Qdrant vs Weaviate: Which Vector Database Should You Use in 2026?

I have used both Qdrant and Weaviate in production. They are both good. They are also built around different assumptions about what kind of team will use them and what kind of application they are building for.

Qdrant assumes you will bring your own embeddings, write clean REST or gRPC calls, and want maximum control over what the database is doing. Weaviate assumes you might want the database to handle more of the stack, including embedding, and prefers a GraphQL interface that treats vectors as part of a richer object model.

Neither assumption is wrong. But they lead to very different experiences, and the right one depends on what your team already knows and what kind of application you are building.

The Short Version

Use Qdrant if you want the fastest retrieval, prefer REST over GraphQL, bring your own embedding model, or care about infrastructure cost at scale.

Use Weaviate if you want built-in vectorizers so the database handles embedding for you, need first-class multi-tenancy for a SaaS product, or prefer a schema-driven object model over bare vector storage.

For everything else, keep reading.

What Each One Is

Qdrant

Qdrant is an open-source vector database written in Rust. It stores vectors in collections, attaches arbitrary JSON payloads to each point, and lets you search by similarity and filter by payload at the same time.

It is built for one thing: fast, accurate vector search. It does not try to be an object store, a knowledge graph, or a full-text search engine. You bring your embeddings. Qdrant stores and retrieves them.

Source code is on GitHub under Apache 2.0. Managed option at cloud.qdrant.io with a free tier.

Weaviate

Weaviate is an open-source vector database written in Go. It organizes data into classes (similar to tables) with a defined schema. Each object in a class has properties and an associated vector. You can let Weaviate generate the vector for you using a vectorizer module, or provide your own.

Weaviate's query interface is GraphQL. It also exposes a REST API for management operations and a gRPC path for performance-critical queries.

Source code is on GitHub under BSD 3-Clause. Managed option at weaviate.io/pricing through Weaviate Cloud Services.

Architecture

The architectural difference between Qdrant and Weaviate explains most of the practical trade-offs that follow.

Qdrant architecture

Qdrant is a pure vector store with payload. Each collection holds vectors of a fixed dimension. Each vector has an ID and an optional payload (JSON dictionary). The HNSW graph is built per-segment and merged by an optimizer that runs in the background. You can tune nearly every parameter: m, ef_construction, ef at query time, quantization type, segment size, and on-disk vs in-memory storage.

The Rust implementation gives it very low per-request overhead. There are no modules, no plugins, and no runtime indirection. It does one thing and does it fast.

Weaviate architecture

Weaviate treats vectors as a property of objects in a class. The schema defines what properties each object has, what types they are, and which vectorizer module generates the vectors. The vectorizer module sits inside the Weaviate process or as a sidecar service and runs inference when you insert data.

This means Weaviate can handle the embedding step for you. You insert raw text and Weaviate calls the configured model to produce the vector. This is convenient but adds latency to writes and creates a dependency on the vectorizer service.

Weaviate also maintains an inverted index alongside the HNSW graph. This enables keyword search (BM25) without a separate search engine. Both indexes are updated on every write.

Qdrant model:
  You → [embed text externally] → upsert (id, vector, payload) → Qdrant

Weaviate model:
  You → insert (id, properties) → Weaviate → [calls text2vec module] → stores (vector + object)

Developer Experience and API Style

This is the most immediate difference for teams evaluating the two.

Qdrant: REST and Python client

from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct, Filter, FieldCondition, MatchValue

client = QdrantClient(host="localhost", port=6333)

# Create collection
client.create_collection(
    collection_name="articles",
    vectors_config=VectorParams(size=1536, distance=Distance.COSINE)
)

# Insert
client.upsert(
    collection_name="articles",
    points=[
        PointStruct(
            id=1,
            vector=[0.1, 0.2, ...],  # your embedding
            payload={"title": "Intro to RAG", "category": "rag"}
        )
    ]
)

# Search with filter
results = client.search(
    collection_name="articles",
    query_vector=query_embedding,
    query_filter=Filter(
        must=[FieldCondition(key="category", match=MatchValue(value="rag"))]
    ),
    limit=5,
    with_payload=True
)

The API is straightforward. Collections, points, payloads, and filters are the main concepts. If you know Python and REST, you can be productive within an hour.

