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Databases · head to head

Apache Hadoop vs Qdrant

Apache Hadoop logo

Apache Hadoop

Databases

The original open source framework for distributed storage and batch processing on commodity servers, now largely a legacy platform.

From
Free
Free plan
Yes
Visit Apache HadoopFull review
Qdrant logo

Qdrant

Databases

High-performance vector database for similarity search and embedding-based retrieval

From
Free
Free plan
Yes
Visit QdrantFull review

The short version

  • Qdrant is an open-source vector search engine.
  • Both are free.
  • Where Qdrant falls short: free tier extremely limited (0.5 vCPU, 1GB RAM, 4GB disk); suitable only for experiments.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

Only the attributes on which Apache Hadoop and Qdrant actually diverge.

Attributes where Apache Hadoop and Qdrant differ
AttributeApache HadoopQdrant
Pricing modelOpen-sourceFreemium
PlatformsWebWeb, Self-hosted, Browser extension, Serverless (coming)

Identical on both: starting price (Free), free tier (Yes), 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 Apache Hadoop

  • HDFS
  • YARN
  • MapReduce
  • HDFS federation and high availability
  • Kerberos security
  • Rack awareness
  • S3A and object store connectors
  • Ecosystem compatibility

Only in Qdrant

Nothing recorded that Apache Hadoop does not also cover.

What people use each for

The jobs each tool is most often brought in to do.

Apache Hadoop

  • Operating an existing multi-petabyte on-premises estate where data residency or egress costs rule out moving to cloud object storagenot Qdrant
  • Running Spark or Flink under YARN on hardware you already own, using HDFS as the storage layernot Qdrant
  • Keeping long-lived regulated archives on infrastructure entirely within your own data centres and legal jurisdictionnot Qdrant
  • Maintaining legacy Hive and MapReduce workloads during a staged migration to a lakehouse or cloud platformnot Qdrant

Qdrant

  • Retrieval-augmented generation (RAG) backends for LLM applicationsnot Apache Hadoop
  • Semantic search across large document corporanot Apache Hadoop
  • Multimodal retrieval (text, images, video) for recommendation systemsnot Apache Hadoop
  • Similarity-based product or content recommendationsnot Apache Hadoop
  • Real-time vector indexing for streaming embedding datanot Apache Hadoop

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Apache Hadoop

  • The free vendor distributions no longer exist: Cloudera's CDH and Hortonworks' HDP have reached end of support and the successor CDP is subscription-only, so running Hadoop without paying now means assembling, testing and security-patching Apache releases yourself
  • HDFS couples storage to compute, so adding capacity means buying whole nodes with CPU and memory you may not need, and the entire industry moved to object storage precisely because it lets the two be bought separately
  • The NameNode holds all filesystem metadata in memory, so a cluster with tens of millions of small files exhausts heap long before it exhausts disk, and the remedy is a file compaction job that somebody has to write, schedule and own indefinitely
  • Operating it is a distinct specialism covering Kerberos, YARN queue tuning, JVM garbage collection and the compatibility matrix between Hive, HBase, Ranger, Oozie and the core, and an upgrade touches all of them at once rather than one at a time
  • MapReduce is maintained for compatibility rather than actively developed, and new work goes to Spark or Flink, so a job written against MapReduce today is written against an API that will not gain anything further
  • Hiring is against you: the talent pool has moved to cloud data platforms over the past decade, so a Hadoop estate increasingly depends on a small number of individuals, which makes it a succession risk before it is a technical one

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

Apache Hadoop

Free

No published plan breakdown. See the Apache Hadoop review.

Qdrant

Free
  • FreeFree
    • Single-node cluster
    • 0.5 vCPU
    • 1GB RAM
  • StandardOn request
    • Dedicated resources
    • Flexible scaling
    • High availability
  • PremiumOn request
    • SSO and SAML
    • Private VPC links
    • 99.9% uptime SLA

Which should you pick?

Choose Apache Hadoop if

  • You need hdfs.
  • You want to start without paying.
  • You also want yarn.
Apache Hadoop review

Choose Qdrant if

  • You want to start without paying.
  • You work on Web, Self-hosted, Browser extension, Serverless (coming).
Qdrant review

Questions people ask

Is Apache Hadoop or Qdrant better?
Neither clearly leads. Apache Hadoop 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, Apache Hadoop or Qdrant?
Apache Hadoop starts at Free and Qdrant at Free.
Does Apache Hadoop or Qdrant run on more platforms?
Apache Hadoop runs on Web. Qdrant runs on Web, Self-hosted, Browser extension, Serverless (coming).
Can I use Apache Hadoop for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Hadoop best used for?
Apache Hadoop is most often used for operating an existing multi-petabyte on-premises estate where data residency or egress costs rule out moving to cloud object storage, running spark or flink under yarn on hardware you already own, using hdfs as the storage layer, keeping long-lived regulated archives on infrastructure entirely within your own data centres and legal jurisdiction, maintaining legacy hive and mapreduce workloads during a staged migration to a lakehouse or cloud platform. Of those, operating an existing multi-petabyte on-premises estate where data residency or egress costs rule out moving to cloud object storage and running spark or flink under yarn on hardware you already own, using hdfs as the storage layer are not what Qdrant is typically brought in for.
What can Apache Hadoop do that Qdrant cannot?
Apache Hadoop covers HDFS, YARN, MapReduce, HDFS federation and high availability.

Answered from the vendors’ own pages

Apache Hadoop: Is Hadoop dead?

No, but it is legacy. Large on-premises HDFS estates still run and are still supported, and Spark and Flink still run on YARN. What has ended is Hadoop as a default choice for new platforms, which now start on object storage.

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.

Source
Apache Hadoop: Can I still get a free packaged distribution?

Not a maintained one. CDH and HDP reached end of support and Cloudera's CDP is a paid subscription. The remaining free route is building and patching Apache releases yourself, which is a real engineering commitment.

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.

Source
Apache Hadoop: Do I need Hadoop to run Spark?

No. Spark runs standalone, on Kubernetes and on managed cloud services, and reads object storage directly. Many Spark deployments include Hadoop client libraries for the filesystem connectors without running a Hadoop cluster at all.

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.

Source
Apache Hadoop: What replaced HDFS?

Object storage, typically S3 or a compatible system, combined with an open table format such as Apache Iceberg or Delta Lake. Apache Ozone exists as an object store within the Hadoop ecosystem for organisations staying on-premises.

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.

Source
Apache Hadoop: Is it cheaper than the cloud?

It can be at multi-petabyte scale with steady, predictable utilisation, particularly where egress charges would be large. Include the staffing cost honestly, because the specialist operators a Hadoop cluster requires are scarce and therefore expensive.

Qdrant: 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.

Source
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