Technology

Tigris Data

An S3-compatible, globally distributed object storage platform engineered to optimize small-object workloads through metadata inlining, adjacent key coalescing, and LSM-backed caching, delivering sub-10ms read/write latency for KB-sized payloads.

4 connections 2 resources

Summary

What it is

An S3-compatible, globally distributed object storage platform engineered to optimize small-object workloads through metadata inlining, adjacent key coalescing, and LSM-backed caching, delivering sub-10ms read/write latency for KB-sized payloads.

Where it fits

An alternative S3-compatible provider positioned between hyperscaler general-purpose storage and specialized caching layers. Addresses the latency and API cost penalty that standard S3 imposes on workloads dominated by millions of small objects such as log aggregations, ML feature stores, and IoT telemetry.

Misconceptions / Traps
  • Not a CDN or cache — it is a durable object store with full S3 API compatibility.
  • Small-object optimization does not mean it sacrifices large-object throughput; the architecture handles both.
  • Global distribution does not imply eventual consistency for all operations — write consistency is maintained per-region.
Key Connections
  • implements S3 API — full S3 compatibility for existing tooling
  • solves Small Files Problem — metadata inlining bypasses per-object storage overhead
  • solves Request Amplification — key coalescing reduces API call volume for adjacent objects

Definition

What it is

An S3-compatible, globally distributed object storage platform engineered for small-object workloads. Uses metadata inlining, adjacent key coalescing, and LSM-backed caching to deliver sub-10ms read/write latency for kilobyte-sized payloads.

Why it exists

Standard S3 imposes severe latency and API cost penalties on workloads dominated by millions of small objects (logs, ML feature stores, IoT telemetry). Every object incurs per-request overhead regardless of size. Tigris inlines small payloads directly into the metadata database layer, bypassing the per-object storage overhead entirely.

Primary use cases

Low-latency storage for ML feature stores, log aggregation pipelines, IoT telemetry ingestion, and any workload where object sizes are consistently below 1MB.

Connections 4

Outbound 4

Resources 2