CircleCI

Automate your workflow from GitHub

GitHub's built-in CI/CD and workflow automation platform that runs directly in your repository. Define workflows in YAML triggered by Git events (push, PR, release), schedules, or manual dispatch. Massive marketplace of 20,000+ community-maintained actions for testing, building, deploying, and automating tasks. Supports matrix builds, caching, secrets, environments with approval gates, and self-hosted runners. Free for public repos with generous minutes for private repos.

Built-in CI/CD for GitLab

GitLab's built-in CI/CD pipeline system defined via .gitlab-ci.yml files in your repository. Features multi-stage pipelines, parallel jobs, matrix builds, environments with deployment tracking, merge train for serialized merges, auto DevOps for zero-config CI/CD, container registry, and security scanning (SAST/DAST). Supports self-hosted runners and Kubernetes executors. Tightly integrated with GitLab's merge requests and issue tracking. A leading alternative to GitHub Actions for GitLab users.

The open-source automation server

Open-source automation server for building, testing, and deploying software. Jenkins is the de facto self-hosted CI/CD platform with thousands of plugins for source control, build tools, cloud providers, and notifications. Pipelines are defined as Groovy code (Jenkinsfile) and run on distributed agents. Battle-tested in enterprise environments since 2011 — the reference implementation for self-managed CI before SaaS alternatives existed.

Kubernetes operator for serving AI inference workloads

KubeAI is an Apache-2.0 Kubernetes operator for deploying and scaling AI inference workloads, including LLMs, embeddings, reranking, and speech-to-text. It gives platform teams OpenAI-compatible endpoints, model proxy/controller primitives, model caching, scale-from-zero behavior, and cluster-native resource management for self-hosted inference on Kubernetes.

Sandboxes for coding agents — Linux VMs, Git, and deploys in one box

Freestyle is YC-backed sandbox infrastructure built for AI coding agents, shipping secure Linux VMs with nested virtualization, Git servers, and one-click web deploys. It lets agents run real workloads, branch repos, and deploy apps under short-lived identities while billing only for active compute. Used in production by vly.ai, Rork, and Vibeflow.

Open-source toolkit for building AI SRE incident response agents

OpenSRE is Tracer Cloud’s open-source public-alpha Python toolkit for building AI SRE agents that investigate and respond to production incidents. It ships 60+ tools across observability, databases, incident management, communications, deployment and protocol integrations, plus simulation/evaluation workflows for benchmarking agent accuracy before live pager use.

Autonomous coding agents that ship while you sleep

Twill is an autonomous coding agent platform that implements features, fixes bugs, and ships pull requests without manual intervention. Uses structured workflow of research, planning, human review, implementation in isolated sandbox, AI code review, then merge. Supports custom agent configurations with multiple LLM providers, isolated dev environments for verification, and integrations with GitHub, Linear, Sentry, Notion, and cloud platforms for end-to-end engineering automation.

ML inference platform for production AI models

Baseten is the inference platform for deploying AI models at scale with dedicated and pre-optimized model APIs and performance-optimized infrastructure. Specializes in image generation, transcription, text-to-speech, LLM serving, embeddings, and compound AI workloads. Delivers 75% latency reduction with 415ms cold starts and 3000+ concurrent scaling. Available as managed cloud or self-hosted, trusted by Cursor, Notion, Descript, and Sourcegraph for production inference.

AI-powered production incident resolution

Resolve AI automates production incident investigation, diagnosis, and remediation acting as an AI SRE that participates in every on-call rotation. Autonomously investigates incidents pursuing multiple hypotheses in parallel, validates against real evidence, creates code snippets and drafts PRs, generates post-mortems, and onboards new teammates with instant answers about code and infrastructure. Drives 5x faster MTTR and 87% faster incident investigations.