55 tools you can run on your laptop

Serves secrets from 1Password to the cluster.

Schedules and monitors data pipelines written as Python.

GitOps delivery with a UI: syncs the cluster to git and shows the drift.

Runs multi-step workflows as Kubernetes pods.

Emulates Azure Storage locally.

A developer portal: a catalog of every service, its owners and its docs.

Issues and renews TLS certificates automatically, including from your own CA.

Runs PostgreSQL on Kubernetes with failover and backups handled by an operator.

Defines your own Kubernetes APIs that create infrastructure and apps.

Building blocks for distributed apps - state, pub/sub, retries - behind one API.

Copies secrets from a vault or cloud store into Kubernetes Secrets.

Watches system calls at runtime and alerts on suspicious behaviour.

Progressive delivery: shifts traffic to a new version and rolls back on bad metrics.

Keeps the cluster matching what is in git, and installs Helm charts declaratively.

The Kubernetes standard for routing traffic in from outside, by hostname and path.

Google Cloud's local emulators for services such as Pub/Sub.

Dashboards over metrics, logs and traces.

A container registry with vulnerability scanning and access control.

A friendly web UI for exploring and managing a cluster.

Packages Kubernetes apps as versioned, configurable charts.

A service mesh: encrypts and controls traffic between services without changing them.

The long-standing automation server for build and deploy pipelines.

Notebooks for many users at once - the classic place to try ML code.

Scales workloads on events - queue length, schedules, metrics - down to zero.

Single sign-on and identity: logins, users, groups, OAuth and OIDC.

Draws the service mesh: who talks to whom, and whether it is encrypted.

Kubernetes in Docker: a whole cluster as containers on your laptop.

Serverless on Kubernetes: scale to zero and back on demand.

Runs containers across machines and keeps them matching what you declared.

Runs virtual machines as Kubernetes resources, next to containers.

Policies as Kubernetes resources: validate, mutate or block what gets created.

Traces and evaluates LLM calls: prompts, outputs, cost and latency.

Emulates AWS services locally (some features need a LocalStack account).

Stores logs indexed only by labels, which keeps it cheap.

Catches email your apps send, so you can read it without sending it.

The live CPU and memory numbers that autoscalers and kubectl top read.

Microsoft's relational database, in its Linux container edition.

A document database storing JSON-like records.

Fast, lightweight messaging between services.

Downloads and runs open language models locally behind a simple API.

Shows what each namespace and workload costs to run.

Receives, processes and forwards metrics, logs and traces in one standard format.

A UI and alerts for Kyverno policy results.

The open-source relational database most teams reach for first.

Scrapes and stores metrics, and answers questions about them over time.

A vector database: finds text by meaning, the retrieval half of RAG.

A message broker: queues that decouple who sends work from who does it.

An in-memory store for caches, queues and counters.

Stores distributed traces: the path one request took through every service.

Long-term, highly available storage for Prometheus metrics.

Watches your files and rebuilds and redeploys to the cluster as you save.

Scans images and clusters for vulnerabilities and misconfigurations.

Backs up and restores cluster resources and volumes.

Security monitoring and SIEM: collects and correlates security events.

The blog engine, here as a familiar app to deploy and break.