RAG — 893. Picture this: instead of 'nowhere to store and search meanings' — a configured foundation for AI search and assistants. The base price starts from 40,000 ₽ (depends on the solution and volume).
Problems we solve
- You need semantic search/RAG but nowhere to store vectors.
- It is unclear which vector DB to choose and why.
- An ordinary DB cannot handle meaning search at scale.
- Fear of overpaying for cloud or not coping with self-hosted.
What's included in the Vector database setup service
- Choosing a vector DB for the task (cloud/self-hosted)
- Deployment, schema, indexes, similarity metrics
- Integration with the application and data pipeline
- An honest comparison of trade-offs (price/scale/vendor)
- Estimating hosting and maintenance costs
- Honest boundaries (it is a component, not a ready solution)
- A link with embeddings (894) and search/RAG (874/893)
- Handover and review with you
What you get
- A configured vector DB as a foundation for AI features
- Fast scalable semantic search (foundation)
- A deliberate DB choice for your task
- An honest understanding of costs and that it is a component
How the work goes: steps
- We define the task, volume, budget; choose the solution
- We deploy, set up indexes, integrate
- We honestly show trade-offs and costs, hand over with you
Why PDV Expert
- Fixed price and timeline — no surprises on the invoice.
- Report and recommendations in plain language — clear without a technical background.
- In touch at every step and answering questions about the result.
FAQ
Does a vector database give anything by itself?
No, honestly: it is an infrastructure component, not a ready solution. By itself it brings no value to the user — it is a foundation for semantic search (874), RAG assistants (893), recommendations. Value appears together with embeddings (894) and the application on top. We honestly build exactly the foundation rather than pass off the base as 'smart AI'.
Which vector DB is the best?
'The best for everyone' does not exist, and that is honest. Cloud ones (Pinecone etc.) are simpler to start but mean costs and vendor dependency; self-hosted (Qdrant/Weaviate/pgvector) are cheaper at scale but require maintenance. Different DBs are strong in different things. We pick for your task, budget and scale and honestly show the trade-offs rather than impose a trendy name.
Is it a one-off setup with no costs afterward?
Not quite, honestly: storage and queries cost money (hosting/cloud), and the base requires support — updates, backups, scaling as data grows. We honestly factor in costs and maintenance rather than promise 'set up and free forever'. But you get a transparent picture of the cost of ownership.
About the provider
The «Vector database setup» service is provided by PDV Expert — a team specialising in «Site quality». We work under contract and deliver a written report with recommendations.