Kubernetes operations and GitOps

Portainer is a unified infrastructure management platform that provides a centralized control plane for deploying, monitoring, and managing containerized applications. It functions as an orchestration-abstraction layer, translating user actions into platform-specific API calls to maintain consistency across diverse container runtimes and cluster technologies. By organizing users, teams, and resources into a single interface, it enables granular role-based access control and lifecycle management for containerized services and stacks. The platform distinguishes itself through its support for distributed edge infrastructure and secure remote connectivity. It utilizes encrypted tunnels and outbound-only agent communication to manage geographically dispersed environments without requiring inbound port exposure. Furthermore, it integrates a GitOps-driven reconciliation engine that automatically synchronizes service configurations from version-controlled repositories, facilitating continuous delivery workflows and automated stack redeployments. Beyond its core orchestration capabilities, the platform offers extensive tools for cluster administration, including web-based terminal access, namespace management, and resource monitoring. It supports standardized deployment through a template-based engine that allows for reusable configuration schemas and dynamic variable injection. Users can also manage multiple orchestration instances and remote environments through automated update scheduling, rollback mechanisms, and custom metadata tagging. The software is designed for flexible deployment, supporting air-gapped environments and providing programmatic access via secure API tokens.

Keycloak is an open-source identity and access management server that provides a centralized platform for user authentication, authorization, and identity federation. It functions as a standards-compliant identity provider, utilizing a centralized engine to validate credentials and issue cryptographically signed tokens based on industry-standard protocols like OpenID Connect and SAML. This enables organizations to secure diverse applications and services through a unified authentication layer. The platform distinguishes itself through its cloud-native orchestration and high-availability capabilities. It utilizes a Kubernetes-native operator and control loop pattern to automate the deployment, scaling, and lifecycle management of identity services within containerized environments. To ensure resilience and continuous uptime, the server employs a distributed data grid that synchronizes session state and cache entries across multiple nodes, preventing service interruptions during hardware or network failures. Beyond its core identity functions, the system offers a modular plugin architecture that allows developers to extend server functionality through custom interfaces for authentication, storage, and user federation. It also includes a theme engine for server-side template rendering, enabling the customization of login screens and user-facing pages to match specific branding requirements. Administrative tasks, including the management of realms, users, and security policies, can be performed through centralized tools or programmatically via a REST API. The project provides comprehensive documentation, including guides for server configuration, performance monitoring, and version migration. Installations are supported across various environments, ranging from standalone archives to containerized deployments managed by automated controllers.

Dynamo is a distributed inference orchestration platform designed for large language models. It functions as a system to coordinate prefill and decode phases across GPU nodes, utilizing a multi-backend runtime adapter to connect engines like vLLM and TensorRT-LLM through a unified block-oriented memory interface. An OpenAI-compatible API server provides the frontend for integration with existing tools and clients. The project is distinguished by its disaggregated serving architecture, which separates prompt processing and token generation onto independent GPU pools to optimize throughput and memory. It employs a key-value cache-aware request router that directs queries to workers holding relevant cache entries to reduce recomputation. High-speed data transfer mechanisms move cache blocks and weights directly between GPU VRAMs over RDMA or NVLink to minimize latency. The platform includes comprehensive capabilities for distributed fault tolerance, allowing in-flight requests to migrate and resume from failure points via token-state continuation. It features SLA-based autoscaling and performance profiling to right-size GPU pools and a Kubernetes-native operator for topology-aware scheduling. Additional support covers multimodal inference for images, video, and audio, alongside dynamic swapping of LoRA adapters. Installation is available via wheels, container images, charts, and crates, with support for major Linux distributions and NVIDIA GPU architectures from Ampere through Blackwell.

