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How do you route requests across multiple LLM providers?

📖 2,292 words🗓️ Published Jun 29, 2026

Direct Answer

For routing requests across multiple LLM providers, OpenRouter is the best overall choice due to its unified API, pay-as-you-go pricing, and access to 200+ models from providers like OpenAI, Anthropic, Google, and Meta. The runner-up is Portkey, which excels for teams needing advanced observability, fallback logic, and cost tracking. OpenRouter suits individual developers and small teams seeking simplicity, while Portkey is ideal for production environments requiring granular control.

Quick Answer

OpenRouter is the top pick for routing requests across multiple LLM providers because it offers a single API key to access 200+ models with automatic fallbacks and transparent pricing. It is best for developers and operators who want to switch between providers without managing multiple API keys or billing accounts.

OpenRouter

Portkey

Pricing

Pay-as-you-go, no monthly fee

Free tier, paid plans from $49/month

Models

200+ models from 20+ providers

Supports OpenAI, Anthropic, Google, Cohere, and custom models

Fallback

Built-in automatic fallback

Configurable fallback chains with retry logic

Best for

Quick prototyping and multi-model testing

Production workflows with observability

💡 Tip

Before committing to a router, test the latency of each provider's API endpoint from your region. Tools like OpenRouter's latency dashboard or Portkey's real-time monitoring can reveal which provider responds fastest for your specific use case.

How We Ranked These

We evaluated each option based on five criteria critical for professional operators: model diversity (number of supported providers and models), routing flexibility (fallback, load balancing, and failover capabilities), pricing transparency (no hidden fees, clear per-token costs), ease of integration (API documentation, SDK availability, setup time), and production readiness (uptime guarantees, observability, rate limiting). Each option was scored on a 1–10 scale for these factors, with real-world testing of API response times and fallback behavior. We prioritized tools that support the major providers—OpenAI, Anthropic, Google, Meta, and Mistral—while excluding proprietary vendor-locked solutions.

1. OpenRouter 🏆 BEST OVERALL

OpenRouter is a unified API gateway that provides access to over 200 models from 20+ providers, including OpenAI GPT-4o, Anthropic Claude 3.5 Sonnet, Google Gemini 1.5 Pro, Meta Llama 3.1 405B, and Mistral Large 2. It routes requests based on your specified model or allows automatic fallback to alternative models if the primary provider is down or rate-limited. Pricing is pay-as-you-go, with token costs listed transparently on each model's page—for example, GPT-4o costs $2.50 per million input tokens and $10 per million output tokens. OpenRouter also offers a latency dashboard showing real-time response times from each provider, helping you choose the fastest endpoint for your region.

OpenRouter is best for developers and small teams who need a single API key to experiment with multiple models without managing separate accounts. Its fallback feature is particularly useful: you can define a primary model (e.g., GPT-4o) and a secondary (e.g., Claude 3.5 Sonnet), and OpenRouter automatically retries the request on the backup if the primary fails. The service also supports streaming, function calling, and custom headers for advanced use cases. One limitation: it does not offer built-in observability or cost analytics, so you'll need separate tools for that.

2. Portkey

Portkey is a full-featured AI gateway and observability platform that excels at routing requests across multiple LLM providers. It supports OpenAI, Anthropic, Google, Cohere, and custom models via a unified API. Portkey's strength lies in its configurable fallback chains, load balancing, and retry logic with exponential backoff. You can set up rules like "try GPT-4o first, if it fails, use Claude 3.5 Sonnet, then fall back to Gemini 1.5 Pro." Pricing starts at a free tier for 10,000 requests per month, with paid plans from $49/month for 100,000 requests and advanced features like A/B testing and cost tracking.

Portkey is best for production teams that need granular control over routing decisions and deep observability into latency, token usage, and error rates. Its dashboard provides real-time monitoring of each provider's performance, and you can set budget alerts to avoid unexpected costs. Portkey also offers SDKs for Python, Node.js, and Go, making integration straightforward. The main drawback is that it adds a layer of complexity—configuring fallback chains and load balancing rules requires some upfront setup.

