ODS turns a GPU machine or server you already own into a complete private AI stack with a single install command, replacing a shelf of separate tools with one system that serves models, chat, agents, automation, and search from your own hardware. The installer reads your GPU, VRAM, memory, and disk, then downloads a GGUF model matched to that hardware tier, and a compact bootstrap model starts answering within minutes while the full model finishes downloading. From the web dashboard you watch every service's health, ports, and resource use, restart what misbehaves, and check storage and updates without touching a terminal. You chat with local models in Open WebUI, hand background work to the Hermes and Portal agents, and build automations in n8n that connect the stack to more than 400 other apps. Documents you add become searchable knowledge through Qdrant vector storage, so answers cite your files instead of the model's memory. SearXNG and Perplexica answer web questions without trackers or accounts, Whisper transcribes speech, Kokoro reads text aloud, and ComfyUI generates images, all on the same box. Every bundled service is an extension you can enable, disable, or replace, and a Privacy Shield scrubs personal data before anything reaches an optional cloud model through the LiteLLM gateway. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

OpenLLM serves any large language model as an OpenAI-compatible API endpoint from a single CLI command, handling model download, backend selection, quantization, and port binding automatically. It supports the full spectrum of popular models including Llama 3.3, Qwen2.5, DeepSeek, Mistral, and Phi3, choosing between vLLM and PyTorch inference backends based on hardware capabilities. When vLLM is available, continuous batching with PagedAttention achieves up to 23x throughput improvement over naive serving, while GPTQ and bitsandbytes quantization reduces memory requirements for GPU-constrained deployments. The server exposes a RESTful API on port 3000 with full OpenAI client library compatibility, enabling drop-in replacement for commercial providers in any application using the standard chat completions format. A built-in web chat UI at the /chat endpoint provides immediate interactive testing without external clients. Custom model repositories allow teams to maintain private catalogs of fine-tuned models alongside the default repository that tracks the latest releases. Deployment workflows generate production-ready Docker images automatically, with Kubernetes manifest support for orchestrated scaling. Native integration with LangChain and LlamaIndex supports RAG pipelines, Transformers Agents enables tool-calling workflows, and HuggingFace Hub handles model discovery. Server-Sent Events enable real-time token streaming across all API endpoints. Backed by BentoML's production ML infrastructure. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.

Imp runs large AI chat models privately on one NVIDIA RTX 50 or RTX PRO graphics card, exposing them through OpenAI and Anthropic compatible APIs plus a built-in browser chat. Start it with a single Docker command, point it at a model, and the chat opens in your browser with saved conversations and a live readout of generation speed. Coding agents such as Claude Code and Codex connect by changing one base URL, so your editor talks to the model on your own hardware instead of a rented cloud endpoint. Tool calls, structured JSON output, streaming responses, and reasoning models all work over the wire, and requests can be protected with an API key, per-IP rate limits, and concurrency caps. Load GGUF files you already have or NVFP4 and FP8 SafeTensors checkpoints straight from Hugging Face with no conversion step before the first answer. When a model is bigger than the card, mixture-of-experts weights stay in system RAM while the busiest experts are cached on the GPU, so a 123.6 GiB model still answers from a 32 GB card. Supported models can also read images alongside text, and tokenize, health, and model listing endpoints make the server easy to wire into scripts and monitors. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

