[Compare the top 10 AI gateways for production workloads that offer fast Docker deployment. An analysis of options like Bifrost, LiteLLM, and Kong shows that different gateways suit different needs, with some better for enterprise scale and others for simpler proxying.]
Deploying AI applications into production requires a reliable infrastructure layer to manage API requests to various Large Language Models (LLMs). An AI gateway acts as a centralized control plane for routing, authentication, and observing this traffic. For teams that value speed and consistency, using Docker to deploy an AI gateway provides a standardized, isolated environment that can be running in minutes.
This post examines ten of the best AI gateways available today that can be deployed quickly using Docker. We will compare them based on key features like provider support, reliability, governance, and observability.
Key Criteria for Evaluating AI Gateways
When selecting an AI gateway, engineering teams should evaluate several key factors to ensure the tool meets their production requirements. The most critical considerations include:
- Provider and Model Support: The gateway should support a wide range of LLM providers (OpenAI, Anthropic, Google Gemini, Azure, etc.) and open-source models (via Ollama, vLLM). A unified API that normalizes these different providers is a significant advantage.
- Performance and Latency: The gateway itself should add minimal overhead to the request lifecycle. Look for published benchmarks and low-latency architecture, as every millisecond counts in user-facing applications.
- Reliability Features: Production systems cannot tolerate provider outages. Key reliability features include automatic provider failover, exponential backoff for retries, and intelligent load balancing to distribute traffic effectively.
- Governance and Security: The ability to create virtual API keys, set per-user or per-project budgets and rate limits, and enforce access controls is essential for managing costs and securing AI usage.
- Deployment Flexibility: While this review focuses on Docker, the ability to deploy in various environments like Kubernetes, in a private VPC, or even air-gapped is a marker of an enterprise-ready solution.
The Top 10 AI Gateways for Docker Deployment
Based on the criteria above, here is an analysis of the leading AI gateways that offer a streamlined Docker deployment process.
1. Bifrost
Bifrost is an open-source AI gateway from Maxim AI, written in Go and designed for high-performance, enterprise-grade workloads. It unifies access to over 1000 models from more than 20 providers through a single OpenAI-compatible API. Its primary differentiator is its performance, adding only 11 microseconds of overhead at 5,000 requests per second.
Best for: Enterprise teams and mission-critical applications that require the lowest possible latency, high availability, and comprehensive governance. Its unified LLM, MCP, and Agents gateway capabilities make it a strong choice for building complex, reliable AI systems.
Key Features:
- Automatic Failover and Load Balancing: The Bifrost AI gateway can automatically route traffic around unavailable providers and balance loads across multiple API keys.
- Semantic Caching: Reduces costs and improves latency by caching responses to semantically similar queries.
- Advanced Governance: Provides virtual keys with configurable budgets, rate limits, and model access controls.
- MCP Gateway: Native support for the Model Context Protocol (MCP) enables complex agentic workflows and tool execution.
- Enterprise-Ready: Offers clustering for high availability, RBAC, audit logs for compliance, and in-VPC or on-prem deployment options. Beyond routing, Bifrost applies governance and security controls centrally, and Bifrost Edge extends that same governance to AI traffic on employee machines with endpoint enforcement.
Docker Deployment:
You can pull the Bifrost image from Docker Hub and run it with a single command.
docker run -d -p 8080:8080 -v bifrost-config:/etc/bifrost maximhq/bifrost:latest
2. LiteLLM
LiteLLM is a popular open-source library that provides a unified interface for calling over 100 LLM APIs. It can be deployed as a proxy server, functioning as a lightweight AI gateway. Its primary focus is on standardizing API inputs and outputs.
Best for: Startups and development teams needing a simple, developer-friendly way to manage multi-provider LLM calls without the complexity of a full enterprise gateway.
Key Features:
- Broad Provider Support: Excellent coverage of a wide range of LLM providers.
- Unified API Format: Consistent input/output format simplifies code when switching between models.
- Cost and Usage Tracking: Provides basic logging and callback functions for tracking token usage.
