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The #1 Time-Series Object Store for Edge Computing

Outperform TimescaleDB, MongoDB, and MinIO with a tailored solution for time-series object data. Why not try it for free today?

Simplify Your Infrastructure

Merge blob and time series functionalities, reducing the need for multiple databases.

Stay In Control Of Your Data

Customize real-time data retention policies and replication strategies.

Handle Large Data Volumes

Store billions of time-stamped blobs with AI labels and access them with low latency.

Get the Best Performance

Outperform other databases with a customized solution for time-series object data.

  • vs TimescaleDB
  • vs MongoDB
  • vs MinIO
Record SizeRead Speed (%)Write Speed (%)
10 MB+850%+1300%
1 MB+855%+1075%
100 KB+217%+205%

See the full TimescaleDB vs ReductStore benchmark.


PANDA GmbH logo
"With ReductStore's approach to data retention, we have forgotten about the disk overrun problems on our edge devices."
Ingo Kaiser
CEO and Co-founder at PANDA GmbH
Mounte AB logo
"The main reason for choosing ReductStore was that it was quick and easy to deploy, use and integrate. This allowed us to have a working system up and running and ingesting data within a day."
Daniel Wedlund
Founder at Mounte AB
Metric Space UG logo
"ReductStore is a vital part of our infrastructure. It handles terabytes of unstructured data in a production environment."
Michael Welsh
Founder at Metric Space UG

Time Series Blob Storage

Capture and access blob data as time series, tailored for edge computing, computer vision, and IoT.

No Size Limit for Blobs

ReductStore handles blob data without size limits; your disk capacity is the only boundary.

Real-Time FIFO Quota

Ensure optimal storage management with FIFO quotas, preventing disk space shortages in real-time.

Data Labeling & Filtering

Manage your time-series blob data with ease: annotate, filter, and save AI labels or meta-data.

Advanced HTTP(S) API

Integrate and communicate with ReductStore using our feature-rich and secure API.

Efficient Data Batching

Minimize network overhead in areas with high latency by fetching records in batched HTTP responses.

Data Replication

Synchronize data across buckets with replication for high availability and disaster recovery.

Iterative Data Querying

Efficiently queries large datasets with minimal load for real-time and historical data processing.

Token Authorization

Secure data access with token-based authorization to protect your data from unauthorized access.


  • Python
  • JavaScript
  • C++
  • Rust
  • cURL
import time
import asyncio
from reduct import Client, Bucket

async def main():
client = Client('http://127.0.0.1:8383')
bucket: Bucket = await client.create_bucket("my-bucket", exist_ok=True)

ts = time.time_ns() / 1000
await bucket.write("entry-1", b"Hey!!", ts)
async with bucket.read("entry-1", ts) as record:
data = await record.read_all()
print(data)

loop = asyncio.get_event_loop()
loop.run_until_complete(main())

Client SDKs

You can use the following client SDKs for quick and easy integration into your applications and infrastructure:


Web Console

ReductStore has an integrated web console that allows you to easily manage your data and access to it.

ReductStore Web Console

Mobile View

CLI Client

You can customize data retention and replication policies using the ReductStore CLI client.


Frequently Asked Questions

How does the database scale, and what are the infrastructure requirements for scaling up?

ReductStore is built for scalability, designed to efficiently manage large data volumes typical in edge computing and AI/ML workflows. It combines blob and time-series data functionalities, enabling the system to handle billions of time-stamped blobs. Scaling up primarily involves expanding disk capacity. The system's architecture supports low-latency access with iterator and range query capabilities, ensuring optimal performance at scale. More information about the architecture can be found in the How Does It Work section of the documentation.

Are there any customization options for data retention and replication policies?

Yes, ReductStore includes several customizable features for managing data, one of which is the First In, First Out (FIFO) approach. This method is specifically designed to handle data based on volume intervals, ensuring that as new entries are added, older ones are systematically removed to prevent disk space issues. Additionally, AI labels can be attached to blobs as metadata, helping in the identification and replication of key data, thus ensuring vital information is retained. These functionalities are all accessible through the ReductStore CLI, making the management of data retention and replication straightforward and effective.

Is it compatible with popular programming languages and data formats?

Yes, ReductStore ensures compatibility with a variety of programming languages via its HTTP(S) API. Being a blob storage system, it supports the storage of all types of unstructured data in byte form, making it straightforward to incorporate into your existing setups and manage a wide range of data.

How does the database ensure data compliance, especially for sensitive or regulated industries?
How does the licensing work, and are there different tiers or plans available?
What kind of support is available for developers and administrators?

AI on the Edge? Download our White Paper

Learn more about ReductStore and how it can help you simplify your data infrastructure and AI/ML workflows.

White Paper