With over 48,000 GitHub stars and monthly releases since March 2023, LocalAI is the self-hosted AI engine that replaces every OpenAI endpoint with a single Docker container running on your own infrastructure — serving chat completions, image generation, text-to-speech, speech-to-text, embeddings, vision, video generation, and function calling through identical API schemas that require zero application code changes. The composable backend architecture isolates each inference engine as a separate gRPC service running in its own OCI container, so llama.cpp, vLLM, SGLang, transformers, whisper.cpp, diffusers, MLX, Stable Diffusion, and Flux install on demand without touching the core, can run on separate machines, and a fault in one never affects others. Hardware acceleration spans NVIDIA CUDA 12 and 13, AMD ROCm, Intel oneAPI/SYCL, Apple Silicon Metal, Vulkan, and NVIDIA Jetson L4T — or runs entirely on CPU without any GPU. Built-in AI agents support autonomous tool use, retrieval-augmented generation, Model Context Protocol integration, and skill-based workflows directly in the web interface. The model gallery provides curated YAML configuration files for hundreds of models that install with a single command, while P2P federated inference distributes model shards across multiple machines for running models larger than any single node's memory. Multi-user API key authentication with quotas and role-based access enables team deployments. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Agent code needs somewhere disposable to run, and Sandstorm turns one modest VPS into a fleet of thousands of isolated sandboxes that wake the moment work arrives. You create sandboxes through a plain HTTP API, starting from a registered template or forking a tagged snapshot, with per-sandbox memory ceilings, attached volumes, and egress rules set on each call. Every sandbox is a gVisor container with its own network namespace, so untrusted agent code runs away from your host and away from its neighbors. Idle sandboxes cool down through four tiers, from running hot, to frozen in place with a wake time near ten milliseconds, to checkpointed on disk and back in about a fifth of a second, so a small machine can hold far more sandboxes than it could ever keep running at once. A single binary acts as master, node, and web console: the master tracks desired state and places work across nodes, nodes behind NAT dial out to join the fleet, and a gateway on each node wakes a sleeping sandbox and forwards its traffic automatically. The built-in Hub pulls ready-made snapshot packs for warmed runtimes like Python with numpy or a pre-warmed Playwright browser, skipping cold starts entirely. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

CubeSandbox gives every AI agent its own hardware-isolated MicroVM with a dedicated Linux kernel — not a shared-kernel container but a true KVM virtual machine — booting in under 60ms with less than 5MB of memory overhead per instance, enabling thousands of concurrent sandboxes on a single server via kernel sharing and Copy-on-Write memory. The Rust-built CubeProxy exposes an E2B SDK-compatible REST API on Axum, so existing E2B Cloud applications switch by changing one environment variable with zero client code changes. CubeMaster orchestrates cluster-level scheduling in Go while Cubelet manages node-local sandbox lifecycles including create, run, pause, resume, snapshot, and destroy operations. CubeVS, the eBPF-powered network layer, enforces per-sandbox isolation with LPM trie-based egress policies entirely in kernel space — no userspace context switches — while CubeEgress provides an L7 security proxy with per-domain, per-path, and per-method allowlists plus automatic credential injection that keeps secrets invisible to sandbox code. Hundred-millisecond-granularity snapshots enable checkpoint, rollback, and parallel fork from any saved state. The volume framework supports E2B-compatible pluggable storage with independent lifecycle and cross-sandbox sharing. Production deployment uses Terraform on Tencent Cloud or Helm charts on Kubernetes, K3s, or TKE, with full ARM64 support alongside x86_64 and AutoPause/AutoResume for cost optimization. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.

Stateful serverless actors that run indefinitely, sleep when idle, and persist state across restarts without external database round trips. Rivet provides a vendor-neutral alternative to Cloudflare Durable Objects, delivering long-running processes with co-located in-memory state and per-actor SQLite databases on any infrastructure you choose. The Rust engine comprises four packages: Pegboard for actor orchestration, Gasoline for durable execution, Guard for traffic routing via Envoy, and Epoxy implementing multi-region KV storage through EPaxos consensus. Each actor maintains instant-access in-memory state plus a dedicated SQLite database for relational queries, backed by RocksDB on single nodes or PostgreSQL with NATS pub/sub for multi-node clusters. RivetKit SDKs in TypeScript, Rust, and Python support built-in WebSocket connections, task queues, scheduling, and CRDT-based real-time collaboration. The v2.3 rewrite moved the core runtime from JavaScript to native Rust via WebAssembly, eliminating main-thread blocking across Node.js, Bun, Deno, and Cloudflare Workers. A built-in dashboard provides actor inspection with real-time Prometheus metrics. Deploys as a single Docker container on port 6420 with optional PostgreSQL for persistence. Available on RepoCloud with a dedicated VPS under the Apache 2.0 license.

