Databases · head to head
Dremio vs Qdrant

Dremio
Databases
SQL query engine and lakehouse layer over Iceberg tables in object storage
- From
- Free
- Rated
- -

Qdrant
Databases
High-performance vector database for similarity search and embedding-based retrieval
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Dremio reflections consume compute and storage to build and refresh continuously, so a team that enables them widely discovers that background maintenance rather than user queries drives the DCU bill.; Qdrant free tier extremely limited (0.5 vCPU, 1GB RAM, 4GB disk); suitable only for experiments
- Prices and features above were last checked on 31 August 2026.
Where they differ
Only the attributes on which Dremio and Qdrant actually diverge.
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Databases).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in Dremio
- Arrow-based execution
- Reflections
- Semantic layer
- Iceberg catalogue
- Federated queries
- Autonomous management
- Fine-grained access control
- BI connectors
Only in Qdrant
Nothing recorded that Dremio does not also cover.
What people use each for
The jobs each tool is most often brought in to do.
Dremio
- A company with petabytes of Parquet in S3 that wants BI dashboards without duplicating it into a warehousenot Qdrant
- A data platform team standardising on Apache Iceberg and needing a SQL engine plus catalogue that does not lock the tables innot Qdrant
- An analytics group accelerating slow lake queries with Reflections instead of hand-built aggregate tablesnot Qdrant
- A regulated enterprise that must keep data on premises but wants a modern lakehouse SQL layernot Qdrant
Qdrant
- Retrieval-augmented generation (RAG) backends for LLM applicationsnot Dremio
- Semantic search across large document corporanot Dremio
- Multimodal retrieval (text, images, video) for recommendation systemsnot Dremio
- Similarity-based product or content recommendationsnot Dremio
- Real-time vector indexing for streaming embedding datanot Dremio
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Dremio
- Reflections consume compute and storage to build and refresh continuously, so a team that enables them widely discovers that background maintenance rather than user queries drives the DCU bill.
- Self-managing Dremio on Kubernetes requires real platform engineering capacity for tuning executors, memory and coordinator sizing, and it is not comparable in effort to running a managed warehouse.
- The Community Edition lacks the security and governance features most enterprises require, so the free tier is a trial path rather than a viable production option for regulated buyers.
- Dremio Cloud is AWS-first, which leaves Azure and Google Cloud customers on the self-managed path with the operational burden that entails.
- Query performance without Reflections on raw, poorly laid out files is often unremarkable, so the promise of querying the lake as is depends on file layout work you still have to do.
Qdrant
- Free tier extremely limited (0.5 vCPU, 1GB RAM, 4GB disk); suitable only for experiments
- Standard and Premium pricing usage-based; specific costs not published; requires calculator or quote
- Requires understanding of embeddings and vector search concepts; not suitable for SQL-only teams
- Early-stage serverless offering (coming soon) suggests maturity gaps in that deployment model
Pricing, plan by plan
Dremio
Free- Community EditionFree
- Self-managed on your own hardware
- SQL engine and semantic layer
- No vendor support
- Dremio Cloud$0.2/hour
- Billed at $0.20 per Dremio Compute Unit
- Includes query execution, Reflections and background processing
- 400 dollar trial credit for 30 days
- Enterprise$undefined/year
- Self-managed on Kubernetes, on premises or any cloud
- Enterprise security, SSO and governance
- Vendor support with SLA
Qdrant
Free- FreeFree
- Single-node cluster
- 0.5 vCPU
- 1GB RAM
- Standard$undefined/usage-based
- Dedicated resources
- Flexible scaling
- High availability
- Premium$undefined/minimum spend
- SSO and SAML
- Private VPC links
- 99.9% uptime SLA
Which should you pick?
Choose Dremio if
- You need arrow-based execution.
- You want to start without paying.
- You work on Linux, Kubernetes, Cloud, Docker.
- You also want reflections.
Choose Qdrant if
- You want to start without paying.
- You work on Cloud (AWS, GCP, Azure), Kubernetes, Self-hosted, Edge (beta), Serverless (coming).
Questions people ask
- Is Dremio or Qdrant better?
- Neither clearly leads. Dremio starts at Free and Qdrant at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Dremio or Qdrant?
- Dremio starts at Free and Qdrant at Free.
- Does Dremio or Qdrant run on more platforms?
- Dremio runs on Linux, Kubernetes, Cloud, Docker. Qdrant runs on Cloud (AWS, GCP, Azure), Kubernetes, Self-hosted, Edge (beta), Serverless (coming).
- Can I use Dremio for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Dremio best used for?
- Dremio is most often used for a company with petabytes of parquet in s3 that wants bi dashboards without duplicating it into a warehouse, a data platform team standardising on apache iceberg and needing a sql engine plus catalogue that does not lock the tables in, an analytics group accelerating slow lake queries with reflections instead of hand-built aggregate tables, a regulated enterprise that must keep data on premises but wants a modern lakehouse sql layer. Of those, a company with petabytes of parquet in s3 that wants bi dashboards without duplicating it into a warehouse and a data platform team standardising on apache iceberg and needing a sql engine plus catalogue that does not lock the tables in are not what Qdrant is typically brought in for.
- What can Dremio do that Qdrant cannot?
- Dremio covers Arrow-based execution, Reflections, Semantic layer, Iceberg catalogue.
Answered from the vendors’ own pages
Dremio: How is Dremio Cloud billed?
At 0.20 US dollars per Dremio Compute Unit, which counts query execution, Reflection building and platform overhead, not just user queries.
Qdrant: Can I use Qdrant for free?
Yes, Qdrant offers a free tier that provides a single node cluster with 0.5 vCPU, 1GB RAM, and 4GB disk space. It includes free cloud inference with selected models and is described as free forever for testing and prototypes.
SourceDremio: Is there a free version?
Yes, a Community Edition you self-manage, but it omits the enterprise security and governance features and comes with no support.
Qdrant: How is Qdrant Cloud billing calculated?
Billing is calculated based on actual resource usage during each billing period. You are charged hourly for compute (vCPU), memory (GB), storage (GB), backups, and any paid inference tokens used.
SourceDremio: Does it lock in my data?
No, tables stay in Apache Iceberg or Parquet in your own object storage and can be read by Spark, Trino or other engines.
Qdrant: What happens if I scale beyond the free tier?
The Standard Tier uses the same usage-based billing model as the free tier but adds features like dedicated resources with flexible scaling, highly available setups with backup and disaster recovery, and a 99.5% uptime SLA.
SourceDremio: Do I still need a warehouse?
Often not for analytics, but Dremio is not a transactional store and high-concurrency operational serving is not its strength.
Qdrant: What SLA does Qdrant offer?
The Standard Tier provides 99.5% uptime SLA. The Premium Tier, which requires a minimum spend for enterprises, offers 99.9% uptime SLA along with single sign-on and private VPC links.
SourceQdrant: Are there dedicated cloud infrastructure options?
Yes, Qdrant offers Hybrid Cloud (runs on your infrastructure) and Private Cloud (complete isolation) options, both with custom pricing that requires contacting the sales team.
SourceRelated pages
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