vs

Fjall vs SlateDB

Fjall: Log-structured, embeddable key-value engine in pure Rust with keyspaces and transactions. SlateDB: Embedded LSM storage engine that writes straight to object storage like S3.

key differences

  • SlateDB is built for larger datasets (≤ 1 PB) than Fjall (≤ 10 TB).
  • Only SlateDB has ttl / expiry, time travel.

choose fjall for

  • +Write-heavy storage inside Rust services
  • +Embedded time-series or log storage
  • +Replacing RocksDB bindings in Rust code

best at · Embedded, Time-series, Metadata

choose slatedb for

  • +State backends for stream processors and serverless compute
  • +Metadata stores for object-storage-native systems
  • +Durable logs and queues on S3
  • +Embedding a KV engine in diskless containers

best at · Embedded, Streaming, Metadata

overview
Primary model
Key-Value
Key-Value
Also supports
–
–
Type
Embedded library
Embedded library
Status
Active
Active
Developer
Marvin Jäger and contributors
SlateDB contributors (Commonhaus Foundation)
First release
2023
2024
Latest version
3.1.13 (Oct 25, 2026)
0.17.0 (Sep 29, 2026)
License
Open source (permissive)MIT or Apache License 2.0
Open source (permissive)Apache License 2.0
Free tier
✓
✓
popularity
WhichDB Score
15.4
19.7
Rank
#215
#163
GitHub stars
2.4K
3.6K
Stack Overflow questions
–
–
scale & guarantees
Comfortable data size
Medium (≤ 10 TB)
Large (≤ 1 PB)
Horizontal scaling
None
None
Multi-region
None
None
ACID
Full
Full
Consistency
Strong
Strong
Schema
Schemaless
Schemaless
Architecture
Embedded
Embedded, Shared storage
deployment
Self-hosted
✓
✓
Vendor-managed cloud
—
—
Serverless
—
—
Embedded
✓
✓
Edge
—
—
Clouds
–
–
capabilities
Transactions
✓
✓
Joins
—
—
Secondary indexes
—
—
Full-text search
—
—
Vector search
—
—
Geospatial
—
—
JSON
—
—
Change data capture
—
—
Triggers
—
—
Stored procedures
—
—
Materialized views
—
—
TTL / expiry
—
✓
Pub/Sub
—
—
Columnar storage
—
—
In-memory
—
—
Durable persistence
✓
✓
Compression
✓
✓
Encryption at rest
—
—
Role-based access
—
—
Time travel
—
✓
Branching
—
—
Online schema changes
—
—
Streaming ingest
—
—
Offline sync
—
—
Runs in WebAssembly
—
—
interfaces
Query languages
Rust API
Rust API
Client languages
Rust
Rust, Go, Java, JavaScript, Python
Best for
Embedded, Time-series, Metadata
Embedded, Streaming, Metadata

Fjall vs SlateDB: FAQ

What is the difference between Fjall and SlateDB?

Fjall: Log-structured, embeddable key-value engine in pure Rust with keyspaces and transactions. SlateDB: Embedded LSM storage engine that writes straight to object storage like S3. SlateDB is built for larger datasets (≤ 1 PB) than Fjall (≤ 10 TB). Only SlateDB has ttl / expiry, time travel.

When should I choose Fjall over SlateDB?

Choose Fjall for write-heavy storage inside Rust services, embedded time-series or log storage and replacing RocksDB bindings in Rust code. Choose SlateDB for state backends for stream processors and serverless compute; metadata stores for object-storage-native systems; and durable logs and queues on S3.

Is Fjall or SlateDB more popular?

SlateDB ranks #163 on the WhichDB Score (19.7/100) and Fjall ranks #215 (15.4/100). Popularity is not fit: check the requirements that matter for your project.

Are Fjall and SlateDB open source?

Yes. Fjall is open source. License: MIT or Apache License 2.0. Yes. SlateDB is open source. License: Apache License 2.0.