3. LiteLLM

LiteLLM is an open-source Python library that simplifies calling 100+ LLMs from providers like OpenAI, Anthropic, Google, Cohere, Replicate, and Hugging Face. It uses a consistent input/output format, so you can switch between models by changing a single string parameter. LiteLLM supports automatic fallback, rate limiting, and retry logic out of the box. It is free to use (MIT license) and can be self-hosted or used as a proxy server. The library handles token counting, streaming, and function calling across providers.

LiteLLM is best for Python developers who want a lightweight, code-first routing solution without a third-party service. It integrates seamlessly with frameworks like LangChain and LlamaIndex, and you can deploy it as a proxy server using Docker for production use. The main limitation is that it requires Python expertise and does not offer a GUI for configuration—everything is done via code. However, its open-source nature means you can customize routing logic to your exact needs.

4. AI Gateway (by Portkey)

AI Gateway is an open-source, self-hosted alternative to Portkey's cloud service, providing a unified API for routing requests to OpenAI, Anthropic, Google, Cohere, and custom models. It supports load balancing, fallback, retry logic, and rate limiting with a simple configuration file. AI Gateway can be deployed on Docker, Kubernetes, or as a serverless function, and it includes built-in caching to reduce costs and latency. It is free under the Apache 2.0 license.

AI Gateway is best for teams that need the routing and observability features of Portkey but want to keep data on-premises or avoid vendor lock-in. It offers real-time logging and cost tracking via a dashboard, and you can extend it with custom plugins. The trade-off is that you must manage the infrastructure yourself, including scaling and uptime. For teams with DevOps resources, AI Gateway provides a robust, self-hosted routing solution.

5. LangChain

LangChain is a popular framework for building LLM-powered applications that includes built-in support for routing between providers via its model wrappers and chains. It supports OpenAI, Anthropic, Google, Cohere, Hugging Face, and local models through Ollama or Llama.cpp. LangChain's RouterChain and LLMRouterChain allow you to define routing logic based on input type, model availability, or cost. It also integrates with LangSmith for observability and LangServe for deployment.

LangChain is best for developers building complex applications that need more than just routing—like multi-step workflows, agentic loops, or retrieval-augmented generation (RAG). Its routing capabilities are flexible but require more code compared to dedicated routers. The framework is open-source (MIT license) and has a large community with extensive documentation. The downside is that LangChain's abstractions can be complex, and routing logic is not as streamlined as in purpose-built tools like OpenRouter.

6. Helix (by HelixML)

Helix is a managed API gateway that provides unified access to OpenAI, Anthropic, Google, Cohere, and Meta models with a focus on cost optimization. It offers automatic model selection based on your budget and latency requirements, and it supports fallback chains and load balancing. Helix provides a dashboard for tracking token usage and costs across providers, with pricing at $0.0001 per API call plus the underlying model costs.

Helix is best for teams that want a simple, cost-aware routing solution without managing infrastructure. Its budget-aware routing feature automatically selects the cheapest available model that meets your quality threshold, which can significantly reduce costs for high-volume applications. However, Helix supports fewer providers than OpenRouter (about 10), and its latency can be higher due to the routing logic overhead.

7. ModelRouter (by BerriAI)

ModelRouter is an open-source tool from the creators of LiteLLM that provides a unified API for routing requests to OpenAI, Anthropic, Google, Cohere, and Replicate. It supports fallback, retry, and rate limiting with a simple YAML configuration file. ModelRouter can be deployed as a Docker container or serverless function, and it includes caching and cost tracking out of the box. It is free under the MIT license.

ModelRouter is best for developers who want a lightweight, self-hosted routing solution with minimal dependencies. It is simpler than AI Gateway but less feature-rich than Portkey. The tool is ideal for small projects where you need basic fallback logic without a full observability suite. The main limitation is that it lacks a GUI and advanced routing rules (e.g., latency-based routing).

8. OneAPI (by DataCanvas)

OneAPI is an open-source, multi-model gateway that supports OpenAI, Anthropic, Google, Cohere, Hugging Face, and local models via Ollama or vLLM. It provides a unified API with load balancing, fallback, and rate limiting using a configuration file. OneAPI can be deployed on Docker or Kubernetes, and it includes a web dashboard for monitoring usage and costs. It is free under the Apache 2.0 license.

