BodhiApp
Self-hosted AI gateway for local GGUF models, cloud APIs, and MCP tools with enterprise auth.
Bodhi App is a standout choice for developers and teams who want both local and cloud LLM access through a single, secure gateway. Its enterprise-grade auth (OAuth2, JWT, RBAC) and MCP tool integration are rare in self-hosted solutions. However, it requires technical comfort with Docker or desktop setup. For a fully managed alternative, consider Portkey or Helicone; for a simpler local setup, try Ollama.
Verified 3d ago · liveness 65/100 · cite: rightaichoice.com/tools/bodhiapp
- Developers building AI apps needing privacy and local control
- Teams wanting a self-hosted AI gateway with user management
- Users experimenting with open-source LLMs easily
- Enterprises requiring role-based access and audit trails
- Users seeking a fully managed cloud AI service
- Those needing image generation or multimodal models
- Non-technical users who cannot run Docker or desktop app
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Skip Bodhi App if you prefer a fully managed AI service, need image generation or multimodal models, or aren't comfortable with Docker or desktop installation and managing local model files.
No hidden costs in the free tier, but you'll need to provide your own API keys for cloud providers, which may have usage costs.
Bodhi App is completely free with no feature limits, making it ideal for individual developers and startups that want enterprise-grade features without subscription fees. In contrast, managed gateways like Portkey or Helicone charge per usage or per seat, so Bodhi App offers a cost advantage for teams comfortable with self-hosting.
In short
BodhiApp — Self-hosted AI gateway for local GGUF models, cloud APIs, and MCP tools with enterprise auth. Best for Developers building AI apps needing privacy and local control, Teams wanting a self-hosted AI gateway with user management, Users experimenting with open-source LLMs easily. Free to use.
What people actually say about BodhiApp — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
10 mentions across 2 sources (Hacker News, GitHub) · researched Jul 3, 2026.
- +Unified local and cloud models via OpenAI-compatible API.
- +Enterprise-grade OAuth2 + JWT authentication out of the box.
- +One-click GGUF model downloads with resume support.
- +Built-in chat UI with markdown and streaming.
- +Docker images optimized for multiple GPU backends.
- −Memory allocation errors on phi-3.5 and large models.
- −Homebrew installs wrong architecture on some Macs.
- −Missing llama-server-bindings in source code occasionally.
- −Windows build not yet available as of mid-2024.
- −No extension/plugin support like Jan AI.
- • No hidden costs—fully free and open-source.
- • Cloud API usage incurs provider fees (OpenAI, Anthropic) not covered by BodhiApp.
Viability Score
How well maintained and how widely used is BodhiApp? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Run local GGUF models via llama.cpp with GPU acceleration
- Proxy cloud APIs (OpenAI, Anthropic, Gemini) through single endpoint
- OpenAI-compatible API (Chat Completions, Responses, Embeddings)
- Anthropic API compatibility layer
- Gemini API compatibility layer
- Ollama API compatibility layer
- Built-in chat UI with markdown and streaming
- Model aliases for inference parameter presets
- One-click model downloads from HuggingFace
- MCP tool integration and agentic tool calling
- User management with 4 roles and RBAC
- OAuth2 + JWT authentication with PKCE
- Access request workflow with audit trail
- Real-time streaming with Server-Sent Events
- Thinking model view for chain-of-thought display
About BodhiApp
Bodhi App is a unified AI gateway that lets developers and teams run local open-weight LLMs via llama.cpp, proxy cloud APIs (OpenAI, Anthropic, Gemini), and integrate MCP tools—all through a single, OpenAI-compatible endpoint. It's built for those who want privacy, flexibility, and control over their AI stack without sacrificing enterprise features. At its core, Bodhi App manages GGUF models downloaded from HuggingFace with one-click downloads, background progress, and auto-resume. You create model aliases that bundle a model file with inference parameters (temperature, top-p, etc.) and switch between them instantly without server restarts. The app exposes multiple API compatibility layers (OpenAI Chat Completions, Responses, Embeddings; Anthropic; Gemini; Ollama) simultaneously, so existing clients connect seamlessly. For teams, Bodhi App provides user management with four roles (User, PowerUser, Manager, Admin), OAuth2 + JWT authentication with PKCE, and an access request workflow with audit trails. A built-in chat UI supports markdown, real-time streaming, and a thinking model view for chain-of-thought. MCP integration allows models to autonomously invoke external tools mid-conversation, enabling agentic workflows. What sets Bodhi App apart is its hybrid approach: treating local and remote models equally, delivering enterprise-grade auth out of the box, and supporting multiple API formats from a single server. Deployment options include native desktop apps (Windows, macOS, Linux) and Docker images optimized for CPU, CUDA, ROCm, Vulkan, MUSA, Intel, and CANN—making it suitable for everything from personal experimentation to production deployments.
