Open source. Ready for production.
Deploy OSS apps, open AI models, private inference, RAG infrastructure, and production environments.
Paste a GitHub repo or Hugging Face model. We review it, score its production readiness, and run it safely on infrastructure you control.
Scored across security, reliability, cost, and operability. Ready to deploy with minor hardening.
- Deployment complexity
- Medium
- Infrastructure
- Private VPC · K8s
- Security
- 0 critical CVEs
- Backups
- Verified
- Monitoring
- Ready
- Est. monthly cost
- $180–420 / mo
Pick a tool. We make it production-ready.
Every app and open model in our catalog has been reviewed, benchmarked, and packaged for real deployments — not just a local demo. Search the index, or paste any GitHub or Hugging Face URL into the cockpit above.
- 120+
- Apps reviewed
- 40+
- Models served
- 6
- Environments
- n8nWorkflow automationDeploy ↵
- DifyLLM app platformDeploy ↵
- LangfuseLLM observabilityDeploy ↵
- PostHogProduct analyticsDeploy ↵
- SupabasePostgres backendDeploy ↵
- Cal.comSchedulingDeploy ↵
- SupersetBI & dashboardsDeploy ↵
- AirbyteData integrationDeploy ↵
- GrafanaMetrics & monitoringDeploy ↵
- Open WebUIChat front-endDeploy ↵
- Qwen372B · Apache-2.0Deploy ↵
- Llama 3.370B · CommunityDeploy ↵
- MistralSmall 3 · Apache-2.0Deploy ↵
- DeepSeek-R1Reasoning · MITDeploy ↵
- Gemma 327B · Gemma termsDeploy ↵
- Qwen3-EmbeddingEmbeddingsDeploy ↵
- bge-rerankerRerankerDeploy ↵
- Qwen2-VLVision-languageDeploy ↵
Open source moves fast.
Production is where teams get stuck.
- 01
The docs work locally, but not in production
Docker Compose on a laptop is one thing. A resilient, secure, observable deployment is another entirely.
- 02
Model choices are confusing and change fast
New open models ship weekly. Picking the right one for your task, license, and hardware is a moving target.
- 03
GPU costs and inference latency are hard to predict
Without benchmarks, capacity planning is guesswork — and the bill arrives before the answers do.
- 04
SSO, backups, monitoring, and upgrades are not included
The boring-but-critical work is exactly where most self-hosting efforts quietly fall apart.
- 05
Fine-tuning without evaluation wastes time and money
Training a model is easy. Proving it actually improved on a measurable benchmark is the hard part.
- 06
Teams want data control without a full DevOps or MLOps team
Owning your stack shouldn't mean hiring full-time platform engineers just to keep the lights on.
From evaluation to production operations.
OSS Reviews & Live Demos
We test open-source projects, document use cases and limitations, and host practical demos so you can evaluate before installing.
App Deployment
We deploy OSS apps into your environment with domains, SSL, databases, storage, backups, monitoring, and handoff documentation.
Model Selection
We help you choose the right open model based on task, license, language, latency, cost, privacy, hardware, and deployment constraints.
Model Deployment & Inference
We set up production inference with vLLM, Ollama, TGI, NIM, llama.cpp, or other serving stacks, including OpenAI-compatible APIs where appropriate.
Fine-tuning & Customization
We prepare datasets, run measurable fine-tuning workflows, evaluate results, and deploy custom models or adapters into production.
Featured reviews and demos.
n8n
Self-hosted automation that connects your tools with visual workflows.
Best for — Ops & internal automation teams
Dify
Build, orchestrate, and ship LLM apps with RAG and agent workflows.
Best for — Teams building AI products
Langfuse
Trace, evaluate, and monitor LLM applications in production.
Best for — AI engineering & QA
PostHog
Product analytics, session replay, and feature flags, self-hosted.
Best for — Product & growth teams
Cal.com
Open-source scheduling infrastructure you fully control.
Best for — Sales & customer teams
Open WebUI
A polished chat interface for your private models and endpoints.
