Vector Database for Business: How to Compare pgvector, Qdrant, Pinecone and Chroma

The starting point for choosing depends on what you already have. If the company is working with PostgreSQL, it is wise to check pgvector first. If you need a separate vector search service, consider Qdrant. If a team wants a managed service without their own server, Pinecone. If you need to get started quickly on-premise, with the option to move to the server or cloud later, Chroma.

These are starting options, not a verdict. The final choice is determined by the filters you need in your search, data isolation requirements, who will maintain the system, and what workload is expected. We haven't done our own speed benchmarks, so we don't name the fastest base, or provide latency or price figures.

After the article, you will be able to write down the requirements of your company, narrow down the choice to one or two candidates, honestly test them and plan in advance how to switch to another solution if necessary. The capabilities of the bases are given according to their documentation as of 11.10.2026. We did not investigate tariffs and regional availability.

First the requirements

The vector base stores numerical representations of text fragments (embeddings) and quickly finds the ones closest to the query. This function is present in all four candidates. The difference is that around her.

Case size. How many fragments will be in the database now and in a year. Memory, index and scaling requirements depend on this. Not only the current volume is important, but also the rate of growth.

Filters. Will you search only by similarity or also by conditions: documents of a specific department, only current versions, only documents available to this employee. For a corporate knowledge base, filters are almost always required, and it is with them that the databases behave differently.

Isolation of tenants. If the same database stores the data of several customers or departments, how to ensure that a request from one does not see the data of another. This is a question of both security and architecture.

Updates. How often documents change, how quickly changes should be reflected in search, how outdated versions are removed.

Backup and restore. How a backup is made, how to restore from it and how long it will take. It is necessary to check the restoration itself, and not only the presence of copies.

Closed loop. Can data go outside your infrastructure. If not, managed cloud services fall away or require a separate deployment mode.

Team. Who will install, update and monitor the database. Own server gives you control, but needs a person to look after it.

pgvector

pgvector is an open source extension that adds vector search to PostgreSQL. Vectors are stored next to other data, and the usual PostgreSQL features work for them, including transactions and JOINs.

The main advantage. If PostgreSQL is already in the company, a separate service is not needed: the same accesses, the same reservation procedures, the same command. Filtering by department or document date is a regular WHERE clause in SQL.

Search. pgvector supports exact and approximate nearest neighbor searches. Without an approximate index, the exact search gives the reference top-k closest vectors by the chosen metric and filters. This does not guarantee substantive relevance: it also depends on embeddings and preparation of documents. There are two types of indexes for acceleration:

• HNSW builds a multilevel graph. Gives a better balance of search speed and completeness, but builds more slowly and uses more memory. It can be created on an empty table.
• IVFFlat splits vectors into lists. Usually builds faster and uses less memory, but has a different trade-off between speed and completeness. Compare indexes for the same target completeness and resources. It is created after the table already has data, and the parameters are carefully selected for good completeness.

What to check. The most important caveat is about filters. According to the documentation, approximate indexes apply a filter after the index scan, so a query with a condition may return fewer rows than expected. For this, there is an iterative index scan, as well as partial indexes (for a small number of filter values) and partitioning (for a large one). For multiple tenants, the documentation suggests partitioning by list or individual tables.

Impact on current base. Building the HNSW index is significantly faster when the graph is placed in maintenance_work_mem, but the documentation cautions against setting this value so high that it exhausts server memory. If the vector search is hosted on the same server as the accounting system, check how index building and searches affect the main load. Sometimes the right solution is the same technology, but a separate instance of PostgreSQL.

Backup. pgvector uses PostgreSQL's write-ahead log (WAL), so it supports point-in-time replication and recovery. That is, the company's existing procedures are working.

Dimensionality limitation. For type vector, vectors of up to 2,000 dimensions can be indexed. For larger dimensions, the documentation suggests other types, such as halfvec with half precision, up to 4,000 dimensions. Check the dimensions of your model's embeddings before choosing.

Qdrant

Qdrant is a specialized vector search system with a client-server architecture. Data is stored in collections of points, each of which has a vector and optional metadata (payload). Dense and sparse vectors and hybrid search combining semantic and lexical are supported.

Filtering. Indexes on metadata fields, according to the documentation, extend the HNSW graph by allowing filter conditions to be applied during a semantic search, rather than after. For a knowledge base with many access conditions, this is a significant architectural difference worth checking on your filters.

Deployment Options. The documentation lists four: own open source deployment, Qdrant managed cloud, shared responsibility hybrid cloud on your clusters, and fully owned private cloud. It provides a path from own server to managed service without changing technology.

Resources. By default, the vectors are stored in RAM, but they can be moved to disk along with the HNSW index. For a production environment, the documentation recommends at least two replicas.