Kubernetes is a distributed container orchestration platform that automates the deployment, scaling, and management of containerized applications across clusters of computing nodes. It functions as a declarative infrastructure controller, utilizing a control loop architecture that continuously monitors the current system state against user-defined configurations to ensure desired operational outcomes. The system relies on a centralized API-driven interface and a replicated key-value store to maintain a consistent source of truth for all cluster objects. The platform distinguishes itself through a highly extensible design that allows users to define domain-specific objects using the same native API and control loop infrastructure. It employs a standardized abstraction layer for container runtimes, enabling modular execution engines, and utilizes a pluggable controller pattern that supports third-party integrations without requiring modifications to the core codebase. An algorithmic bin-packing engine further optimizes hardware utilization by dynamically matching workload requirements with available cluster capacity. Beyond core orchestration, the system provides comprehensive operational support for distributed environments, including automated lifecycle management, horizontal and vertical scaling, and self-healing mechanisms that maintain service availability. It encompasses integrated solutions for networking, persistent storage orchestration, and secure secret management. Diagnostic utilities for monitoring performance metrics, aggregating logs, and troubleshooting infrastructure-level issues are also included to support cluster health and reliability.

Strimzi is a Kubernetes operator that automates the deployment, management, and lifecycle of Apache Kafka clusters on Kubernetes or OpenShift. It uses custom resource definitions and declarative YAML configuration to define Kafka cluster topology, broker placement, and security settings, with operator-based controllers that reconcile the desired state with the actual cluster state. The operator handles rolling updates during cluster upgrades or configuration changes to maintain availability and data integrity, and supports rack-aware broker scheduling across Kubernetes nodes and availability zones for fault tolerance. It also includes an HTTP bridge that translates the Kafka binary protocol to HTTP requests and responses, enabling non-JVM applications to produce and consume messages without native Kafka client libraries. Strimzi provides tools for managing Kafka topics, users, connectors, and MirrorMaker through standard kubectl commands and custom resources. It secures Kafka communication with TLS, SCRAM-SHA, or OAuth authentication, automates TLS certificate generation and renewal, and verifies container image signatures using cosign before deployment to ensure supply chain integrity.

OpenTofu is a declarative infrastructure orchestrator that automates the provisioning and management of cloud resources. It functions as a platform-agnostic interface, allowing users to define their desired environment state in configuration files, which the system then reconciles against live infrastructure to calculate and execute necessary updates. The project utilizes a graph-based execution engine to determine the optimal sequence for resource operations, enabling the parallel processing of independent components to reduce deployment times. To support complex, multi-platform environments, it employs a provider-based plugin architecture that translates generic configuration definitions into specific API calls for various cloud services and third-party providers. Beyond core provisioning, the system facilitates infrastructure lifecycle management through reusable configuration modules that standardize deployments and enforce consistent patterns. It also provides a synchronization layer for state metadata, enabling distributed teams to coordinate changes and maintain consistent environment status across collaborative workflows.

The Prometheus Operator is a Kubernetes monitoring orchestrator and controller that manages Prometheus clusters and observability components through declarative custom resources. It functions as a custom resource controller that translates high-level Kubernetes resource definitions into the configuration files required by the underlying monitoring software. The project automates the deployment, scaling, and lifecycle of an observability stack, including the integration of components like Thanos and Alertmanager. It distinguishes itself by syncing monitoring targets, alerting rules, and scrape configurations directly via the Kubernetes API to maintain a consistent desired state across the cluster. The system covers several capability areas, including automated target discovery via label queries, declarative alerting and recording rule management, and the configuration of remote storage endpoints. It also handles infrastructure state management, synthetic endpoint probing, and the synchronization of notification routing and receivers. Resource correctness is maintained through admission webhooks that validate configuration rules and resource schemes before they are persisted to the cluster.

Maybe is a self-hosted financial platform designed for private deployment, providing a centralized interface to track investments, budgets, and net worth. By running the application on your own infrastructure, you maintain full control over your sensitive financial data and privacy. The platform is delivered as a containerized application suite, utilizing a declarative configuration framework to manage service lifecycles. It distinguishes itself through a structured approach to version control, allowing users to pin specific release tags to ensure environment consistency and perform controlled updates by pulling updated images from a remote registry. The system includes comprehensive tools for managing the application lifecycle, including database volume maintenance and the ability to reset persistent storage states. Deployment is handled through container orchestration, which ensures that the service remains portable and consistent across diverse hosting environments.

