Open-Source Databases · slatedb

slatedb

SlateDB is an embedded key-value storage engine written in Rust that stores data in cloud object storage (S3, GCS, etc.) instead of local disk. It uses log-structured merge-tree (LSM) techniques with batching, caching, and compression to balance cloud storage's high latency and API costs against traditional database performance.

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Source: GitHub — github.com/slatedb/slatedb

3.2k

GitHub stars

260

Forks

Rust

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	slatedb/slatedb
Owner	slatedb
Primary language	Rust
License	Apache-2.0 — OSI-approved
Stars	3.2k
Forks	260
Open issues	189
Latest release	v0.14.1 (2026-07-01)
Last updated	2026-07-08
Source	https://github.com/slatedb/slatedb

What slatedb is

SlateDB implements an LSM-tree architecture backed by object storage, mitigating write costs through MemTable batching and read costs through block caches, bloom filters, and local disk caches. It supports range queries, transactions, merge operators, change data capture, and official bindings for Go, Java, Node.js, Python, and .NET.

Quickstart

Get the slatedb source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/slatedb/slatedb.git2cd slatedb3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Embedded storage in cloud-native applications

Ideal for applications that need local-like embedded database behavior but want durability and replication through cloud object storage, eliminating local disk management and enabling horizontal scaling.

High-durability, low-maintenance data persistence

Leverages cloud object storage's inherent durability and replication, reducing operational overhead compared to managing local storage replicas or traditional database infrastructure.

Cost-optimized storage for variable workloads

Write batching and caching strategies reduce API costs when paired with cost-efficient object storage. Suitable for workloads where object storage access patterns can be optimized through configuration.

Implementation considerations

Flush interval and block cache sizing are critical tuning parameters for balancing latency, API cost, and memory footprint; requires load testing against target object storage backend.
Object storage backend selection (S3, GCS, MinIO, Tigris, etc.) affects latency and cost; verify compatibility and test region/endpoint configuration early.
Local disk cache setup is optional but recommended for read performance; plan disk space for SST caching and configure cache eviction policies.
No API stability guarantee between versions; plan version upgrade cycles and test storage format compatibility (guaranteed between adjacent versions only).
Async Rust runtime required (tokio shown in examples); integrating into sync codebases requires bridge patterns or dedicated async executor threads.

When to avoid it — and what to weigh

Real-time latency-critical workloads — Object storage introduces inherent latency compared to local disk or in-memory databases. Applications requiring sub-millisecond response times should use in-memory or local-SSD solutions instead.
High-frequency write workloads without tolerance for async durability — While put_with_options allows non-durable writes for lower latency, applications requiring synchronous durability on every single write may incur high object storage API costs and see latency penalties.
Mature, production-hardened API requirements — Project is at v0.14.1 with no API compatibility guarantees between versions (semantic versioning for storage format only). Breaking changes to compile-time APIs are explicitly reserved.
Offline-first or zero-connectivity scenarios — SlateDB depends on continuous connectivity to object storage. Applications that must work offline or in disconnected modes require different architectural approaches.

License & commercial use

SlateDB is licensed under Apache License 2.0 (Apache-2.0), a permissive OSI-approved license that allows commercial use, modification, and distribution with clear attribution and liability disclaimers.

Apache-2.0 permits commercial use, derivative works, and distribution. No explicit restrictions on proprietary applications, though modifications must be documented. Recommend legal review of your specific deployment model, especially if packaging and distributing SlateDB as part of a commercial product, to ensure compliance with Apache-2.0 conditions (notice, license copy, statement of changes).

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Good
Assessment confidence	High

Security considerations

Security posture is not clearly documented in provided data. Considerations for evaluation: object storage authentication and encryption must be handled by the underlying ObjectStore implementation; SlateDB does not appear to provide application-level encryption. In-memory structures (MemTables, block caches) hold unencrypted data; ensure host-level memory protection and access controls. Input validation, timing attack resistance, and cryptographic hardening are unknown; requires code review or security audit if handling sensitive data.

Alternatives to consider

RocksDB

Mature, widely-adopted LSM-tree engine with local disk backing, better latency, and stable API. Choose if you need synchronous durability, lower latency, or traditional local storage is acceptable.

DynamoDB or managed cloud databases

Fully managed, zero-ops durability via cloud provider. Choose if you want to avoid infrastructure management entirely, though higher per-operation costs and vendor lock-in.

SQLite or libSQL

Lightweight, serverless SQL database. Choose if you need SQL query support, ACID transactions, or simpler embedded footprint; trades object storage durability for local-only persistence.

Software development agency

Build on slatedb with DEV.co software developers

Our engineering team can help you prototype, benchmark, and architect SlateDB integration into your application. We'll validate object storage costs, tune cache and flush parameters, and ensure architectural fit.

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slatedb FAQ

How much does SlateDB cost to run?

SlateDB itself is open-source and free. Costs come from your object storage backend (S3, GCS, etc.); write batching and caching are designed to minimize API calls, but ultimate cost depends on workload volume, backend pricing, and configuration tuning.

Can I migrate from RocksDB to SlateDB?

Not directly; SlateDB is a different engine with different storage formats. Migration requires exporting data from RocksDB and importing into SlateDB, with application code changes to use SlateDB's async API.

What happens if my object storage goes down?

Reads and writes from the in-memory MemTable will continue until flush is required; once data must be flushed to object storage, operations will fail. SlateDB does not provide local fallback or resilience to prolonged object storage unavailability.

Is SlateDB suitable for a multi-tenant SaaS application?

Possible, but not clearly documented. You would need to manage separate Db instances per tenant or implement custom isolation logic. No explicit multi-tenancy guidance is visible; recommend prototype and architecture review.

Custom software development services

Adopting slatedb is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate open-source databases software in production.

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