Best for — Internal AI assistants
Deploy where your data lives.
We design around your security, data residency, latency, hardware, and compliance requirements.
Your cloud account
AWS · GCP · Azure
Private VPC
Network-isolated
Kubernetes
EKS · GKE · AKS · self-managed
On-prem servers
Your data center
Private GPU cluster
Dedicated inference
Local workstation
Dev & evaluation
Edge or air-gapped
No public network
Not a slide deck.
The actual control surface.
Every deployment ships with live logs, readiness checks, inference metrics, and a documented architecture you own and can hand to your team.
- TLS / HTTPS enforced
- Automated backups + restore test
- SSO / OIDC configured
- Monitoring + alerts wired
- Secrets in vault, not env files
- Rate limiting + WAF
- Disaster-recovery runbook
Choose the level of help you need.
Review Report
For teams deciding whether to use an OSS app or open model.
- Use case fit
- Alternatives
- Deployment complexity
- License and risk notes
- Cost estimate
Starter Deployment
For founders, makers, and small teams.
- Deploy one OSS app or model endpoint
- Domain and SSL setup
- Basic configuration
- Basic documentation
Production Deployment
For teams running open source in production.
- Architecture setup
- Database, storage, GPU, or inference configuration
- Backups and restore validation
- Monitoring and alerts
- Upgrade or rollback plan
Enterprise Readiness
For companies with security, compliance, or scale requirements.
- Private VPC, Kubernetes, on-prem, or GPU cluster setup
- SSO / SAML / OIDC where supported
- RBAC and audit logging
- Security hardening checklist
- Fine-tuning or model evaluation workflow
- SLA and ongoing support
How it works.
Send us what you want to run
Share a GitHub repo, a Hugging Face model, or a business requirement.
We review fit and risk
We assess fit, risks, license, hardware, and deployment complexity.
We recommend an approach
You get a recommended architecture and a clear cost estimate.
We deploy or fine-tune
We deploy, fine-tune, or configure it directly in your environment.
Handoff & support
You get documentation, a clean handoff, and optional ongoing support.
Why not just follow the docs?
- Spend hours debugging setup
- Unclear production risks
- Unknown GPU cost and latency
- No backup validation
- No model evaluation workflow
- No upgrade or rollback plan
- Working deployment delivered
- Production-ready architecture
- Cost and hardware guidance
- Tested backup and restore
- Measurable model evaluation
- Security and operations checklist
Found an open-source project or model you want to use?
Send us a GitHub repo, Hugging Face model, or business requirement. We'll review it and recommend the best way to run it.
Frequently
asked.
01Do you deploy into our own cloud account?
Yes. We deploy directly into infrastructure you own and control, so your data never leaves your environment. We work with AWS, GCP, Azure, DigitalOcean, Hetzner, and more.
02Can you support on-prem or private GPU environments?
Yes. We deploy to on-prem servers, private GPU clusters, and even air-gapped environments, designing around your hardware, latency, and compliance requirements.
03Can you help choose an open AI model?
Absolutely. We recommend the right open model based on your task, license, language, latency, cost, privacy, and hardware constraints — backed by practical benchmarks.
04Can you fine-tune a model with our data?
Yes. We prepare datasets, run measurable fine-tuning workflows, evaluate the results against a benchmark, and deploy the custom model or adapters into production.
05Can you expose a self-hosted model through an OpenAI-compatible API?
Yes. We serve models with stacks like vLLM, TGI, Ollama, or NIM and expose OpenAI-compatible endpoints where appropriate, so your existing code works with minimal changes.
06Do you provide ongoing maintenance?
Yes. We offer optional plans covering upgrades, security patches, backup validation, monitoring, cost optimization, and incident support.
07Do you review licenses and commercial usage risks?
Yes. Every review includes license analysis and notes on commercial usage, redistribution, and any risks you should be aware of before adopting a project or model.
08Can you deploy both OSS apps and AI models together?
Yes. Many teams want an app like Dify or Open WebUI connected to a self-hosted model and RAG stack. We design and deploy the full system end to end.