Technology · head to head
etcd vs Greenhouse
etcd
Technology
A distributed key-value store using Raft consensus, built for cluster coordination rather than application data.
- From
- On request
- Rated
- -
The short version
- Each has a real cost: etcd it is sized for coordination data, not application data: the default backend quota is 2 GB and 8 GB is the documented recommended maximum, and exceeding it puts the cluster into a NOSPACE alarm where it accepts no writes until an operator compacts, defragments and clears the alarm by hand.; Greenhouse core plan lacks talent discovery and contact lookups
- They diverge on capability: etcd covers Raft consensus, Greenhouse covers Applicant tracking.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which etcd and Greenhouse actually diverge.
| Attribute | etcd | Greenhouse |
|---|---|---|
| Pricing model | open-source | quote |
| Platforms | Web | Web, Ios, Android, Api |
| Founded | Unknown | 2012 |
Identical on both: starting price (On request), free tier (No), user rating (Not yet rated), category (Technology).
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 etcd
- Raft consensus
- Linearizable reads
- Transactions
- Leases
- Watches
- MVCC revision history
- Role-based access control
- Snapshot backup and restore
Only in Greenhouse
- Applicant tracking
- Interview scheduling
- Scorecard system
- Job board posting
- Candidate CRM
- Reporting & analytics
- Offer management
- EEO compliance
What people use each for
The jobs each tool is most often brought in to do.
etcd
- Storing Kubernetes cluster state, which is what the overwhelming majority of etcd deployments are doingnot Greenhouse
- Leader election and distributed locking in a home-grown scheduler or control plane, using leases and transactionsnot Greenhouse
- Service discovery and dynamic configuration where readers need to be notified of changes rather than poll for themnot Greenhouse
- Coordinating failover in a clustered database, as Patroni does for PostgreSQLnot Greenhouse
Greenhouse
- Enterprise hiring and recruiting automationnot etcd
- Multi-location and multi-team talent acquisitionnot etcd
- Structured interview process managementnot etcd
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
etcd
- It is sized for coordination data, not application data: the default backend quota is 2 GB and 8 GB is the documented recommended maximum, and exceeding it puts the cluster into a NOSPACE alarm where it accepts no writes until an operator compacts, defragments and clears the alarm by hand.
- Every write is replicated and fsynced before acknowledgement, so cluster performance is bounded by the slowest disk in it; a member on network-attached storage with high fsync latency causes leader elections and cluster-wide latency spikes that look like network problems and are not.
- Adding members increases availability but reduces write throughput, because each write must reach a larger quorum; you run three or five members for fault tolerance, and increasing capacity means faster hardware rather than more nodes.
- There is no sharding and no multi-tenancy, so isolating workloads means running separate clusters, each with its own quorum, certificates, backup schedule and upgrade path, and that operational multiplication is often unexpected.
- Running it yourself is a real job: periodic compaction and defragmentation, snapshot backups you have actually rehearsed restoring, and rotation of both peer and client TLS certificates, none of which happens automatically outside a managed Kubernetes service.
- Losing quorum is not self-healing; recovering a cluster that has lost a majority means restoring from a snapshot and accepting that everything written since that snapshot is gone, which makes backup frequency a data-loss budget decision rather than a routine setting.
Greenhouse
- Core plan lacks talent discovery and contact lookups
- Core plan lacks email automation and applicant texting
- Plus plan lacks resume anonymisation and application limits
- Plus plan lacks audit logging and developer tools
- Pricing customised by hiring volume and company size, not published
- Only Pro tier offers audit logs and developer sandbox
Pricing, plan by plan
etcd
On requestNo published plan breakdown. See the etcd review.
Greenhouse
On requestNo published plan breakdown. See the Greenhouse review.
Which should you pick?
Choose Greenhouse if
- You need applicant tracking.
- You work on Web, Ios, Android, Api.
- You also want interview scheduling.
Questions people ask
- Is etcd or Greenhouse better?
- Neither clearly leads. etcd starts at On request and Greenhouse at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, etcd or Greenhouse?
- etcd starts at On request and Greenhouse at On request.
- Does etcd or Greenhouse run on more platforms?
- etcd runs on Web. Greenhouse runs on Web, Ios, Android, Api.
- What is etcd best used for?
