Qdrant

A classic search engine copes well with exact names, codes and phrases. It struggles more when users describe what they need in different words from those used in the catalogue or document. Qdrant stores vectors that represent the meaning of content and lets you search for items that are semantically similar.

You can use it in product search, a knowledge base, recommendations, spotting similar support tickets or a RAG system. RAG (retrieval-augmented generation) fetches relevant material before a language model writes its answer. Qdrant is responsible for the search layer, not for whether the whole answer is true.

We can build a proof of concept, integrate Qdrant with your existing application or tidy up a pipeline that is already running. We start with example questions and expected results, because the quality of the system is judged by the user’s task, not by how fast the database responds.

In e-commerce, semantic search can connect the query “rain shoes for the city” with products whose description doesn’t contain exactly that phrase. In a knowledge base, it helps find a procedure based on a description of the problem. In a support system, it can point to similar tickets and earlier solutions.

Not every field should be turned into a vector. An SKU code, a document number or a specific brand need an exact match. That is why the best results often come from combining semantic search with keyword search and business filters.

Before rollout, we agree which content can be indexed, who is allowed to see it and how quickly changes need to reach the search engine. This is especially important for internal data and different permission levels.

An embedding is a numerical description of content generated by a model. Similar passages get vectors that sit close to each other. The choice of model, the way documents are split and the text passed to the embedding affect the results as much as the database configuration does.

We usually split a long document into smaller chunks. Chunks that are too small lose context, while ones that are too large add noise and increase cost. It is worth adding a payload to each point, meaning metadata such as category, language, customer, date and source.

The index has to be updated whenever content changes. We design identifiers and the synchronisation process so that a document can be replaced, its old chunks deleted and the whole index rebuilt from the source system.

Qdrant lets you combine dense semantic vectors with sparse text representations. This means hybrid search can take into account both meaning and exact words. It is useful when a user sometimes types a description of what they need and at other times a specific product code.

Filters narrow down results based on metadata. We can show only products available in a given country, documents belonging to the user’s organisation or content valid for a chosen period. The filter must be part of the database query, not an extra check after the results have been fetched.

Ranking often needs several stages: a fast search for candidates followed by more precise reranking of a shorter list. We choose this architecture based on quality, latency and the cost of the models.

In a RAG system, the model receives passages found by the search engine and uses them to prepare an answer. If retrieval returns the wrong document or misses an important passage, the model has nothing good to work with. That is why we measure search quality and final answer quality separately.

We prepare a set of representative questions, expected sources and cases where the system should decline to answer. We check accuracy, completeness, citations and behaviour after the embedding model or prompt is changed.

The interface should show sources and clearly explain the system’s limitations. RAG can make knowledge easier to reach, but it does not guarantee there will be no mistakes. In high-risk processes, extra validation or human involvement is needed.

If one application serves many organisations, each customer’s data must stay separate. Qdrant lets you use payload filters, separate shards or a tiered multitenancy approach. The choice depends on the number of tenants (customer organisations), their size and the level of isolation required.

A separate collection for every small customer can waste resources. A shared collection, on the other hand, requires consistent filtering and permission testing. We design the tenant identifier as part of the data model and of every query.

On top of this come encrypted connections, API key management, network restrictions and control over the data sent to an external embedding model. Privacy has to be considered across the whole pipeline, not only in the database itself.

You can run Qdrant as a cloud service or on your own infrastructure. The cloud option reduces the work involved in maintaining a cluster. Self-hosting gives you more control over the environment and the flow of data, but it requires monitoring, backups, updates and emergency procedures.

Hybrid Cloud lets you manage a deployment that runs on your organisation’s own infrastructure. Qdrant also offers an Edge option for searching inside the application process and working without a permanent connection. These are different models for different requirements, not successive levels that every project has to go through.

At a larger scale, Qdrant can run as a cluster with shards and replicas. A distributed deployment increases capacity and resilience, but it raises cost and complexity. We start with a simpler environment if the data and traffic allow it.

The cost covers more than the vector database. You need to prepare the data, choose an embedding model, build synchronisation, design the search, measure quality and monitor production. With RAG, there is also the model that generates answers and the control of sources.

Ongoing costs depend on the number of documents, how often they are updated, the size of the vectors, replication, traffic and the models chosen. Quantisation can reduce memory use at the expense of some precision. Reranking improves results but adds latency and computing cost.

That is why we start with a small data set and a measurable goal. A proof of concept should answer whether users find the right material faster, not just show an impressive chat demo.

Qdrant combines vector search with filters and gives you a choice between the cloud and your own infrastructure. Pinecone focuses on a managed service. Weaviate and Milvus have their own ecosystems and deployment models, while pgvector can be a simple starting point if your data and team already work with PostgreSQL.

We compare filter quality, hybrid search, latency, scaling, data location, integrations and running costs. You don’t always need a separate vector database. For a smaller index, an extension to your existing database may be enough.

If the project’s requirements are uncertain, we will prepare a test of several options on the same data and questions. The result will be based on quality and cost for your specific case.