Behind the Verdict
Bodhi App occupies a unique niche: it's a self-hosted AI gateway that bridges local and cloud models with enterprise-grade authentication. The core strength is its flexibility—you can run GGUF models locally with GPU acceleration, proxy cloud APIs from OpenAI, Anthropic, and Gemini, and connect MCP tools for agentic workflows, all behind a single OpenAI-compatible endpoint. The MCP integration is particularly impressive; models can invoke external tools mid-conversation, and the per-user MCP instances with multiple auth methods (Header, OAuth2) offer granular control. The built-in chat UI with streaming and thinking model views is a nice touch for interactive exploration. However, this is not a tool for non-technical users. You need to be comfortable with Docker or desktop installation, managing model files (which can be several GB), and configuring authentication. The free tier has no explicit limits, but performance depends on your hardware. Compared to alternatives, Bodhi App offers more control than managed services like Portkey or Helicone, but with a steeper learning curve. Ollama is simpler for local-only use, but lacks multi-user auth and MCP support. If you're a developer or team needing privacy, local control, and a secure gateway, Bodhi App is worth evaluating.
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Real-world workflow fit
Concrete scenarios for the personas BodhiApp actually fits — and what changes day-one when you adopt it.
Download a GGUF model from HuggingFace via Bodhi App, create a model alias with desired parameters, and start using the OpenAI-compatible endpoint from existing code.
Outcome: Local model running with minimal setup, accessible via familiar API calls without code changes.
Deploy Bodhi App via Docker with CUDA support, set up OAuth2 and JWT authentication, configure roles (Admin, Manager, PowerUser, User), and invite team members.
Outcome: Centralized AI access with role-based control, audit trails, and secure token management, ready for production.
Connect an MCP server (e.g., a database tool) via the MCP playground, whitelist tools, and start a chat with an agentic model to execute autonomous tasks.
Outcome: Agentic workflows operational, with MCP tools invoked inline during conversations for real-world interactions.
Use Cases
- Run Llama 3 or Mistral locally with OpenAI-compatible API
- Proxy multiple cloud AI providers through one authenticated endpoint
- Deploy a team-wide AI gateway in Docker with RBAC
- Use MCP tools for autonomous database queries or API calls
- Compare GGUF models side-by-side via model aliases
- Build third-party apps connecting via OAuth2
Limitations
- Bodhi App requires downloading model files locally, which can be large (several GB).
- The free tier has no explicit limitations, but performance depends on local hardware.
- Multi-user features require running the Docker variant with authentication configured.
- Cloud proxy capabilities depend on the user's own API keys and subscriptions.
as of 2026-08-23
Verification history
We have re-verified BodhiApp 6 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published BodhiApp tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0
Ideal for
Individual developers or small teams exploring local LLMs with full features, no cost or limits.
What this tier adds
No cost and no feature restrictions—access to all core capabilities including MCP, auth, and Docker deployment.
Where the pricing makes sense
The company stage and team size where BodhiApp's pricing actually pencils out — and where peers do it cheaper.
Bodhi App is completely free with no feature limits, making it ideal for individual developers and startups that want enterprise-grade features without subscription fees. In contrast, managed gateways like Portkey or Helicone charge per usage or per seat, so Bodhi App offers a cost advantage for teams comfortable with self-hosting.
Setup time & first value
How long it actually takes to get something useful out of BodhiApp — broken out by persona, not the marketing-page minute.
Desktop app: 10-15 minutes to download and run a small model. Docker deployment: 30-60 minutes including configuration (GPU drivers, auth setup). MCP setup: add 15-30 minutes per server.
Switching to or from BodhiApp
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Ollama: Use Bodhi App's Ollama-compatible API to connect existing clients with minimal changes, then expand to local + cloud + MCP.
- →From direct OpenAI API: Switch base URL to Bodhi App's endpoint and keep the same code, while gaining local model options and auth.
- ↗To Ollama: Export model names and use Ollama's compatible API if you no longer need multi-user or cloud proxy.
- ↗To a managed gateway: Use Bodhi App's OpenAI-compatible endpoint as a drop-in, so switching to Portkey or Helicone is straightforward.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with BodhiApp
Common stack mates teams adopt alongside BodhiApp, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Bodhiapp vs Voyage Ai
Choose Voyage AI if you need high-accuracy embeddings and rerankers for enterprise RAG, especially in finance/legal domains. BodhiApp is unbeatable for teams wanting a free, self-hosted gateway to mix local GGUF models with cloud APIs, with built-in user management. They are complementary: Voyage improves retrieval quality; BodhiApp simplifies model orchestration.
Bodhiapp vs Spider Cloud
Spider Cloud and BodhiApp serve entirely different needs. Spider Cloud is a high-speed web scraping and crawling API optimized for feeding real-time data to AI agents and RAG pipelines, with a Rust engine and new Browser AI commands. BodhiApp is a self-hosted AI inference gateway that unifies local GGUF models and cloud APIs under a single endpoint with user management. Choose Spider Cloud if your primary need is web data extraction; choose BodhiApp if you need a privacy-first, controllable AI model server.
Bodhiapp vs Temporal Ai
Temporal AI and BodhiApp serve entirely different needs. Choose Temporal if you're building mission-critical AI agents that must survive failures and require durable orchestration with human-in-the-loop. Choose BodhiApp if you need a self-hosted, privacy-focused gateway to run local open-weight LLMs (GGUF) and proxy cloud APIs through a single OpenAI-compatible endpoint.
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Cortex.cpp
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