Managing Kubernetes shouldn't require every developer to become a Kubernetes expert — KubeSphere wraps cluster operations, CI/CD, service mesh, and observability into guided web workflows that operations teams configure once and developers consume without kubectl. The LuBan extensible architecture treats each capability as an independently installable extension plugging into a lightweight microkernel: enable DevOps without service mesh, add monitoring without the app store, compose exactly what your organization needs. Multi-cluster management provides a centralized control plane for provisioning and operating clusters across cloud providers, data centers, and edge locations with unified identity and resource quotas. The DevOps extension integrates Jenkins for graphical pipeline creation with S2I and B2I workflows, while Argo CD powers GitOps continuous deployment with real-time sync status. Istio-based service mesh provides traffic management, canary deployments, circuit breaking, and distributed tracing through the console without CLI knowledge. Prometheus monitoring with custom dashboards, Fluentd log collection with multi-tenant search, and alerting via Slack and email complete the observability stack. Multi-tenant workspace hierarchy enforces resource quotas and fine-grained RBAC roles across teams. A built-in Helm-based app store handles lifecycle management across clusters. KubeKey deploys production-grade clusters on bare metal or VMs with air-gapped support. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

With nearly 3,000 GitHub stars and 14 releases since its June 2025 launch, Kite delivers a lightweight Kubernetes dashboard that replaces the fragmented workflow of switching between kubectl, Lens, and separate monitoring tools with a unified multi-cluster management platform. The dashboard automatically discovers clusters from kubeconfig files and supports independent Prometheus configurations per cluster, providing real-time CPU, memory, and network charts alongside live pod log streaming with filtering and search. A built-in web terminal opens shells directly into pods and nodes without kubectl port-forward, while the Monaco-based YAML editor offers syntax highlighting and validation for in-place resource editing. The integrated AI agent, powered by OpenAI or Anthropic models via native Go SDK integrations, translates natural language into precise client-go API calls — querying cluster health, analyzing pod logs, scaling deployments, patching resources, and cleaning up failed jobs — all operating strictly within the logged-in user's RBAC permissions. Enterprise governance includes OAuth 2.0 single sign-on, multi-factor authentication, FIDO2 passkeys, granular role-based access control with per-namespace permissions, and comprehensive audit logging. Helm chart management enables browsing, installing, and upgrading releases directly from the UI, while the Docker registry integration provides quick image tag selection. Deploy via a single Docker container with SQLite or use the official OCI Helm chart with PostgreSQL or MySQL for production. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.

The de facto dashboard of observability: Grafana is the open-source frontend that turns the data stores you already run into interactive graphs. It does not store metrics itself; it connects to the data stores you already run and turns their contents into interactive dashboards. Supported sources number over 150 via plugins: Prometheus, Loki, Tempo, InfluxDB, Elasticsearch, MySQL, PostgreSQL, Microsoft SQL Server, AWS CloudWatch, Azure Monitor, Google Cloud Monitoring, and many more. Dashboards are built from a large library of panel types (time series, heatmaps, tables, gauges, logs) with template variables for reusable, parameterized views. Unified alerting evaluates rules against any connected data source, not just Prometheus, and routes notifications to Slack, PagerDuty, email, and other channels with grouping and silencing - unlike Prometheus Alertmanager, a single rule can combine a Loki log pattern, a PostgreSQL query result, and a CloudWatch metric. Dashboards serialize to JSON and data sources configure via provisioning files, so the entire observability setup can live in Git and deploy repeatably across environments. Explore mode adds ad-hoc querying outside dashboards, with split view for correlating a metric spike against the matching log lines, and access control spans organizations, teams, folder permissions, and OAuth, LDAP, and SAML integration. Written in Go and TypeScript, AGPL-licensed. Self-hosting gives you unlimited users, dashboards, and queries at flat hosting cost, without Grafana Cloud's usage-based pricing.