```html NaviNeLabs — Open-source AI infrastructure
Open-source AI infrastructure

Build AI apps.
Own the infrastructure.

NaviNeLabs is the open-source control plane for production AI. Connect models, build agents, manage prompts, monitor costs, evaluate responses, and deploy — from one place.

terminal
$ npx navinelabs init
✓ Initializing AI infrastructure...
✓ Model gateway connected
✓ Observability enabled
✓ Ready to build
→ Your AI infrastructure is ready.
Open Source Self Hosted Developer First Model Agnostic Production Ready

AI prototypes are easy. Production AI isn't.

Building a demo takes an API call. Building a reliable AI product means stitching together models, prompts, databases, observability, queues, evaluations, and infrastructure.

Multiple model providers
Scattered prompt management
No visibility into AI costs
Difficult agent debugging
RAG and evaluation complexity
WITHOUT NAVINELABS
OpenAI API
↓
Prompt files
↓
Vector database
↓
Logging service
↓
Custom evaluation scripts

Everything your AI app needs.

One developer platform for the infrastructure behind production-grade AI applications.

01
⌁

Model Gateway

One API for OpenAI, Claude, Gemini, Groq, Ollama and other model providers. Switch models without rewriting your app.

02
✦

Agent Runtime

Build tool-using AI agents with controlled execution, memory, retries, limits and structured outputs.

03
⌘

Prompt Management

Version, test and roll back prompts without changing application code.

04
◌

RAG Pipeline

Build retrieval pipelines with embeddings, vector search, chunking and context management.

05
◈

Observability

See every request, prompt, tool call, latency, token and failure in one developer dashboard.

06
↗

Evaluations

Compare models and prompts against datasets to measure quality, cost, latency and reliability.

Know exactly what your AI is doing.

Trace requests from user input to final response. Understand model usage, token consumption, latency, failures and cost.

app.navinelabs.me / overview

Overview

Last 30 days

Last 30 days ▾
Requests
1.28M
↑ 18.4%
Tokens
48.2M
↑ 12.1%
Avg latency
1.82s
↓ 9.2%
Cost
$137
↓ 6.8%
AI REQUESTS

Bring your models.
Bring your stack.

NaviNeLabs sits between your application and infrastructure. Use the tools you already love.

OpenAI
Anthropic
Gemini
Groq
Ollama
PostgreSQL
pgvector
Redis
Docker
Kubernetes

Built in the open.
Owned by you.

Self-host NaviNeLabs on your own infrastructure. Your data, your models, your configuration. No vendor lock-in.

★ Star on GitHub
navinelabs/core
★ Open Source
MIT License
100% Self-hosted
∞ Control
git clone github.com/navinelabs/core

Start free.
Scale when you need.

The core platform stays open source. Pay only when you want NaviNeLabs to manage the infrastructure for you.

Open Source
$0 / forever
Run NaviNeLabs yourself.
  • Full core platform
  • Unlimited projects
  • Model gateway
  • Agent runtime
  • Basic observability
  • Docker deployment
Pro
$19 / month
For serious AI builders.
  • Everything in Cloud
  • Advanced evaluations
  • AI replay
  • Extended logs
  • Higher limits
  • Priority support
Team
$49 / month
For teams building together.
  • Everything in Pro
  • Team workspaces
  • RBAC
  • Audit logs
  • Shared datasets
  • Priority support

Enterprise deployments and private infrastructure available on request.

Questions, answered.

Yes. The core NaviNeLabs platform is designed to be self-hostable and open source. You can run it on your own infrastructure and retain control over your data.
No. NaviNeLabs is model agnostic. The goal is to let developers work with multiple providers through a consistent interface.
Yes. The platform is designed around self-hosting. Docker is the initial deployment path, with more deployment options planned as the project grows.
The $5 Cloud plan is aimed at indie developers who want a managed NaviNeLabs instance without managing servers, updates and backups themselves.
Developers, startups and small teams building AI-powered applications that need more infrastructure than a simple LLM API call but don't want to build the entire platform themselves.

Stop stitching AI infrastructure together.

Build your AI application on infrastructure you can understand, control and own.

```