Milvus and Qdrant are both open-source vector databases with large GitHub followings, but they emphasize different strengths and pricing paths. Milvus is…
Best for Milvus: Teams that need to self-host at very large scale (tens of billions of vectors), want flexible deployment modes from an embedded library up to a distributed enterprise cluster, and are already integrating with LangChain, LlamaIndex, OpenAI, or Hugging Face.
Best for Qdrant: Teams that want a documented free-forever cloud tier to start, need hybrid search combining dense and sparse vectors (BM25, SPLADE++), or want quantization to cut memory usage by up to 64x.
At a Glance
Milvus
Qdrant
Primary category
Databases
Databases
Rating
Not documented
Not documented
Pricing model
Open Source / Freemium
Open-core: free self-hosted/open-source engine plus usage-based Qdrant Cloud tiers (Free, Standard pay-as-you-go, Premium minimum-spend) and custom Hybrid/Private Cloud and Enterprise plans
Starting price
Free (open source, self-hosted); Zilliz Cloud from a free tier or roughly $99/month for dedicated clusters
Free (open source and free cloud tier); paid Standard cloud clusters are billed hourly based on resources used
Free plan
Yes
Yes
Free trial
Not documented
Not documented
Platforms
Not documented
Web
Team collaboration
Not documented
Not documented
AI features
Yes
Yes
Public API
Yes
Yes
Key Differences
Managed cloud pricing transparency
Milvus: Managed hosting (Zilliz Cloud) pricing could not be verified from the official pricing page during this research; no pricing plans are documented.
Qdrant: Publishes a free-forever tier (0.5 vCPU, 1GB RAM, 4GB disk) plus Standard (usage-based) and Premium (minimum spend) tiers.
Documented managed pricing lets teams evaluate cost without needing a sales conversation.
Search technology emphasis
Milvus: Global Index technology powers high-speed similarity search across huge datasets, with metadata filtering and hybrid/multi-vector queries.
Qdrant: Hybrid search explicitly combines dense and sparse vectors using BM25 and SPLADE++, plus native multivector support for multimodal retrieval.
Qdrant's facts name the specific sparse-retrieval algorithms (BM25, SPLADE++) it combines with dense vectors, giving more concrete insight into its hybrid search implementation.
Scale ceiling
Milvus: Explicitly built to scale to tens of billions of vectors.
Qdrant: Uses quantization to reduce memory usage by up to 64x, which helps efficiency at scale but does not state a specific vector-count ceiling.
Milvus's facts make an explicit massive-scale claim, while Qdrant's facts emphasize memory efficiency rather than a stated absolute scale ceiling.
Implementation language and performance profile
Milvus: No implementation language is specified in the available facts.
Qdrant: Built in Rust with SIMD optimization for performance.
Rust with SIMD optimization is a specific, documented performance characteristic that engineering teams may weigh when evaluating latency-sensitive workloads.
Deployment flexibility
Milvus: Lite (embedded library), Standalone (single machine), or Distributed (enterprise cluster).
Qdrant: Cloud, Hybrid Cloud, Private Cloud, and Edge (Edge deployment is still in beta).
Milvus's modes scale from embedded to distributed compute topology, while Qdrant's modes span cloud-hosting arrangements including an in-beta Edge option.
Feature-by-Feature
Core Features
Feature
Milvus
Qdrant
Open-source / self-hostable
Available
Available
Hybrid search (dense + sparse vectors)
Available
Available
Metadata filtering
Available
Available
Built-in web UI
Not documented
Available
AI framework integrations (LangChain, LlamaIndex, OpenAI, Hugging Face)
Available
Not documented
Quantization for memory reduction
Not documented
Available
Pricing & Plans
Feature
Milvus
Qdrant
Documented managed cloud pricing
Unavailable
Available
Free-forever cloud tier
Not documented
Available
SSO / private VPC links
Not documented
Limited
Pricing Compared
Starting price reflects the lowest paid tier, not the full cost for every team size or usage level.