VictoriaMetrics is a high-performance, scalable time series database and observability platform designed for long-term storage and analysis of metric, log, and trace data. It functions as a unified backend for monitoring ecosystems, offering full compatibility with industry-standard protocols and query languages. The system is built to handle massive data volumes through a distributed architecture that supports horizontal scaling and efficient data lifecycle management. The platform distinguishes itself through a storage engine that utilizes consistent hashing for data sharding and log-structured merge trees to optimize write throughput and disk space. It provides robust multi-tenant isolation, allowing organizations to segment data and alerting configurations by account or project while maintaining secure, partitioned access. By offloading long-term data to object storage while retaining local caching, it balances cost-effective persistence with high-performance query execution. The system covers the entire observability lifecycle, including automated metric scraping, log aggregation, and distributed tracing. It features a sophisticated alerting and recording engine that supports dynamic rule evaluation and high-availability execution. Additionally, the project includes a Kubernetes operator that automates the deployment, configuration, and lifecycle management of monitoring components, ensuring consistent observability across containerized environments. VictoriaMetrics is distributed as a set of container-native services and can be managed via declarative resource definitions within Kubernetes clusters.

Watchtower is a container-based solution designed to automate the lifecycle management of Docker applications. It functions as a background service that monitors running containers, detects when new base image versions are available in registries, and automatically redeploys the containers to ensure they remain synchronized with the latest builds. The project distinguishes itself through its ability to orchestrate complex deployment workflows and maintain service availability during updates. It interacts directly with the container runtime to manage service dependencies and restart sequences, ensuring that dependent containers are handled in the correct order. Users can further customize the update process by defining lifecycle hooks that execute shell commands before or after a container is replaced, allowing for tailored initialization and cleanup tasks. Beyond automated updates, the tool provides extensive infrastructure observability and flexible management options. It supports event-driven updates via HTTP webhooks, declarative filtering to target specific containers, and secure remote management through encrypted communication and private registry authentication. Operational statistics can be exported to external monitoring systems, and the service can be configured to run in a passive observation mode to track image changes without performing automated redeployments.

Tyk is an open-source API gateway written in Go that routes, secures, and monitors network traffic across REST, GraphQL, TCP, and gRPC protocols. It functions as a multi-protocol proxy designed to deliver requests to backend services while managing the end-to-end API lifecycle. The system distinguishes itself through a plugin-based architecture that allows for the injection of custom logic into the request and response middleware chain. It also features native Kubernetes integration, operating as an ingress controller that uses operators and custom resource definitions to deploy security policies and orchestrate API routing. The gateway covers a broad range of management capabilities, including standardized authentication via tokens and certificates, granular access control, and network restrictions. It provides tools for traffic rate limiting and quotas to protect backend services, along with usage analytics and event-driven webhooks for external notifications. Configuration is managed through a dedicated command line tool that synchronizes system settings with version control systems across distributed nodes.

This project is a self-hosted platform-as-a-service that provides a centralized management interface for deploying, configuring, and monitoring containerized applications and databases on private infrastructure. It functions as a visual control plane, automating the end-to-end lifecycle of services from source code to production. By managing container orchestration, networking, and resource allocation, it allows users to maintain full control over their own hardware while streamlining the delivery of software. The platform distinguishes itself through its agentless architecture, which uses secure shell connections to execute administrative tasks and manage remote servers without requiring persistent local software. It integrates directly with version control systems to trigger automated build and deployment pipelines, including the creation of temporary, isolated preview environments for every pull request. This workflow is supported by a declarative engine that uses templates to standardize the deployment of complex multi-container architectures and persistent database engines. Beyond core orchestration, the system handles the operational requirements of hosted services by managing dynamic reverse-proxy routing and automated SSL certificate lifecycles. It provides a comprehensive suite of infrastructure management tools, including browser-based terminal access for debugging, automated system dependency installation, and persistent state management via a central database. These capabilities ensure that infrastructure remains synchronized and consistent across multiple remote environments.

