Softwr

Databases · head to head

Apache Hadoop vs Tinybird

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
Tinybird logo

Tinybird

Databases

Managed ClickHouse with a workflow that turns SQL queries into hosted HTTP APIs

From
Free
Free plan
Yes
Visit TinybirdFull review

The short version

  • Apache Hadoop is free; Tinybird starts at $25/mo.
  • Only Apache Hadoop lists HDFS federation and high availability; only Tinybird lists managed ClickHouse.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

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

Attributes where Apache Hadoop and Tinybird differ
AttributeApache HadoopTinybird
Pricing modelOpen-sourcePer month by compute and storage
PlatformsWebWeb, Linux, macOS

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 Tinybird

  • Managed ClickHouse
  • Pipes as APIs
  • Events HTTP endpoint
  • Streaming connectors
  • Materialized views
  • Git-based workflow
  • Token-scoped auth
  • Observability

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 Tinybird
  • Running Spark or Flink under YARN on hardware you already own, using HDFS as the storage layernot Tinybird
  • Keeping long-lived regulated archives on infrastructure entirely within your own data centres and legal jurisdictionnot Tinybird
  • Maintaining legacy Hive and MapReduce workloads during a staged migration to a lakehouse or cloud platformnot Tinybird

Tinybird

  • A SaaS product adding a per-customer usage dashboard that must render in under a second across billions of eventsnot Apache Hadoop
  • A team building rate limiting or fraud checks that need an aggregate over the last few minutes returned inside a request cyclenot Apache Hadoop
  • A data team offloading interactive operational dashboards from Snowflake, where per-query warehouse cost makes constant refresh untenablenot Apache Hadoop
  • A game or ad-tech company ingesting a high-volume event stream and exposing live counters back to customers through an APInot 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

Tinybird

  • It is ClickHouse underneath, so it inherits ClickHouse limits: multi-table joins degrade badly at scale, updates and deletes are expensive mutations rather than cheap operations, and a poorly chosen sorting key at table creation cannot be fixed without rebuilding the data
  • Compute is metered per vCPU-second with overage at 0.0002 USD per second, so an inefficient query shipped to production shows up directly on the invoice rather than merely running slowly
  • Only the Enterprise tier gets horizontal scaling and dedicated infrastructure; Free, Developer and SaaS all run on shared infrastructure with vertical scaling only, which caps both isolation and headroom for anyone not on a custom contract
  • Storage is billed at 0.058 USD per gigabyte on top of compute, and egress is charged separately at 0.01 USD per gigabyte intra-cloud and 0.10 USD inter-cloud, so a high-fanout API serving many small responses accrues costs in three places at once
  • You are building on a proprietary workflow around an open database: the pipes, tokens and API layer are Tinybird specific, so leaving means keeping your data but rewriting the entire serving layer you adopted Tinybird to avoid writing

Pricing, plan by plan

Apache Hadoop

Free

No published plan breakdown. See the Apache Hadoop review.

Tinybird

Free
  • FreeFree
    • 0.25 vCPU on shared infrastructure
    • 10 GB storage included
    • 1,000 requests per day
  • Developer$25/mo
    • 0.5 vCPU scaling to 8 vCPU
    • 25 GB storage included
    • Two replicas
  • SaaSOn request
    • Up to 32 vCPU
    • 500 GB storage included
    • Four to sixteen threads per request
  • EnterpriseOn request
    • Unlimited vCPU and bottomless storage
    • Dedicated infrastructure and private regions
    • Vertical and horizontal scaling

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 Tinybird if

  • You need managed clickhouse.
  • You want to start without paying.
  • You work on Web, Linux, macOS.
  • You also want pipes as apis.
Tinybird review

Questions people ask

Is Apache Hadoop or Tinybird better?
Neither clearly leads. Apache Hadoop starts at Free and Tinybird at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Hadoop or Tinybird?
Apache Hadoop starts at Free and Tinybird at Free.
Does Apache Hadoop or Tinybird run on more platforms?
Apache Hadoop runs on Web. Tinybird runs on Web, Linux, macOS.
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 Tinybird is typically brought in for.
What can Apache Hadoop do that Tinybird cannot?
Apache Hadoop covers HDFS, YARN, MapReduce, HDFS federation and high availability. Tinybird covers Managed ClickHouse, Pipes as APIs, Events HTTP endpoint, Streaming connectors.

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.

Tinybird: Is Tinybird just hosted ClickHouse?

No. The database is ClickHouse, but the product is the layer above it: publishing parameterised SQL as authenticated, rate-limited REST endpoints without writing an API server.

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.

Tinybird: What does it actually cost?

Free tier at 1,000 requests a day, Developer from 25 USD a month, then compute at 0.0002 USD per vCPU-second and storage at 0.058 USD per gigabyte. Higher tiers are quoted.

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.

Tinybird: Can I run it on my own infrastructure?

Only on Enterprise, which offers dedicated infrastructure and private regions. Lower tiers are shared multi-tenant cloud.

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.

Tinybird: Does it handle updates and deletes?

Poorly, as ClickHouse does. Design for append-only event data; frequent mutation is the wrong workload for this engine.

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.

Share

Related pages

More on Apache Hadoop

More on Tinybird

Keep looking

Other head to heads