Weaviate: schema-first, GraphQL queries

import weaviate

client = weaviate.Client("http://localhost:8080")

# Define a schema class
schema = {
    "class": "Article",
    "vectorizer": "text2vec-openai",  # Weaviate calls OpenAI to embed
    "moduleConfig": {
        "text2vec-openai": {
            "model": "text-embedding-3-small"
        }
    },
    "properties": [
        {"name": "title", "dataType": ["text"]},
        {"name": "content", "dataType": ["text"]},
        {"name": "category", "dataType": ["text"]}
    ]
}
client.schema.create_class(schema)

# Insert (Weaviate generates the vector automatically)
client.data_object.create(
    data_object={
        "title": "Intro to RAG",
        "content": "RAG combines retrieval with generation...",
        "category": "rag"
    },
    class_name="Article"
)

# GraphQL search
result = client.query.get(
    "Article", ["title", "content", "category"]
).with_near_text(
    {"concepts": ["retrieval augmented generation"]}
).with_where({
    "path": ["category"],
    "operator": "Equal",
    "valueText": "rag"
}).with_limit(5).do()

Weaviate requires you to define a schema before inserting. The vectorizer setting tells Weaviate which module to use for embedding. The GraphQL query syntax uses a builder pattern that is expressive but requires more learning.

The API verdict

If your team writes REST APIs daily and brings its own embeddings, Qdrant is faster to get productive with. If your team prefers GraphQL and wants the database to handle vectorization, Weaviate's model is cleaner.

Performance

Both use HNSW for approximate nearest neighbor search. The performance difference comes from implementation language and architectural overhead.

Qdrant is written in Rust. Weaviate is written in Go. In benchmarks from ann-benchmarks.com and the Qdrant benchmark suite, Qdrant consistently shows higher throughput and lower latency at the same hardware level.

The gap is most visible at high QPS. At 1,000 queries per second on a 10-million-vector dataset, Qdrant's Rust implementation uses less CPU and produces lower tail latency than Weaviate on equivalent hardware.

At lower scale (under 1 million vectors, under 100 QPS), both are fast enough that the difference is not meaningful for most applications. You will not feel the performance gap in development or small production workloads.

Numbers are representative benchmarks on equivalent hardware. Your results will vary based on dimension size, filter complexity, and hardware.

Winner: Qdrant on raw performance, particularly at scale and high QPS.

Filtering

Both databases support filtering by metadata at query time. The implementation quality matters a lot for real applications.

Qdrant filtering

Qdrant uses a filtered HNSW algorithm. When you add a filter, Qdrant traverses the HNSW graph while checking payload conditions in parallel. It does not scan the full index and then filter; the filter is applied during traversal. This keeps recall high even with selective filters.

from qdrant_client.models import Filter, FieldCondition, MatchValue, Range, MatchAny

# Complex filter: category is rag OR vector-search, published after 2025, not draft
results = client.search(
    collection_name="articles",
    query_vector=query_vec,
    query_filter=Filter(
        must=[
            FieldCondition(
                key="category",
                match=MatchAny(any=["rag", "vector-search"])
            ),
            FieldCondition(
                key="published_year",
                range=Range(gt=2025)
            )
        ],
        must_not=[
            FieldCondition(key="draft", match=MatchValue(value=True))
        ]
    ),
    limit=10,
    with_payload=True
)

For selective filters that narrow the dataset significantly, add a payload index to the filtered field:

client.create_payload_index(
    collection_name="articles",
    field_name="category",
    field_schema="keyword"
)

Weaviate filtering

Weaviate filters using its inverted index alongside the HNSW index. The filter narrows the candidate set using the inverted index first, then the HNSW search runs over the filtered subset.

result = client.query.get(
    "Article", ["title", "category", "published_year"]
).with_near_vector(
    {"vector": query_vec}
).with_where({
    "operator": "And",
    "operands": [
        {
            "path": ["category"],
            "operator": "ContainsAny",
            "valueTextArray": ["rag", "vector-search"]
        },
        {
            "path": ["published_year"],
            "operator": "GreaterThan",
            "valueInt": 2025
        }
    ]
}).with_limit(10).do()

Weaviate's approach works well for broad filters. For very selective filters (where less than 1% of the dataset matches), Weaviate can struggle because the filtered HNSW traversal may run out of candidates and fall back to a brute-force scan within the filtered subset.