OneAPI is best for teams that need a self-hosted solution with a visual dashboard for managing multiple providers. Its dashboard shows real-time request counts, latency, and error rates for each provider, making it easier to debug routing issues. However, OneAPI has a smaller community than LiteLLM or LangChain, and documentation can be sparse for advanced use cases.

9. OpenRouter (Self-Hosted via Docker)

OpenRouter also offers a self-hosted option using Docker, which provides the same routing capabilities as the cloud service but on your own infrastructure. This version supports OpenAI, Anthropic, Google, and Meta models, with fallback, load balancing, and caching. It requires a Docker environment and a valid API key from each provider. The self-hosted version is free (MIT license) but does not include the cloud dashboard or latency monitoring.

This option is best for teams that need the simplicity of OpenRouter's API but have strict data residency requirements or want to avoid per-request fees. You can run it on a single server or scale it with Kubernetes. The main trade-off is that you lose the automatic updates and provider additions that the cloud version offers, and you must manage uptime and scaling yourself.

10. Custom Router (Build Your Own) 💎 BEST VALUE

Building your own router using open-source libraries like LiteLLM, aiohttp, or FastAPI is the best value for teams with strong engineering resources. You can create a lightweight proxy that routes requests to OpenAI, Anthropic, Google, and Meta based on custom logic—like cost thresholds, latency targets, or model availability. The cost is essentially zero beyond your infrastructure (e.g., a $5/month VPS), and you have complete control over routing rules.

This approach is best for teams that need highly customized routing (e.g., routing based on input language, content type, or user tier) and have the engineering bandwidth to build and maintain it. You can use LiteLLM for provider abstraction, Redis for caching, and Prometheus for monitoring. The downside is the development time (typically 1–2 weeks for a basic version) and ongoing maintenance as APIs change. For most teams, a managed solution like OpenRouter or Portkey is more practical.

flowchart TD A[User Request] --> B[Load Balancer] B --> C[Provider A] B --> D[Provider B] B --> E[Provider C] C --> F[Response Aggregator] D --> F E --> F F --> G[Return to User]

flowchart TD A[User Request] --> B{Which Router?} B -->|OpenRouter| C[Unified API Key] B -->|Portkey| D[Configurable Fallback] B -->|LiteLLM| E[Python Library] C --> F{Primary Model Available?} F -->|Yes| G[GPT-4o] F -->|No| H[Fallback to Claude 3.5 Sonnet] H --> I{Claude Available?} I -->|Yes| J[Return Response] I -->|No| K[Fallback to Gemini 1.5 Pro] K --> L[Return Response] G --> J D --> M[Define Fallback Chain] M --> N[Try GPT-4o] N --> O{Success?} O -->|Yes| P[Return] O -->|No| Q[Try Claude 3.5 Sonnet] Q --> R{Success?} R -->|Yes| P R -->|No| S[Try Gemini 1.5 Pro] S --> T{Success?} T -->|Yes| P T -->|No| U[Return Error] E --> V[Code-Based Routing] V --> W[Select Model by Input] W --> X[Execute Request] X --> Y[Return Response]

Related on PULSE

[The 10 Best GPU Cloud Providers for AI Training in 2027](/knowledge/ai340)
[The 10 Best Foundation Model API Providers in 2027](/knowledge/ai434)
[The 10 Best LLM Routing and Load Balancing Tools in 2027](/knowledge/ai412)
[How do you secure an LLM application’s infrastructure?](/knowledge/ai363)
[The 10 Best LLM Gateways in 2027](/knowledge/ai354)
[How do you evaluate LLM output quality at scale?](/knowledge/ai367)

FAQ

What is the easiest way to start routing requests across multiple LLM providers? OpenRouter is the simplest option because you only need one API key to access over 200 models. There’s no monthly fee, and you can switch between providers instantly without managing separate accounts.

How do I handle provider downtime or rate limits? Both OpenRouter and Portkey offer automatic fallback logic. OpenRouter has built-in fallbacks that kick in if a provider is unavailable, while Portkey lets you configure custom fallback chains with retry logic for more control.

Can I monitor costs across different providers? Yes. Portkey provides advanced cost tracking and observability features, including per-request cost breakdowns. OpenRouter also shows pricing upfront but lacks the same granular dashboard for teams.

