Technology · head to head
etcd vs Sweep
etcd
Technology
A distributed key-value store using Raft consensus, built for cluster coordination rather than application data.
- From
- On request
- Free plan
- No
The short version
- Only Sweep has a free tier, so it costs nothing to try first.
- 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.; Sweep limited to JetBrains IDEs only, not available for VS Code or other editors
- They diverge on capability: etcd covers Raft consensus, Sweep covers Autocomplete.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which etcd and Sweep actually diverge.
Identical on both: user rating (Not yet rated).
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 Sweep
- Autocomplete
- AI Agent
- Full codebase indexing
- Code review functionality
- Multi-IDE support
- GitHub integration
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 Sweep
- Leader election and distributed locking in a home-grown scheduler or control plane, using leases and transactionsnot Sweep
- Service discovery and dynamic configuration where readers need to be notified of changes rather than poll for themnot Sweep
- Coordinating failover in a clustered database, as Patroni does for PostgreSQLnot Sweep
Sweep
- Accelerating code completion for JetBrains developersnot etcd
- Automating refactoring tasks across codebasesnot etcd
- Handling bug fixes with full repository contextnot etcd
- Code review and quality assurancenot 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.
Sweep
- Limited to JetBrains IDEs only, not available for VS Code or other editors
- Free tier limited to 1,000 autocompletes per month
- API credit consumption varies by feature usage
- Performance dependent on codebase size and indexing quality
Pricing, plan by plan
etcd
On requestNo published plan breakdown. See the etcd review.
Sweep
Free- FreeFree
- 1,000 autocompletes
- 5 USD API credits included
- All JetBrains IDEs supported
- Pro$20/month
- Unlimited autocomplete
- Higher API credit allocation
- Priority support
- Ultra$60/month
- Unlimited autocomplete
- Maximum API credit allocation
- Extended context window
Which should you pick?
Choose Sweep if
- You need autocomplete.
- You want to start without paying.
- You work on JetBrains IDEs.
- You also want ai agent.
Questions people ask
- Is etcd or Sweep better?
- Neither clearly leads. etcd starts at On request and Sweep at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, etcd or Sweep?
- Sweep has a free tier; the other does not. Paid plans start at On request for etcd and Free for Sweep.
- Does etcd or Sweep run on more platforms?
- etcd runs on Web. Sweep runs on JetBrains IDEs.
- Can I use Sweep for free?
- Yes. Sweep has a free tier, so you can try it without paying. etcd starts at On request.
- 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 Sweep is typically brought in for.
- What can etcd do that Sweep cannot?
- etcd covers Raft consensus, Linearizable reads, Transactions, Leases. Sweep covers Autocomplete, AI Agent, Full codebase indexing, Code review functionality.
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.
Sweep: What programming languages does Sweep support?
Sweep works with all programming languages supported by JetBrains IDEs, including Python, Java, JavaScript, Go, Kotlin, C++, and many others.
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.
Sweep: Does Sweep retain my code data?
No. Sweep is SOC 2 Type II certified with zero data retention. Code is processed for suggestions but not stored.
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.
Sweep: Can I cancel my Sweep subscription anytime?
Yes. You can cancel your subscription at any time from your account settings with no long-term commitments or cancellation fees.
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.
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