Milvus
Open Source (self-hosted) — Free N/A
Zilliz Cloud Free Tier — Free Monthly
Zilliz Cloud Serverless — Pay-as-you-go, about $4 per million vCUs Usage-based
Zilliz Cloud Dedicated — From about $99/month Monthly
Qdrant
Free (Open Source) — Free N/A
Qdrant Cloud Free Tier — $0 N/A
Standard — Usage-based, billed hourly Hourly usage, monthly invoicing
Premium — Custom minimum spend Custom
Hybrid/Private Cloud — Custom quote Custom
Pros & Cons
Milvus
Pros
Completely free and open source for self-hosted use
Proven to scale to billions of vectors in production
Vendor-neutral governance under the Linux Foundation
Flexible deployment options from embedded and local to distributed clusters
Strong ecosystem integration with RAG and LLM tooling
Cons
Self-hosting a distributed cluster requires significant DevOps expertise
Zilliz Cloud costs can scale with usage for large workloads
Steeper learning curve than simpler vector stores like Chroma
Feature differences exist between Milvus Lite and full cluster deployments
Competing managed alternatives like Pinecone offer simpler zero-ops onboarding
Qdrant
Pros
Free, open-source core engine with no licensing fees for self-hosting
Built in Rust for strong performance and memory efficiency at scale
Multiple deployment options, including Hybrid and Private Cloud for compliance-sensitive workloads
Well-funded and growing quickly, with backing from established venture investors
Multi-language client support covers most modern AI development stacks
Free cloud tier makes it easy to prototype before committing to paid infrastructure
Cons
Standard and Premium cloud pricing is usage-based and not fully transparent without using the calculator or contacting sales
Premium tier requires a minimum spend commitment rather than flat predictable pricing
Free cloud tier is limited to a single small node, unsuitable for production workloads
Competition from well-funded alternatives like Pinecone, Weaviate, and Milvus is intense
Self-hosting for full control requires infrastructure and ops expertise
Company is still relatively young (founded 2021), so long-term enterprise track record is shorter than some legacy database vendors
Use Cases
Choose Milvus: Teams that need to self-host at very large scale (tens of billions of vectors), want flexible deployment modes from an embedded library up to a distributed enterprise cluster, and are already integrating with LangChain, LlamaIndex, OpenAI, or Hugging Face.
Choose Qdrant: Teams that want a documented free-forever cloud tier to start, need hybrid search combining dense and sparse vectors (BM25, SPLADE++), or want quantization to cut memory usage by up to 64x.
Need both: A team building a large-scale RAG pipeline might self-host Milvus for its billions-of-vectors capacity in a core data store while using Qdrant's free-forever cloud tier for a smaller, hybrid-search-driven feature like semantic search with keyword filtering, since the two emphasize different scale/search tradeoffs.
Milvus
Retrieval-augmented generation for LLMs — Store and retrieve embeddings to ground large language model responses in factual data.
Semantic and multimodal search — Search text, images, or other content by meaning rather than exact keyword match.
Recommendation and similarity matching — Find similar items or users based on vector embeddings.
Qdrant
Retrieval-augmented generation (RAG) pipelines — AI teams use Qdrant to store and search document embeddings, retrieving relevant context to feed into large language model prompts.
Semantic search and recommendation systems — Product teams use Qdrant to power semantic search and similarity-based recommendation features across catalogs, content, or user profiles.
Compliance-sensitive AI deployments — Regulated enterprises use Qdrant's Hybrid or Private Cloud options to run vector search within their own infrastructure while meeting data residency requirements.
Frequently Asked Questions
Is Milvus free?
Yes, Milvus is open-source and free to self-host, though its managed Zilliz Cloud pricing could not be verified from the official pricing page.
Does Qdrant have a free plan?
Yes, a free-forever tier is available for testing and prototypes (0.5 vCPU, 1GB RAM, 4GB disk).
Is Qdrant open source?
Yes, it can be self-hosted, with 30K+ GitHub stars, similar to Milvus's 45.2K+ GitHub stars.
What deployment modes does Milvus support?
Lite (embedded library), Standalone (single machine), and Distributed (enterprise-scale).
Does Qdrant support hybrid search?
Yes, combining dense and sparse vectors including BM25 and SPLADE++.
Does Milvus integrate with AI frameworks?
Yes, including LangChain, LlamaIndex, OpenAI, and Hugging Face.