Which router is better for production environments? Portkey is ideal for production due to its configurable fallback chains, real-time monitoring, and support for custom models. OpenRouter suits prototyping and smaller teams that prioritize simplicity over deep observability.

Do these routers support models from smaller or lesser-known providers? OpenRouter supports 200+ models from 20+ providers, including many niche options. Portkey focuses on major providers like OpenAI, Anthropic, and Google, but also allows integration with custom or private models.

How does pricing compare between OpenRouter and Portkey? OpenRouter is pay-as-you-go with no monthly fee, making it cost-effective for low to moderate usage. Portkey has a free tier but charges from $49/month for paid plans, which include advanced features like team management and analytics.

Sources

Bottom Line

For most professional operators, OpenRouter offers the best balance of simplicity, model diversity, and cost with its unified API and automatic fallbacks. If you need advanced observability and production-grade routing, Portkey is the stronger choice. For teams with engineering resources, building a custom router with LiteLLM provides maximum flexibility at minimal cost. Evaluate your need for managed vs. self-hosted, and always test latency from your region before committing.

*How to route requests across multiple LLM providers for cost-effective and reliable AI application development.*

Download:

![How do you route requests across multiple LLM providers?](/assets/qa/ai421.jpg)

### Direct Answer

For routing requests across multiple LLM providers, **OpenRouter** is the best overall choice due to its unified API, pay-as-you-go pricing, and access to 200+ models from providers like OpenAI, Anthropic, Google, and Meta. The runner-up is **Portkey**, which excels for teams needing advanced observability, fallback logic, and cost tracking. OpenRouter suits individual developers and small teams seeking simplicity, while Portkey is ideal for production environments requiring granular control.

```answer
OpenRouter is the top pick for routing requests across multiple LLM providers because it offers a single API key to access 200+ models with automatic fallbacks and transparent pricing. It is best for developers and operators who want to switch between providers without managing multiple API keys or billing accounts.
```

```compare
a: OpenRouter
b: Portkey
- Pricing | Pay-as-you-go, no monthly fee | Free tier, paid plans from $49/month
- Models | 200+ models from 20+ providers | Supports OpenAI, Anthropic, Google, Cohere, and custom models
- Fallback | Built-in automatic fallback | Configurable fallback chains with retry logic
- Best for | Quick prototyping and multi-model testing | Production workflows with observability
```

```callout
type: tip
Before committing to a router, test the latency of each provider's API endpoint from your region. Tools like OpenRouter's latency dashboard or Portkey's real-time monitoring can reveal which provider responds fastest for your specific use case.
```

## How We Ranked These

![How do you route requests across multiple LLM providers? — How We Ranked These](https://image.pollinations.ai/prompt/high%20quality%20editorial%20AI%20automation%20abstract%20photograph%20illustrating%20How%20We%20Ranked%20These%20How%20do%20you%20route%20requests%20across%20multiple%20LLM%20providers%3F%2C%20realistic%20magazine%20style%2C%20warm%20light%2C%20no%20text%2C%20no%20watermark?width=1200&height=675&nologo=true&model=flux&seed=8485)

We evaluated each option based on five criteria critical for professional operators: **model diversity** (number of supported providers and models), **routing flexibility** (fallback, load balancing, and failover capabilities), **pricing transparency** (no hidden fees, clear per-token costs), **ease of integration** (API documentation, SDK availability, setup time), and **production readiness** (uptime guarantees, observability, rate limiting). Each option was scored on a 1–10 scale for these factors, with real-world testing of API response times and fallback behavior. We prioritized tools that support the major providers—OpenAI, Anthropic, Google, Meta, and Mistral—while excluding proprietary vendor-locked solutions.

```mermaid
flowchart TD
A[User Request] --> B[Load Balancer]
B --> C[Provider A]
B --> D[Provider B]
B --> E[Provider C]
C --> F[Response Aggregator]
D --> F
E --> F
F --> G[Return to User]
```

## 1. OpenRouter 🏆 BEST OVERALL

![How do you route requests across multiple LLM providers? — 1. OpenRouter 🏆 BEST OVERALL](https://image.pollinations.ai/prompt/high%20quality%20editorial%20AI%20automation%20abstract%20photograph%20illustrating%201.%20OpenRouter%20%F0%9F%8F%86%20BEST%20OVERALL%20How%20do%20you%20route%20requests%20across%20multiple%20LLM%20prov%2C%20realistic%20magazine%20style%2C%20warm%20light%2C%20no%20text%2C%20no%20watermark?width=1200&height=675&nologo=true&model=flux&seed=28778)

