Topics / #autoscaling

Autoscaling projects on GitHub

Automatically scaling workloads based on demand and metrics

As of , the most-starred Autoscaling projects on GitHub are neon (23.3K stars), keda (10.7K stars) and substrate (5.8K stars). GitHub.Locker tracks 5 Autoscaling repositories with 45.1K stars combined. The fastest-growing on October 25 was substrate, with +36 stars in a day.

5Projects
45.1KStars in total
+59Stars in a day
Browse in the catalog →github.com/topics/autoscaling ↗

Hot right now

Most stars gained in a day · Oct 25

+36

substrate

agent-substrate/substrate · ★ 5.8K

A secure-by-default runtime that hosts AI agents in isolated sandboxes — microVMs or gVisor containers — and runs millions of them on shared infrastructure, with much higher density than standard container runtimes.

+15

llm-d

llm-d/llm-d · ★ 5K

llm-d is a high-performance distributed inference serving stack for running large language models on Kubernetes, aiming to get state-of-the-art inference performance on any hardware accelerator.

+4

neon

neondatabase/neon · ★ 23.3K

Neon is an open source serverless Postgres platform. It splits storage from compute and replaces the PostgreSQL storage layer with a distributed engine that spreads data across a cluster of nodes — which is what enables autoscaling, database branching, and scale to zero.

+4

keda

kedacore/keda · ★ 10.7K

KEDA is a Kubernetes-based event driven autoscaling component — it automatically scales any container running in a Kubernetes cluster based on events.

The all-time top

5 projects · by stars

  1. #1 · neondatabase/neon

    neon

    Neon is an open source serverless Postgres platform. It splits storage from compute and replaces the PostgreSQL storage layer with a distributed engine that spreads data across a cluster of nodes — which is what enables autoscaling, database branching, and scale to zero.

    23.3K
  2. #2 · kedacore/keda

    keda

    KEDA is a Kubernetes-based event driven autoscaling component — it automatically scales any container running in a Kubernetes cluster based on events.

    10.7K
  3. #3 · agent-substrate/substrate

    substrate

    A secure-by-default runtime that hosts AI agents in isolated sandboxes — microVMs or gVisor containers — and runs millions of them on shared infrastructure, with much higher density than standard container runtimes.

    5.8K
  1. 4

    llm-d

    5Kllm-d/llm-d · Python

    llm-d is a high-performance distributed inference serving stack for running large language models on Kubernetes, aiming to get state-of-the-art inference performance on any hardware accelerator.

  2. 5

    privacy-preserving-control-plane

    418gbenletoluwase/privacy-preserving-control-plane · Java

    A Java control plane that bundles four coupled mechanisms for production ML pipelines: forecast-driven autoscaling, zero-shot PII redaction at the point of processing, governed model promotion, and drift/anomaly detection.

Often tagged together