Operational cost. Separate service means separate installation, update, monitoring, backup and access rights. The table of hosting options lists automatic backup and recovery for the managed, hybrid, and private options, but not for the open source version. In an in-house deployment, the backup process is configured and tested by your team.

Pinecone and Chroma

These two options are often contrasted as "cloud service" and "local library". Both descriptions are incomplete.

Pinecone

Pinecone works as a managed service. The documentation describes indexes with a field scheme, in which you can combine full-text, semantic, sparse and hybrid search, built-in creation of embeddings on the service side, filtering by metadata and reordering of results.

Isolation. Namespaces separate data from different tenants in the same index.

Operation. The main difference from a private server is who is responsible for the infrastructure. The documentation mentions index backups, dedicated nodes for consistently high load, and the Bring Your Own Cloud option, i.e. deployment in the customer's own cloud account. For a company without an infrastructure team, this is a significant advantage. For a closed circuit, you need to check separately whether any of the available placement options are suitable for you.

Chroma

Chroma is an open source data infrastructure for AI licensed under the Apache 2.0 license. The documentation describes three ways of working: locally, in your own deployment, or in Chroma Cloud as a managed serverless service. Among the possibilities: dense, sparse and hybrid search, search by keywords and regular expressions without embeddings, filtering by metadata and search by images and other modalities.

The overview describes the capabilities of the product in general: the availability of each type of search should be checked for the specific version and mode. Chroma Cloud capability should not automatically be attributed to an on-premises installation. Therefore, it is incorrect to describe Chroma only as a tool for prototyping on a laptop. Its strength is that you can start very quickly and locally, and then move to the server or the cloud. But, as with any database, the requirements for productive operation (reservation, rights, monitoring, load) should be checked for the mode you plan to use, and not for the one in which the prototype was made.

Comparison table

The table describes what to check for each option. There are no delay and price columns on purpose: without the same measurement on your data, such numbers are misleading.

The data separation mechanism itself is not a substitute for authorization. The server must determine the allowed tenant, namespace, or filter from the authenticated user's identity, rather than trusting the value from the client request. Separately check attempts to read and change other people's data; this is your system requirement for any of the four bases.

How to honestly test

Comparing vector bases is easy to make unfair. For the result to mean something, six things must be the same.

Embeddings. The same model of embeddings, the same vectors. If one base creates the embeddings itself, and for the other you count them separately, you are comparing models, not bases.

Queries. A uniform set of real user requests, not synthetic random vectors.

Filters. The same conditions that will be in the work system: department, document status, access rights. A test without filters can show a completely different picture than working with them.

Search quality. First, measure ANN recall@k: what fraction of reference top-k neighbors for the same metric and filters was returned by the approximate index. An exact pgvector search can provide such a benchmark. Then separately check the meaningful relevance of the experts' answers and the quality of the model's final answers. The nearest vector is not necessarily the required business document.

Load. Realistic number of simultaneous requests and simultaneous data updates. A database that is fast on an empty server may behave differently during reindexing.

Configuration. Comparative resources and index settings. The HNSW or IVFFlat parameters have a significant effect on both speed and completeness, so write them next to the results.

Separately about the prototype. The fact that the base is convenient for a prototype does not answer the question of productive operation: redundancy, updates, rights, monitoring. Test the deployment mode you are going to use.

Migration decision and plan

The choice of base is not final for years. But migration is cheaper if it is planned before the start.

Unloading data. Ensure that vectors can be unloaded from the database of choice, along with metadata and fragment IDs, in a format suitable for loading into another system.

Keep the source separate. Keep the fragment source texts, metadata, and embedding model version in your own repository, independent of the vector base. Then the move is possible even without downloading the vectors: the base can be rebuilt from scratch. This will be needed when changing the embedding model, when the vectors will still have to be recalculated.

Restore. Restore the database from a backup on a test environment before launch. Write down how long it took and what you had to do manually.

Cost. Consider not only the price of the service or server, but also the team's time for support. For a managed service, check how the cost changes with the growth of volume and requests, according to the terms of your tariff.

Conditions for switching providers. For a managed service, find out how to download data at the end of the contract and how long it takes. For your own deployment, make sure that the procedure does not depend on one person.

What to do in the near future:

1. Write down the requirements: scale and growth, filters, tenant isolation, update frequency, redundancy, closed loop, command.
2. According to the table, choose one or two candidates that correspond to the existing infrastructure.
3. Test on the same embeddings, real queries, and working filters, with exact search as the benchmark for completeness.
4. Test restoring from a backup in the selected deployment mode.
5. Configure separate storage of source texts, metadata, and embedding model versions.
6. Record the unloading and moving procedure while the system is still small.

Frequently asked questions