Winner: Qdrant for complex and selective filtering. Weaviate is solid for common cases but Qdrant's filtered HNSW implementation handles edge cases better.

Hybrid Search

Hybrid search is where both databases are strong, but through different mechanisms.

Qdrant hybrid search

Qdrant stores dense and sparse vectors separately in named vector slots. At query time, it runs both searches and fuses the results using reciprocal rank fusion (RRF).

from qdrant_client.models import Prefetch, FusionQuery, Fusion

results = client.query_points(
    collection_name="articles_hybrid",
    prefetch=[
        Prefetch(query=dense_vector, using="dense", limit=20),
        Prefetch(query=sparse_vector, using="sparse", limit=20)
    ],
    query=FusionQuery(fusion=Fusion.RRF),
    limit=5,
    with_payload=True
)

You control the embedding models for both dense and sparse separately. This gives maximum flexibility but requires you to generate both types of embeddings before inserting.

Weaviate hybrid search

Weaviate has BM25 built into its inverted index. The hybrid search API takes an alpha parameter to balance between BM25 keyword ranking and vector similarity.

result = client.query.get(
    "Article", ["title", "content"]
).with_hybrid(
    query="retrieval augmented generation",
    alpha=0.75  # 0 = pure keyword, 1 = pure vector, 0.75 = mostly vector
).with_limit(5).do()

Weaviate's hybrid search is simpler to implement because BM25 uses the text properties you already stored. No separate sparse vector generation step. The trade-off is less control: you cannot use a custom sparse encoder, and the fusion weighting is a single scalar.

Winner: draw. Qdrant gives more control and is better for custom sparse encoders. Weaviate is simpler when BM25 is sufficient and you do not want to manage a separate sparse vector generation pipeline.

Multi-Tenancy

This is the clearest area where Weaviate wins.

Weaviate multi-tenancy

Weaviate has first-class multi-tenancy built into its schema. You define a class as multi-tenant and then activate tenants individually. Each tenant's data is isolated in its own HNSW graph and inverted index. Queries are automatically scoped to the requesting tenant.

# Enable multi-tenancy on a class
schema = {
    "class": "UserDocument",
    "multiTenancyConfig": {"enabled": True},
    "properties": [
        {"name": "content", "dataType": ["text"]}
    ]
}
client.schema.create_class(schema)

# Activate tenants
client.schema.add_class_tenants(
    "UserDocument",
    [
        weaviate.Tenant(name="tenant_001"),
        weaviate.Tenant(name="tenant_002")
    ]
)

# Insert for a specific tenant
client.data_object.create(
    data_object={"content": "..."},
    class_name="UserDocument",
    tenant="tenant_001"
)

# Query scoped to one tenant
result = client.query.get(
    "UserDocument", ["content"]
).with_near_text({"concepts": ["search query"]}).with_tenant("tenant_001").do()

Weaviate also supports hot tenants (loaded into memory) and cold tenants (offloaded to disk) to manage memory across many tenants efficiently.

Qdrant multi-tenancy

Qdrant does not have a native multi-tenancy concept. The two common patterns are:

Pattern 1: One collection per tenant. Simple and perfectly isolated, but you end up with thousands of small collections that are hard to manage at scale.

Pattern 2: Shared collection with a tenant ID in the payload

# Insert with tenant ID in payload
client.upsert(
    collection_name="documents",
    points=[
        PointStruct(
            id=doc_id,
            vector=embedding,
            payload={"tenant_id": "tenant_001", "content": "..."}
        )
    ]
)

# Filter every query by tenant
results = client.search(
    collection_name="documents",
    query_vector=query_vec,
    query_filter=Filter(
        must=[FieldCondition(key="tenant_id", match=MatchValue(value="tenant_001"))]
    ),
    limit=10
)

This works, but it relies on application-level discipline to always include the tenant filter. A missed filter in one code path exposes all tenant data. Weaviate's native multi-tenancy makes that mistake impossible at the query level.

Winner: Weaviate for multi-tenant SaaS applications. It is a meaningful design advantage.