TiDB is a horizontally scalable, distributed SQL database designed to provide consistent transactional storage and high-performance analytical processing within a single unified architecture. It utilizes a decoupled compute-storage design and a distributed key-value storage layer to ensure horizontal scalability and efficient range-based queries. By employing a consensus-based replication algorithm, the system maintains high availability and automatic failover across multiple nodes and geographical regions. The platform distinguishes itself through its hybrid transactional and analytical processing capabilities, which allow complex SQL queries to run against replicated columnar data without disrupting primary transactional workloads. It also integrates high-dimensional vector search functionality, enabling semantic similarity queries directly alongside traditional relational data. To support diverse operational needs, the system provides native tools for real-time data streaming, seamless migration from external database systems, and multi-region disaster recovery. The database is built for cloud-native environments, offering comprehensive lifecycle management through Kubernetes operators that automate deployment, scaling, and rolling upgrades. It maintains compatibility with standard SQL interfaces, allowing applications to connect using common drivers while managing complex concurrency through pessimistic transaction handling. Detailed documentation and command-line utilities are available to assist with cluster orchestration, performance troubleshooting, and the configuration of production-grade topologies.

Terraform is a declarative infrastructure-as-code tool designed to manage the lifecycle of cloud and on-premises resources. It functions as a workflow engine that reconciles a defined desired state against real-world infrastructure, using a persistent state-tracking layer to maintain consistency and visibility across distributed environments. By mapping infrastructure components into a directed acyclic graph, the system calculates the optimal order for provisioning, updating, or destroying resources. The platform is distinguished by its extensible plugin-based architecture, which decouples core orchestration logic from vendor-specific service APIs. This allows users to manage diverse infrastructure across multiple providers through a unified workflow. The system enforces predictability by separating operations into a three-stage lifecycle—planning, applying, and state-updating—and supports policy-as-code evaluation to validate changes against security and compliance rules before any modifications are executed. Beyond core orchestration, the tool provides robust support for collaborative management, including workspace isolation for environment separation and module sharing for distributing standardized infrastructure patterns. It integrates into broader development ecosystems through support for programmatic definition in various languages, external system hooks, and comprehensive tooling for configuration debugging and editor assistance.

Mattermost is a self-hosted, enterprise-grade communication platform designed for organizations that require strict control over their internal data and messaging infrastructure. It functions as a centralized hub for real-time team interaction, offering persistent messaging, voice and video conferencing, and integrated project management tools within a single, private workspace. The platform is built to support high-security environments, including air-gapped deployments where public internet access is restricted or unavailable. The platform distinguishes itself through a focus on regulatory compliance and administrative sovereignty. It provides granular role-based access control, comprehensive audit logging, and data retention policies to meet legal and security standards. Organizations can extend the core functionality through a plugin-based framework, allowing for the injection of custom server-side logic and UI components without modifying the underlying source code. Furthermore, the system acts as a secure workflow orchestrator, enabling teams to integrate automated tasks and external services directly into their communication channels. The architecture is designed for scalability and reliability, supporting large-scale deployments through Kubernetes-based orchestration and microservices-ready infrastructure. Administrators can manage complex environments using centralized identity federation, external search indexing for high-performance data retrieval, and robust disaster recovery planning. The platform also includes tools for mobile device management and custom branding to ensure a consistent and secure experience across organizational hardware. Comprehensive documentation is available to guide administrators through installation, configuration, and maintenance, including specific procedures for Kubernetes deployments and air-gapped environment setups.

Dokku is a self-hosted platform as a service that automates the deployment and management of web applications on your own infrastructure. It functions as an infrastructure automation tool, providing a git-driven engine that triggers container builds, service orchestration, and release workflows directly from source code repositories. The platform distinguishes itself by using buildpack-based image construction to detect project structures and automate container creation without manual configuration. It manages the full application lifecycle through a simplified interface that abstracts low-level container runtime commands, while dynamically handling reverse-proxy routing and environment-variable-driven configuration to map traffic and decouple settings from the underlying host. Beyond core deployment, the system provides comprehensive infrastructure lifecycle management, including the automated setup of system dependencies and the configuration of administrative access controls. The platform is designed for modular expansion, allowing users to extend core functionality through a plugin system that hooks into lifecycle events. It is installed on Linux distributions using automated scripts to ensure consistent environment preparation.