Docker Deployment:
LiteLLM offers a straightforward Docker setup for its proxy server.
docker run -d -p 4000:4000 -e LITELLM_MASTER_KEY=sk-1234 ghcr.io/berriai/litellm:main --master_key sk-1234
3. Kong AI Gateway
Kong AI Gateway is an extension of the widely used Kong API Gateway. It brings AI-specific capabilities to an existing, battle-tested infrastructure product. It is a good fit for organizations that have already standardized on Kong for their microservices architecture.
Best for: Large organizations already invested in the Kong ecosystem for API management that want to add AI-specific controls and plugins.
Key Features:
- Multi-LLM Support: Routes requests to different LLM providers.
- AI-Specific Plugins: Offers plugins for prompt engineering, response transformation, and credential management.
- Enterprise Integrations: Inherits Kong's robust support for enterprise authentication, security, and observability.
Docker Deployment:
Deploying Kong with the AI Gateway capabilities involves running the Kong Gateway container and configuring the AI plugins.
docker run -d --name kong-gateway \
-e "KONG_DATABASE=off" \
-e "KONG_DECLARATIVE_CONFIG=/kong/declarative/kong.yml" \
-p 8000:8000 \
-p 8443:8443 \
kong:latest
4. Cloudflare AI Gateway
Cloudflare's AI Gateway is a managed gateway service that sits in front of your AI applications. It focuses on providing caching, analytics, and rate limiting as part of the broader Cloudflare ecosystem. It's less of a self-hosted solution and more of a managed service.
Best for: Teams already using Cloudflare's network services who want to add caching and observability to their LLM calls with minimal setup.
Key Features:
- Global Caching: Caches responses at the edge to reduce latency.
- Analytics and Logging: Provides insights into request volume, errors, and costs.
- Rate Limiting: Protects applications from abuse and controls costs.
Docker Deployment:
Cloudflare AI Gateway is a managed service, not a self-hosted Docker container. Deployment involves configuring your application to send requests to a Cloudflare endpoint instead of directly to the LLM provider.
5. OpenRouter
OpenRouter is a hosted service that provides a unified API to a wide variety of LLM models, including many open-source and fine-tuned models. It acts as a "meta-provider," routing requests to the best model for the job based on price and performance.
Best for: Developers and researchers who want to experiment with a diverse range of models without managing individual API keys for each one.
Key Features:
- Model Variety: Access to a huge catalog of models, including experimental ones.
- Pay-per-use: A single billing point for usage across all models.
- Model Rankings: Provides leaderboards and rankings to help developers choose models.
Docker Deployment:
Like Cloudflare, OpenRouter is a hosted service. There is no self-hosted Docker image; usage involves pointing your application to the OpenRouter API endpoint.
6. NVIDIA NIM
NVIDIA NIM (NVIDIA Inference Microservices) are packaged AI models that can be deployed as optimized services. While not a gateway in the same vein as others on this list, a collection of NIMs deployed behind a load balancer can serve a similar function for self-hosted models.
Best for: Teams with on-premise GPU infrastructure who want to deploy and serve highly optimized open-source or custom models with maximum performance.
Key Features:
- Optimized Inference: Built on TensorRT-LLM for high-throughput, low-latency inference.
- Standard APIs: Exposes OpenAI-compatible API endpoints.
- NGC Catalog: Packaged and ready to deploy from NVIDIA's NGC catalog.
Docker Deployment:
NIMs are designed for Docker and can be pulled from the NGC container registry.
docker run --gpus all -it --rm -p 8000:8000 nvcr.io/nvidia/nim/meta-llama3-8b-instruct:latest
7. Fast-GPT
Fast-GPT is an open-source project focused on providing a high-performance API for GPT models. It includes features like load balancing and a unified API but has a narrower focus than more comprehensive gateways.
Best for: Teams primarily using OpenAI models that need a simple, self-hosted proxy for basic load balancing and key management.