Vectorizer Modules (Weaviate-only)

Weaviate's module system lets the database handle embedding for you. This is a real convenience that Qdrant does not offer.

# With text2vec-openai: just insert text, Weaviate embeds it
client.data_object.create(
    data_object={"content": "Vector search enables semantic retrieval."},
    class_name="Article"
)

# With text2vec-cohere: same approach, different model
# With text2vec-huggingface: uses a HuggingFace model
# With multi2vec-clip: multimodal image+text embeddings

Available modules include text2vec-openai, text2vec-cohere, text2vec-huggingface, multi2vec-clip (for image and text), and ref2vec-centroid (for user-based personalization).

The generative module (generative-openai, generative-cohere) goes further: it generates answers over retrieved results inside a single Weaviate query. You can retrieve and generate in one round trip.

# Retrieve and generate in one Weaviate query
result = client.query.get(
    "Article", ["content"]
).with_near_text(
    {"concepts": ["vector search"]}
).with_generate(
    single_prompt="Summarize this article in two sentences: {content}"
).with_limit(3).do()

This is a genuinely different architecture than Qdrant. With Qdrant, you always bring your own embedding step and your own LLM call separately. With Weaviate, both can be inside the database.

The trade-off: tight coupling to specific model providers. Swapping models means changing your schema configuration, not just your embedding code. And if the vectorizer service goes down, writes fail.

Cost Comparison

Both are open source and free to self-host. Managed pricing differs.

Self-hosted cost

Weaviate's memory footprint is higher than Qdrant's at the same dataset size because it maintains both an HNSW graph and an inverted index, plus the module sidecar processes.

Managed cloud cost

Qdrant Cloud: Free tier with 1GB storage. Paid tiers start at around $25/month. Pricing is based on cluster size.

Weaviate Cloud Services (WCS): Free sandbox tier (expires after 14 days). Paid tiers are priced on the same sandbox-to-professional scale. WCS tends to be slightly more expensive than Qdrant Cloud for equivalent storage because Weaviate's memory requirements are higher.

Winner: Qdrant on cost, both self-hosted and managed. Qdrant's Rust implementation uses significantly less memory, which translates directly to smaller instances and lower bills.

Ecosystem and Integrations

Both have strong integrations with the main frameworks teams use for building AI applications.

Both integrate cleanly with LangChain and LlamaIndex as the vector store component in a RAG pipeline.

When to Use Each One

Use Qdrant when:

• You bring your own embeddings and do not need the database to vectorize for you
• You need the highest possible query throughput on a given hardware budget
• Your filtering logic is complex or highly selective
• You want the simplest possible REST/Python API without schema definitions
• Cost at scale is a constraint
• You are not building a multi-tenant SaaS product

Use Weaviate when:

• You want the database to handle embedding so you do not manage separate embedding pipelines
• You are building a multi-tenant SaaS application and need native tenant isolation
• Your team is comfortable with GraphQL and prefers a schema-based data model
• You want to use the generative module to retrieve-and-generate in one query
• You are building a knowledge graph or object-centric application, not just a vector store

Use pgvector instead of either when:

• You are already on PostgreSQL with under 1 to 2 million vectors
• You want to keep everything in one database

See pgvector vs Pinecone and how to choose a vector database for the broader comparison.

Side-by-Side Summary

Summary

Qdrant and Weaviate are both solid production vector databases. The choice comes down to two things: how much you want the database to do versus control yourself, and whether you need native multi-tenancy.

Qdrant is the leaner, faster option. It does one thing and does it very well. You own the embedding step and the LLM integration. In return, you get better performance per dollar, a simpler API, and excellent filtered search.

Weaviate is the more opinionated option. It wants to be a larger part of your stack. The vectorizer modules, the generative modules, and native multi-tenancy are real advantages for teams building complex applications where that tight integration is worth the added complexity.

For most RAG pipelines and semantic search applications in 2026, I reach for Qdrant first. For multi-tenant SaaS products where many customers share one deployment, Weaviate's native tenant isolation makes it the clearer choice.

Related Reading

• Qdrant Getting Started Guide
• Pinecone vs Qdrant
• pgvector vs Pinecone
• How to Choose a Vector Database
• RAG Architecture Explained
• Dense vs Sparse Vectors
• HNSW Algorithm Explained