Quick Summary
Not all vector databases are built for the same workloads. In this article, we compare 10 open-source vector databases, explain the criteria used to evaluate them, and provide practical guidance to help you identify the right option for your specific RAG requirements.
Table of Contents
As organizations begin to create a RAG pipeline, choosing a vector DB for RAG is frequently regarded as just one task that has to be done. More often than not, the difficult part comes later as organizations deal with increasing volumes of documents and more complicated retrieval demands. At such a stage, switching databases can prove to be a burdensome process.
The problem is that most vector database comparisons do not reflect how enterprise RAG systems operate. Many rely on vendor benchmarks, evaluate only a few databases, or use datasets that are too small to expose real scalability and retrieval challenges.
In order to understand the situation better, we conducted an assessment of 10 open-source vector databases based on the same data, the same embedding model, and the same testing environment. Instead of looking at the one benchmark only, we paid attention to the aspects that really matter in the real-world deployment, such as retrieval efficiency, scalability, metadata filtering capability, and readiness for production.
This article will walk you through 10 open-source databases that we tested and which vector DB you should choose to help you make a more informed decision for your RAG pipeline.
Most vector database comparisons fail to reflect how enterprise RAG systems actually operate. Many rely on vendor-published benchmarks, toy datasets, or synthetic workloads that prioritize indexing speed over retrieval quality.
To evaluate which vector DB for RAG performs best under realistic conditions, we tested each platform using the same embedding model, identical datasets, and consistent query workloads. The goal was not simply to measure raw performance, but to understand how each database behaved when supporting production-scale retrieval systems.
Testing Setup at a Glance
| Category | Configuration |
|---|---|
| Dataset Size | 100K, 1M, and 10M vectors |
| Content Types | Technical documentation, knowledge bases, and mixed enterprise content |
| Embeddings | Single embedding model across all tests |
| Queries | Fact-based, semantic, and metadata-filtered searches |
| Evaluation Focus | Retrieval quality, latency, scalability, filtering, and operational reliability |
We then measured each database across the metrics that matter most for enterprise RAG deployments:
This approach allowed us to compare databases in conditions that more closely resemble real-world RAG systems rather than isolated benchmark environments.
Not every metric carries the same weight in a RAG system. A database that returns results in milliseconds is still a poor choice if it retrieves irrelevant chunks. Likewise, strong retrieval quality means little if performance collapses once vector counts reach production scale. To separate real-world performers from benchmark winners, we scored every database across three areas.
The primary function of any vector database is to return information that will allow for proper responses from the LLM. We measured Recall@10, Recall@20, semantic search accuracy, hybrid search efficiency, and performance on edge cases. The databases that returned useful documents scored highly despite not being the fastest in testing.
Many databases tend to be good on paper with small amounts of data. Real-world RAG applications based on enterprise use-cases rarely remain small-scale. We measured the following parameters for databases of different sizes varying from 100K to 10M vectors – query latency, indexing time, ingestion throughput, and simultaneous query processing capacity.
In production settings, however, users do not query the entire knowledge base. They have queries that may be limited by department, client, document type, date range, or access. It was found that the implementation of Boolean filters, range queries, pre-filtering, post-filtering, and the overhead resulting from filter-based queries were factors that were key to assessing the functionality of the database. Filters were a key differentiating factor in many databases.
Choose a RAG development service provider that has stress-tested these platforms against enterprise workloads.
There is no one-size-fits-all Vector DB for RAG. The table below summarizes the scenarios where each database is most likely to succeed, helping you narrow your options before making a final decision.
| Database | License | Best Enterprise Use Case | Avoid If |
|---|---|---|---|
| Qdrant | Open Source | General enterprise RAG | You need billion-scale immediately |
| Milvus | Open Source | Massive-scale deployments | You want operational simplicity |
| Weaviate | Open Source | Hybrid search RAG | Resource efficiency is critical |
| Chroma | Open Source | Prototyping | Production scale is required |
| pgvector | Open Source | PostgreSQL environments | Starting greenfield |
| FAISS | Open Source | Embedded retrieval | You need database features |
| Elasticsearch | Open Source | Existing ELK deployments | Greenfield RAG deployment |
| Vespa | Open Source | Extreme-scale search | Small teams |
| LanceDB | Open Source | Edge/local AI | Large enterprise workloads |
| TiDB Vector Search | Open Source | SQL + vectors | Pure vector search needs |
Each database was tested using the same datasets, embeddings, and query workloads to ensure a fair comparison. While several platforms delivered strong results, their strengths varied significantly depending on scale requirements, filtering complexity, and retrieval patterns. Here’s how all 10 databases performed.
Verdict: The best all-round vector database for most enterprise RAG deployments.