- etcd is most often used for storing kubernetes cluster state, which is what the overwhelming majority of etcd deployments are doing, leader election and distributed locking in a home-grown scheduler or control plane, using leases and transactions, service discovery and dynamic configuration where readers need to be notified of changes rather than poll for them, coordinating failover in a clustered database, as patroni does for postgresql. Of those, storing kubernetes cluster state, which is what the overwhelming majority of etcd deployments are doing and leader election and distributed locking in a home-grown scheduler or control plane, using leases and transactions are not what Greenhouse is typically brought in for.
- What can etcd do that Greenhouse cannot?
- etcd covers Raft consensus, Linearizable reads, Transactions, Leases. Greenhouse covers Applicant tracking, Interview scheduling, Scorecard system, Job board posting.
Answered from the vendors’ own pages
etcd: Can I use etcd as an application database?
No. It is designed for metadata and coordination, with a recommended maximum store size of around 8 GB, no sharding and a write path deliberately optimised for durability rather than throughput. Application data belongs in a database built for it.
Greenhouse: How much does Greenhouse cost?
Greenhouse does not publish specific pricing. The company states that pricing is customized based on your hiring needs, hiring volume, organizational complexity, and required features.
Sourceetcd: How many members should a cluster have?
Three for most cases, five where you need to survive two simultaneous failures. Always an odd number, because an even-sized cluster gains no additional fault tolerance while making quorum harder to reach.
Greenhouse: What are the Greenhouse pricing tiers?
Greenhouse offers three plan levels: Core (basic hiring structure), Plus (multi-location optimization), and Pro (complex enterprise hiring). Exact pricing requires contacting Greenhouse for a custom quote.
Sourceetcd: What happens when the store fills up?
The cluster raises a NOSPACE alarm and stops accepting writes, becoming effectively read-only. Recovery requires compacting old revisions, defragmenting each member and then explicitly disarming the alarm, all done by an operator.
Greenhouse: Do all Greenhouse tiers include the same features?
No. Core includes sourcing and scheduling, Plus adds automation and texting, and Pro adds enterprise data configuration and audit logs. All tiers can be customized.
Sourceetcd: Why is my etcd cluster slow or unstable?
Almost always disk latency. Because every write is fsynced before acknowledgement, slow or shared storage causes heartbeat timeouts, leader elections and cascading latency. Local SSDs with low fsync latency are effectively a requirement.
etcd: How does it compare with Consul or ZooKeeper?
All three provide consensus-backed coordination. etcd has the simplest data model and the Kubernetes ecosystem behind it; Consul bundles service discovery, health checking and a service mesh; ZooKeeper is older, JVM-based and still common under Kafka and Hadoop-era systems.
Related pages
Other head to heads
- etcd vs Linear
- etcd vs Asana
- etcd vs ClickUp
- etcd vs Figma
- etcd vs Istio
- etcd vs MongoDB
- etcd vs Finxact
- etcd vs Sentry
- etcd vs Trino
- etcd vs Jenkins
- etcd vs Apache Hadoop
- etcd vs Height
- etcd vs Raycast
- etcd vs Superhuman
- etcd vs Vim
- etcd vs Whimsical
- etcd vs Apache Spark
- etcd vs Aha!
- etcd vs Okta
- etcd vs Eclipse
- etcd vs Postgres
- etcd vs Amplitude
- etcd vs Mixpanel
- etcd vs Pendo
- etcd vs PostHog
- etcd vs Postman
- Greenhouse vs Linear
- Greenhouse vs Asana
- Greenhouse vs ClickUp
- Greenhouse vs Figma
- Greenhouse vs Istio
- Greenhouse vs MongoDB
- Greenhouse vs Finxact
- Greenhouse vs Sentry
- Greenhouse vs Trino
- Greenhouse vs Jenkins
- Greenhouse vs Apache Hadoop
- Greenhouse vs Height
- Greenhouse vs Raycast
- Greenhouse vs Superhuman
- Greenhouse vs Vim
- Greenhouse vs Whimsical
- Greenhouse vs Apache Spark
- Greenhouse vs Aha!
- Greenhouse vs Okta
- Greenhouse vs Eclipse
- Greenhouse vs Postgres
- Greenhouse vs Amplitude
- Greenhouse vs Mixpanel
- Greenhouse vs Pendo
- Greenhouse vs PostHog
- Greenhouse vs Postman