CloudNativePG is a Kubernetes operator designed for the administration, lifecycle management, and high availability of PostgreSQL database clusters. It functions as a declarative orchestrator that manages database instances through custom resources and manifests. The project distinguishes itself by automating complex operational tasks, including primary election and failover management via streaming physical replication. It provides specialized tools for database version migrations, supporting both offline in-place upgrades and online migrations through logical replication. The operator covers a broad range of capabilities including continuous physical backups to object storage with point-in-time recovery, dynamic cluster scaling, and persistent storage orchestration. It also handles traffic management by routing read-write and read-only requests to different endpoints and provides observability through performance metrics export and cluster health monitoring. Security is integrated through network traffic encryption and support for client certificate authentication.

K3s is a lightweight Kubernetes distribution designed for resource-constrained environments, edge computing, and simplified deployment across diverse hardware architectures. It functions as a container orchestration engine that automates the deployment, scaling, and management of containerized applications. By bundling all necessary control plane components and dependencies into a single binary, it minimizes the system footprint and streamlines the installation process. The project distinguishes itself through a flexible architecture that supports both high-availability clustering and minimal, single-node setups. It provides options for using an embedded SQLite datastore for small deployments or external databases for larger, resilient environments. Security is integrated into the core, featuring token-based node authentication, encrypted communication between nodes, and support for mandatory access control policies like SELinux. The platform covers a broad operational surface, including automated cluster version upgrades, manifest-based resource deployment, and integrated Helm chart management. It offers extensive configuration capabilities for networking, certificate management, and storage backends, allowing administrators to tailor the environment to specific infrastructure requirements. The system is designed to maintain consistent operational standards across distributed locations, ensuring that management remains centralized even when hardware resources are limited.

GreptimeDB is a distributed, open-source time-series database built for unified observability. It stores and queries metrics, logs, and traces together in a single columnar engine, supporting both SQL and PromQL for analysis. The database is designed as a Kubernetes-native operator with a decoupled compute and storage architecture, enabling horizontal scaling and multi-region deployment. What distinguishes GreptimeDB is its role as a multi-protocol ingestion gateway, accepting data through OpenTelemetry, Prometheus Remote Write, InfluxDB, Loki, Elasticsearch, Kafka, and MQTT protocols without requiring a predefined schema. It provides a unified observability data model that processes all three signal types as timestamped wide events, allowing JOIN queries across signals. The system includes a continuous aggregation pipeline with an optional Flownode component for streaming and materialized view computations, plus configurable log pipeline processing that parses and transforms raw log lines during ingestion. The database offers a broad capability surface including automatic schema inference, columnar storage with LSMT, distributed query execution with pushdown, and support for inverted, fulltext, and skipping indexes. It provides multiple query APIs (MySQL, PostgreSQL, HTTP, gRPC, Elasticsearch, Jaeger), BI tool connectivity, and integration with AI assistants through the Model Context Protocol. Deployment options range from standalone binaries to distributed clusters on Kubernetes, with metadata stored in etcd, MySQL, or PostgreSQL.

Colima is a command-line utility that provides lightweight container runtimes and local Kubernetes orchestration by managing isolated virtual machine environments. It functions as a virtualization manager that abstracts the underlying container engine, allowing users to run containerized applications and system workloads on non-native operating systems without the overhead of heavy desktop software. The project distinguishes itself through its support for hardware-accelerated workloads, enabling direct GPU passthrough to virtual machines for high-performance machine learning tasks. It offers robust profile-based configuration management, which allows users to maintain multiple independent runtime instances with dedicated resources, and supports seamless switching between different container engines to suit specific development requirements. Beyond core container and orchestration management, the tool provides comprehensive control over virtual machine lifecycles, including persistent volume mapping and resource optimization for CPU, memory, and disk usage. It facilitates secure interaction with these environments through socket forwarding and direct shell access, ensuring that developers can monitor and debug isolated instances effectively. Colima is distributed as a command-line tool that automates the initialization and configuration of virtualized environments through simple flags and configuration files.