Key Features:
- OpenAI Focus: Optimized for OpenAI API compatibility.
- Load Balancing: Can distribute requests across multiple OpenAI keys.
- Streaming Support: Efficiently handles streaming responses.
Docker Deployment:
A Docker image is available for easy deployment.
docker run -d --name fast-gpt -p 8080:3000 laisky/fast-gpt:latest
8. LLM-Proxy
LLM-Proxy is another open-source project that provides a simple proxy server for LLMs. It offers basic features like request logging and key management.
Best for: Hobbyists and small projects needing a minimal proxy to hide API keys and log basic request data.
Key Features:
- Lightweight: Minimal resource footprint.
- Simple Setup: Easy to configure and run.
- Request Logging: Basic visibility into API calls.
Docker Deployment:
It can be run via a simple Docker command.
docker run -d -p 7860:7860 -e OPENAI_API_KEY="your-key" solidrust/llm-proxy:latest
9. AI-Gateway (by Microsoft)
Microsoft's AI-Gateway is an open-source project that enables resilient and scalable access to OpenAI services, including Azure OpenAI. It's designed to handle retries, token-based load balancing, and priority routing.
Best for: Organizations heavily invested in the Azure ecosystem and using Azure OpenAI Service.
Key Features:
- Azure OpenAI Integration: First-class support for Azure AD authentication and Azure services.
- Token-Aware Load Balancing: Distributes load based on available tokens-per-minute (TPM).
- Health Probes: Actively monitors the health of backend endpoints.
Docker Deployment:
The project provides a Dockerfile for building and running the gateway.
docker run -d -p 8099:8080 -e "OpenAIService:Endpoint=https://your-aoai.openai.azure.com" mcr.microsoft.com/aigateway:latest
10. Ollama
Ollama is not a gateway itself, but a tool for running open-source LLMs locally. By deploying Ollama in a Docker container, teams can create a private, self-hosted model endpoint. When placed behind a reverse proxy or another gateway, it becomes a crucial part of a self-hosted AI infrastructure.
Best for: Teams wanting to run open-source models like Llama 3 or Mistral on their own infrastructure with a simple, standardized API.
Key Features:
- Simple Local LLMs: Makes it incredibly easy to download and run popular open-source models.
- REST API: Exposes a simple API for generation and chat.
- GPU Acceleration: Automatically utilizes available GPUs for faster inference.
Docker Deployment:
Ollama provides an official Docker image for running the server.
docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
How the Options Compare
| Gateway | Primary Use Case | Open Source | Reliability | Governance |
|---|---|---|---|---|
| Bifrost | Enterprise, Mission-Critical | Yes | High (Failover, LB) | High (Virtual Keys, Budgets) |
| LiteLLM | Development, Unified API | Yes | Basic (Retries) | Minimal |
| Kong AI Gateway | Existing Kong Users | No | High | High (Via Plugins) |
| Cloudflare | Caching & Analytics | No | High (Managed) | Basic (Rate Limits) |
| OpenRouter | Model Experimentation | No | High (Managed) | Minimal |
| NVIDIA NIM | Self-Hosted Model Serving | No | High (Optimized) | N/A |
| Fast-GPT | OpenAI Proxy | Yes | Basic (LB) | Minimal |
| LLM-Proxy | Hobbyist Proxy | Yes | Minimal | Minimal |
| AI-Gateway (MS) | Azure OpenAI Users | Yes | High (Retries, LB) | Basic |
| Ollama | Local Model Serving | Yes | N/A | N/A |
Recommendation
For teams building production AI applications, an AI gateway is a non-negotiable piece of infrastructure. While lightweight proxies are useful for development, production workloads demand robust reliability, security, and governance features.
Based on this analysis, Bifrost stands out as the most comprehensive solution for teams that prioritize performance and enterprise-grade features. Its low latency, automatic failover, and detailed governance controls make it the top choice for deploying scalable and resilient AI services with Docker.
Teams evaluating AI gateways can request a Bifrost demo or review the open-source repository to learn more.
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