Qdrant delivered the most balanced performance across our testing framework. While it did not dominate every individual benchmark, it consistently ranked near the top in retrieval quality, metadata filtering performance, and operational simplicity. This balance made it one of the few platforms that felt equally comfortable handling both proof-of-concept projects and production-scale deployments.
Its payload filtering capabilities were particularly impressive. As metadata complexity increased, Qdrant maintained strong retrieval consistency without introducing significant latency overhead. At vector counts between 1 million and 10 million, performance remained predictable, making it a strong choice for enterprise knowledge bases, customer support systems, and internal AI assistants.
Where It Excels
Where It Falls Short
Best Enterprise Use Case
Mid-to-large enterprise RAG systems requiring filtering-heavy retrieval.
Verdict: The strongest option for billion-scale vector workloads.
Milvus was built for organizations that expect vector volumes to grow aggressively over time. During testing, it demonstrated exceptional scalability and remained stable under the largest workloads. Organizations planning large multi-tenant deployments or AI platforms serving millions of users are likely to appreciate its distributed architecture.
The trade-off is operational complexity. Milvus requires more infrastructure planning, monitoring, and tuning than some newer alternatives. Teams with dedicated platform engineering resources will find this manageable, but smaller organizations may find the learning curve unnecessary for their current scale.
Where It Excels
Where It Falls Short
Best Enterprise Use Case
Massive production RAG deployments with long-term scale requirements.
Verdict: The best hybrid search experience available out of the box.
Weaviate distinguished itself through its strong hybrid retrieval capabilities, combining vector search and keyword search with minimal configuration. In our testing, this approach often produced more reliable results for enterprise knowledge bases where users searched using a mix of natural language, product names, technical terminology, and exact keywords. Organizations that rely heavily on retrieval accuracy for customer support, documentation search, or internal knowledge management will appreciate this flexibility.
Another advantage is the platform’s built-in capabilities. Features such as multi-tenancy and integrated retrieval functionality reduce the amount of custom engineering required to launch production systems. However, these conveniences come at the cost of higher infrastructure requirements. Compared to lighter alternatives, Weaviate typically demands more resources to achieve similar scale and performance.
Where It Excels
Where It Falls Short
Best Enterprise Use Case
Knowledge-intensive RAG applications requiring semantic and keyword retrieval.
Verdict: Excellent for prototypes but difficult to recommend for serious enterprise production.
Chroma remains one of the most developer-friendly vector databases available today. Its straightforward setup process, intuitive APIs, and minimal operational overhead make it particularly attractive for teams building their first RAG prototype or experimenting with retrieval workflows. During testing, it was among the fastest platforms to deploy and begin querying.
The challenge appears as workloads become more demanding. While Chroma performs well in small-scale environments, it lacks many of the scalability, operational, and enterprise-focused capabilities found in more mature vector databases. Organizations planning large production deployments may eventually outgrow its architecture and need to migrate to a platform designed for greater scale and reliability.
Where It Excels
Where It Falls Short
Best Enterprise Use Case
Proof-of-concepts, internal experimentation, and early-stage RAG development.
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Verdict: The easiest choice when PostgreSQL already exists in your stack.
pgvector takes a different approach from most vector databases by extending PostgreSQL rather than introducing an entirely new platform. For organizations that already rely heavily on PostgreSQL, this can significantly reduce adoption friction. Existing operational processes, monitoring tools, backup strategies, and developer expertise can often be reused, eliminating the need to manage another database technology.
During testing, pgvector delivered solid retrieval performance for small to medium-sized RAG workloads and benefited from the maturity of the PostgreSQL ecosystem. However, as vector counts increased and query complexity grew, purpose-built vector databases generally demonstrated better scalability and performance. While pgvector is more than capable for many enterprise applications, organizations planning very large deployments may eventually encounter limits that dedicated vector databases are specifically designed to address.
Where It Excels
Where It Falls Short
Best Enterprise Use Case
Organizations already standardized on PostgreSQL that want to add vector search without introducing a separate database platform.
Verdict: A great vector search library, not a vector database.
FAISS remains one of the most influential technologies in the vector search ecosystem and serves as the foundation for many modern vector databases. During testing, it consistently delivered exceptional approximate nearest neighbor (ANN) search performance, particularly in scenarios where raw retrieval speed was the primary objective. Its efficient indexing algorithms and support for large-scale vector operations make it a powerful tool for developers building custom retrieval systems.
However, FAISS is fundamentally a vector search library rather than a complete database platform. It lacks many of the capabilities enterprise teams typically require, including metadata filtering, distributed operations, replication, access controls, and operational management features. As a result, organizations often need to build substantial infrastructure around FAISS to support production RAG workloads.
Where It Excels
Where It Falls Short
Best Enterprise Use Case
Offline retrieval systems, embedded AI applications, and custom search platforms where teams want full control over the retrieval architecture.
