# nuclio/nuclio

High-Performance Serverless event and data processing platform

Repository: https://github.com/nuclio/nuclio
Canonical: https://ross.abutalabs.com/products/nuclio
Homepage: https://nuclio.io
Language: Go
License: Apache-2.0
License Family: permissive
Topics: serverless, kubernetes, containers, go, docker, functions, faas, faas-platform
Last push: 2026-08-26T16:55:37+00:00

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 87, longevity 100
- inputs: {"age_days": 3393, "days_push": 7, "days_rel": 7, "gap_med": 7.0, "n_releases_24m": 77}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5750, forks 567 (observed 2026-08-28T04:09:29.011298+00:00)

## What it is
Nuclio is a high-performance open-source serverless (FaaS) platform for real-time event and data processing, deployable standalone via Docker or on Kubernetes. It supports CPU and GPU workloads, integrates with data science tools like Jupyter and Kubeflow, and handles hundreds of thousands of function invocations per second.

## Use cases
- deploy serverless functions on kubernetes
- process real-time event and IoT sensor streams
- run AI inference as serverless functions with GPU support
- build data transformation and movement pipelines
- deploy functions from jupyter notebooks
- run a self-hosted FaaS platform without cloud lock-in

## When to choose
- you need high-throughput, low-latency function invocation (100k+ req/s)
- your workloads are data, I/O, or compute intensive, including GPU inference
- you want serverless on Kubernetes, on-prem, edge, or multi-cloud without vendor lock-in
- you integrate serverless functions into data science workflows (Jupyter, Kubeflow, MLRun)

## When to avoid
- you only need simple scheduled cron jobs or lightweight background tasks
- you prefer a managed FaaS offering like AWS Lambda and are on AWS already
- your team lacks Kubernetes or Docker operational experience
- you need a broad ecosystem of third-party function marketplaces

## Facets
- artifact type: service
- maturity: stable
- function: serverless, deployment, microservices, streaming, etl, gpu-computing, container-runtime, api-framework
- domain: cloud-computing, machine-learning, big-data
- platform: go, cloud, self-hosted, cross-platform
- tags: faas, serverless-platform, event-processing, data-science, iguazio, mlrun, function-as-a-service, gpu-support, data-engineering, devops, containers, real-time, kubernetes, docker

## Member repositories
- nuclio/nuclio (main) score 95

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:29.011298+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:53:18.654273+00:00, confidence not recorded.
  - readme: https://github.com/nuclio/nuclio (fetched 2026-08-28T04:09:29.011298+00:00, sha 0d0c6acdf0c5)
  - homepage: https://nuclio.io (fetched 2026-08-29T08:49:04.799779+00:00, sha ec7ec29e5147)
- Data as of 2026-08-30T08:39:29.467469+00:00.