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Reviews onG2
12 reviews tracked

The Bottom Line

Entry price

Free plan available, paid tiers above

Biggest pro

Fast performance

Biggest con

Smaller community

TL;DR - Qdrant

  • Qdrant is a vector database for AI similarity search applications
  • It stores and queries high-dimensional vectors with filtering support
  • Free and open-source, Cloud version available
Pricing: Free plan available
Best for: Growing teams
4.5/5 across review platforms

What is Qdrant?

Editorial review
Qdrant is an open-source vector similarity search engine. Features horizontal scaling, filtering, and high availability for production AI applications.

Available on: Web

Pros & Cons

Pros

  • Fast performance
  • Rust-based
  • Good filtering
  • Open source
  • Self-hostable

Cons

  • Smaller community
  • Cloud newer
  • Documentation improving
  • Fewer integrations
  • Less known

Ratings Across the Web

4.5(12 reviews)

Qdrant holds an aggregate rating of 4.5 out of 5 from 12 reviews across G2, last checked August 25, 2026.

Ratings aggregated from independent review platforms. Learn more

Key Features

Vector search engineHorizontal scalingFilteringHigh availabilityAuto-healingBackup and recoveryOpen-sourceOn-premise optionAir-gapped deploymentMulti-cloud support

Pricing Plans

Pricing checked Aug 25, 2026

Managed Cloud (Free)

Free

  • 1 GB free forever cluster
  • No credit card required
  • AWS/GCP/Azure regions
  • Horizontal and vertical scaling
  • Monitoring and alerting
  • High availability
  • Backup and disaster recovery
  • Zero-downtime upgrades

Managed Cloud (Paid)

null

  • Usage-based pricing
  • All free features
  • Standard support and uptime SLAs
  • Premium support available

Hybrid Cloud

null

  • All Managed Cloud benefits
  • Security and data isolation
  • Optimal latency
  • Standard or Premium support

Private Cloud

null

  • All Hybrid benefits
  • On-premise deployment
  • Maximum data sovereignty
  • Air-gapped environments
  • Premium Support included

Is Qdrant worth the price?

80/100

Qdrant occupies the sweet spot between developer-friendly open source and production-grade managed cloud.

The open-source engine is genuinely full-featured, unlike some competitors that gate critical features behind paid tiers, Qdrant's filtering, quantization, and distributed mode are all available self-hosted at zero cost. The free cloud tier at 1 GB RAM is the most generous among vector database providers (Pinecone's free tier limits to fewer dimensions and lower throughput).

The managed cloud pricing is usage-based with no per-query charges, which makes costs predictable once you size your cluster, you pay for infrastructure, not API calls. The main trade-off is that Qdrant Cloud does not publish exact per-resource rates on its website, requiring the pricing calculator or sales contact to get precise numbers.

A typical production cluster runs $150-200/month for moderate workloads, which is competitive with Pinecone Serverless and significantly cheaper than Pinecone's pod-based pricing. For teams already running Kubernetes, self-hosting Qdrant is the highest-value option, the Helm chart is well-maintained and horizontal scaling is straightforward.

Hidden Costs & Gotchas

Opaque per-unit pricing

Qdrant Cloud does not publish per-vCPU, per-GB-RAM, or per-GB-storage rates on its pricing page. You must use the pricing calculator at cloud.qdrant.io/calculator or contact sales to estimate costs. This makes budget planning harder compared to competitors with transparent rate cards

High availability doubles or triples cost

the free tier is single-node. Production high availability requires 3-node replication, which roughly triples your compute and storage costs. A $150/month single-node cluster becomes $450/month with HA, plan for this in production budgets

Backup storage is billed separately

snapshots and backups consume additional storage that is charged on top of your cluster's primary disk allocation. Frequent backups of large collections can add 20-50% to your storage costs

Embedding model costs are separate

Qdrant stores and searches vectors but does not generate them. You need an embedding API (OpenAI, Cohere, or open-source models) to convert text/images to vectors before storing them. At scale, embedding API costs can exceed Qdrant infrastructure costs, budget for both

Egress and data transfer

cloud providers charge for data transfer out of the region. If your application queries Qdrant from a different region or cloud provider, egress fees apply. Co-locate your application and Qdrant cluster in the same region

Premium tier minimum spend

the Premium tier requires an annual minimum commitment (amount undisclosed). Organizations wanting SSO and VPC links cannot access these features on a purely usage-based model, the minimum spend acts as an effective price floor

Reviews

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4.5/5

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Qdrant FAQ

How does Qdrant facilitate AI application development?

Qdrant serves as a vector database specifically designed for similarity search, providing the necessary infrastructure for production AI applications. It offers features like horizontal scaling and high availability to support demanding AI workloads.

What kind of user benefits most from Qdrant?

Qdrant is best suited for developers and organizations building AI applications that require efficient and scalable similarity search capabilities. Its open-source nature and self-hostable option appeal to those who need control over their data infrastructure.

How does Qdrant compare to Milvus?

Qdrant and Milvus both offer vector similarity search capabilities, but Qdrant is built with Rust, which contributes to its fast performance and good filtering. Qdrant is also open-source and self-hostable, providing flexibility for deployment.

What are the main trade-offs when choosing Qdrant?

A primary trade-off with Qdrant is its relatively smaller community and fewer integrations compared to more established solutions. Its cloud offering is also newer, and the documentation is still in the process of improving.

How is Qdrant priced?

Qdrant is available on a free tier, allowing users to get started without initial cost. For more extensive usage and additional features, paid plans are offered.

Can Qdrant be self-hosted?

Yes, Qdrant is an open-source vector database that can be self-hosted. This provides users with flexibility and control over their deployment environment.

Which features support high-performance AI applications in Qdrant?

Qdrant supports high-performance AI applications through its fast performance, horizontal scaling, and high availability features. It also offers robust filtering capabilities for precise similarity searches.

Source: qdrant.tech

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