Verdict: Makes sense if you’re already invested in ELK, rarely as a fresh RAG choice.
Elasticsearch brings decades of search expertise to the vector database conversation. During testing, its biggest advantage was the ability to combine traditional keyword search with vector retrieval, making it particularly effective for applications where exact terms, product identifiers, error codes, and semantic intent all influence relevance. Organizations already familiar with search-driven applications will find its retrieval capabilities both powerful and flexible.
The platform’s maturity is another major strength. Elasticsearch benefits from a large ecosystem, extensive documentation, robust monitoring tools, and a proven track record in production environments. For companies already operating within the ELK stack, adding vector search capabilities can be a natural extension of existing infrastructure and operational processes.
Where It Excels
Where It Falls Short
Best Enterprise Use Case
Organizations already running Elasticsearch extensively and looking to extend existing search infrastructure with vector retrieval capabilities.
Verdict: The highest performance ceiling and the steepest learning curve.
Vespa was one of the most technically impressive platforms in our evaluation. Designed for large-scale search, recommendation, and retrieval workloads, it offers a level of ranking flexibility and query customization that few vector databases can match. During testing, it demonstrated strong performance across large datasets and provided extensive control over how retrieval and ranking logic are executed.
Its greatest strength lies in advanced ranking capabilities. Organizations can combine vector search with traditional ranking signals, business rules, and custom relevance models to create highly sophisticated retrieval pipelines. This makes Vespa particularly attractive for companies building search-driven products, recommendation engines, or large-scale AI applications where ranking quality directly impacts business outcomes.
Where It Excels
Where It Falls Short
Best Enterprise Use Case
Very large-scale search, recommendation, and AI retrieval systems where advanced ranking control and long-term scalability are top priorities.
Verdict: A promising embedded-first option worth watching.
LanceDB takes a different approach from many of the platforms in this comparison by focusing on local-first and embedded vector search rather than large distributed deployments. During testing, it delivered a streamlined developer experience and performed particularly well in scenarios where vector retrieval needed to run close to the application rather than through a dedicated database cluster.
Its architecture makes it especially attractive for edge AI, desktop applications, private AI assistants, and environments where data locality matters. Organizations looking to deploy RAG systems on local machines or within resource-constrained environments may find LanceDB significantly easier to manage than traditional client-server vector databases.
Where It Excels
Where It Falls Short
Best Enterprise Use Case
Edge AI applications, local-first RAG systems, desktop AI products, and environments where running retrieval infrastructure locally is preferred over managing dedicated database clusters.
Verdict: The strongest option when SQL and vector retrieval must coexist.
TiDB Vector Search stands out by bringing vector retrieval directly into a distributed SQL database environment. For organizations already managing large volumes of structured business data, this approach can simplify architecture significantly. Rather than maintaining separate systems for transactional data and vector search, teams can work within a unified platform that supports both workloads.
During testing, TiDB’s greatest advantage was its ability to combine traditional SQL queries with semantic retrieval. This proved particularly valuable for enterprise applications where vector search results frequently need to be enriched with structured business data, customer records, operational metrics, or transactional information. Its HTAP (Hybrid Transactional and Analytical Processing) capabilities further strengthen its appeal for organizations running data-intensive applications.
Where It Excels
Where It Falls Short
Best Enterprise Use Case
Operational applications that combine structured business data, transactional workloads, and semantic retrieval within a single platform.
If you’ve made it this far, you’re probably asking the same question we did at the start of this evaluation: Which vector database should I actually choose? The answer depends on your priorities, but the table below provides a practical starting point based on the strengths and trade-offs we observed during testing.
| Scenario | Recommended Options |
|---|---|
| General enterprise RAG | Qdrant, Weaviate |
| PostgreSQL-centric stack | pgvector |
| Massive-scale deployments | Milvus, Vespa |
| Hybrid search requirements | Weaviate, Elasticsearch |
| Edge and local AI applications | |
| Custom retrieval infrastructure | FAISS |
| Structured + semantic workloads | TiDB Vector Search |
After evaluating 10 open-source vector databases across multiple enterprise RAG scenarios, one thing became clear: the right choice depends on your data volumes, retrieval requirements, scalability goals, and operational needs.
There is no universally best vector DB for RAG. The most successful organizations identify their requirements first and then select the platform that best supports their current and future workloads. Just as importantly, retrieval quality depends on more than database selection. Chunking strategy, embeddings, metadata design, and retrieval architecture all play a critical role in RAG performance.
As a leading data engineering company, Bacancy helps organizations evaluate vector databases, design scalable data architectures, and build production-ready RAG systems that align with long-term business goals. Choosing the right foundation today can make scaling AI initiatives significantly easier tomorrow.