🏠 Self-Hosted Solutions

Best Self-Hosted LLM Proxy

Comprehensive guide to deploying your own LLM proxy infrastructure. Complete control, privacy, and customization for organizations requiring on-premise AI gateway solutions.

10+
Solutions Reviewed
100%
Data Privacy
$0
Licensing Cost
24/7
Your Control

LiteLLM Self-Hosted

Easy Setup

LiteLLM is the most popular self-hosted LLM proxy, supporting over 100 different providers through a unified OpenAI-compatible API. The self-hosted version provides complete control over your infrastructure, data privacy, and unlimited customization options. Ideal for organizations that need multi-provider support without relying on external services. The active community and comprehensive documentation make it accessible even for teams new to LLM infrastructure.

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Multi-Provider Support

Connect to 100+ LLM providers including OpenAI, Azure, Anthropic, and local models.

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Cost Tracking

Built-in usage tracking and cost allocation across teams and projects.

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Enterprise Security

API key management, rate limiting, and access control features.

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High Performance

Async support, caching, and load balancing for production workloads.

System Requirements

CPU
2+ cores
RAM
4GB minimum
Storage
10GB+ free
Runtime
Python 3.8+
# Quick Docker deployment docker run -d \ -p 4000:4000 \ -e OPENAI_API_KEY=your_key \ ghcr.io/berriai/litellm:main-latest # Or install via pip pip install litellm[proxy] litellm --model gpt-3.5-turbo

LocalAI

Moderate

LocalAI is a fully self-contained, OpenAI-compatible API that runs entirely on your infrastructure without any external API calls. Perfect for organizations with strict data privacy requirements or those looking to reduce ongoing API costs. Supports a wide range of open-source models and provides GPU acceleration for optimal performance. No internet connection required after initial setup, making it ideal for air-gapped environments.

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Complete Privacy

All processing happens locally with no external data transmission.

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OpenAI Compatible

Drop-in replacement for OpenAI API, no code changes required.

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GPU Acceleration

CUDA support for high-performance inference on NVIDIA GPUs.

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Multiple Formats

Support for GGML, GGUF, and other quantized model formats.

System Requirements

CPU
4+ cores
RAM
16GB+ recommended
GPU
NVIDIA (optional)
Storage
50GB+ for models
# Docker deployment docker run -p 8080:8080 \ -v $PWD/models:/models \ -e MODELS_PATH=/models \ localai/localai:latest # Pull a model curl http://localhost:8080/models/apply \ -H "Content-Type: application/json" \ -d '{"name": "llama-2-7b-chat"}'

Ollama

Beginner Friendly

Ollama provides the simplest path to running large language models locally with minimal setup. The platform handles model downloads, GPU configuration, and provides an OpenAI-compatible API out of the box. Excellent for development environments, prototyping, and small to medium production deployments. Cross-platform support including native Apple Silicon optimization for Mac users.

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One-Command Setup

Install and run models with a single command, no complex configuration.

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Apple Silicon Native

Optimized performance on M1/M2/M3 Macs with Metal acceleration.

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Model Library

Easy access to popular models like Llama 2, Mistral, and CodeLlama.

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REST API

Built-in API server for integration with applications.

System Requirements

CPU
Modern 64-bit
RAM
8GB+ recommended
Platform
Mac/Linux/Windows
Storage
10GB+ for models
# Install (macOS/Linux) curl -fsSL https://ollama.com/install.sh | sh # Run a model ollama run llama2 # Start API server ollama serve # API available at http://localhost:11434

Gloo Edge AI Gateway

Advanced

Gloo Edge provides a production-grade, Kubernetes-native AI gateway built on Envoy proxy technology. Designed for organizations requiring enterprise-level traffic management, observability, and service mesh integration. The platform excels in high-scale deployments with advanced features like custom filter chains, WAF integration, and multi-cluster support. Best suited for teams with strong DevOps capabilities.

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Kubernetes Native

Declarative configuration with custom resource definitions.

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Advanced Observability

Integration with Prometheus, Grafana, and distributed tracing.

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Enterprise Security

mTLS, RBAC, and WAF integration for comprehensive protection.

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High Performance

Envoy-based proxy handling millions of requests per second.

System Requirements

Platform
Kubernetes 1.24+
Nodes
3+ recommended
Memory
8GB+ per node
Expertise
DevOps required
# Install via Helm helm repo add gloo https://storage.googleapis.com/solo-public-helm helm install gloo gloo/gloo --namespace gloo-system # Configure AI upstream kubectl apply -f - <

Self-Hosting Deployment Checklist

Assess Requirements

Evaluate your organization's needs: expected traffic volume, latency requirements, compliance needs, and available infrastructure resources.

Choose Solution

Select the appropriate self-hosted solution based on complexity, features, and your team's technical expertise.

Prepare Infrastructure

Provision servers or containers with adequate CPU, memory, and storage. Configure network access and security groups.

Deploy & Configure

Install the chosen solution, configure API keys, set up authentication, and integrate with your existing systems.

Monitor & Maintain

Set up monitoring, logging, and alerting. Plan for regular updates and security patches.

Quick Comparison

Solution Setup Complexity Best For GPU Support Models
LiteLLM Easy Multi-provider use Not required 100+ providers
LocalAI Moderate Full privacy Yes Local models
Ollama Easy Quick start Yes Popular models
Gloo Edge Advanced Enterprise scale N/A Any upstream

🔗 Related Self-Hosting Resources

Continue exploring: Open Source Gateways | Ollama API Setup | LM Studio Guide | Production Deployment