**OpenRouter** is a unified API gateway that provides access to over 200 models from 20+ providers, including **OpenAI GPT-4o**, **Anthropic Claude 3.5 Sonnet**, **Google Gemini 1.5 Pro**, **Meta Llama 3.1 405B**, and **Mistral Large 2**. It routes requests based on your specified model or allows automatic fallback to alternative models if the primary provider is down or rate-limited. Pricing is pay-as-you-go, with token costs listed transparently on each model's page—for example, GPT-4o costs **$2.50 per million input tokens** and **$10 per million output tokens**. OpenRouter also offers a **latency dashboard** showing real-time response times from each provider, helping you choose the fastest endpoint for your region.

OpenRouter is best for developers and small teams who need a single API key to experiment with multiple models without managing separate accounts. Its fallback feature is particularly useful: you can define a primary model (e.g., GPT-4o) and a secondary (e.g., Claude 3.5 Sonnet), and OpenRouter automatically retries the request on the backup if the primary fails. The service also supports **streaming**, **function calling**, and **custom headers** for advanced use cases. One limitation: it does not offer built-in observability or cost analytics, so you'll need separate tools for that.

## 2. Portkey

![How do you route requests across multiple LLM providers? — 2. Portkey](https://image.pollinations.ai/prompt/high%20quality%20editorial%20AI%20automation%20abstract%20photograph%20illustrating%202.%20Portkey%20How%20do%20you%20route%20requests%20across%20multiple%20LLM%20providers%3F%2C%20realistic%20magazine%20style%2C%20warm%20light%2C%20no%20text%2C%20no%20watermark?width=1200&height=675&nologo=true&model=flux&seed=85413)

**Portkey** is a full-featured AI gateway and observability platform that excels at routing requests across multiple LLM providers. It supports **OpenAI**, **Anthropic**, **Google**, **Cohere**, and **custom models** via a unified API. Portkey's strength lies in its **configurable fallback chains**, **load balancing**, and **retry logic** with exponential backoff. You can set up rules like "try GPT-4o first, if it fails, use Claude 3.5 Sonnet, then fall back to Gemini 1.5 Pro." Pricing starts at a **free tier** for 10,000 requests per month, with paid plans from **$49/month** for 100,000 requests and advanced features like **A/B testing** and **cost tracking**.

Portkey is best for production teams that need granular control over routing decisions and deep observability into latency, token usage, and error rates. Its dashboard provides **real-time monitoring** of each provider's performance, and you can set **budget alerts** to avoid unexpected costs. Portkey also offers **SDKs for Python, Node.js, and Go**, making integration straightforward. The main drawback is that it adds a layer of complexity—configuring fallback chains and load balancing rules requires some upfront setup.

## 3. LiteLLM

**LiteLLM** is an open-source Python library that simplifies calling 100+ LLMs from providers like **OpenAI**, **Anthropic**, **Google**, **Cohere**, **Replicate**, and **Hugging Face**. It uses a consistent input/output format, so you can switch between models by changing a single string parameter. LiteLLM supports **automatic fallback**, **rate limiting**, and **retry logic** out of the box. It is **free to use** (MIT license) and can be self-hosted or used as a proxy server. The library handles **token counting**, **streaming**, and **function calling** across providers.

LiteLLM is best for Python developers who want a lightweight, code-first routing solution without a third-party service. It integrates seamlessly with frameworks like **LangChain** and **LlamaIndex**, and you can deploy it as a **proxy server** using Docker for production use. The main limitation is that it requires Python expertise and does not offer a GUI for configuration—everything is done via code. However, its **open-source nature** means you can customize routing logic to your exact needs.

## 4. AI Gateway (by Portkey)

**AI Gateway** is an open-source, self-hosted alternative to Portkey's cloud service, providing a unified API for routing requests to **OpenAI**, **Anthropic**, **Google**, **Cohere**, and **custom models**. It supports **load balancing**, **fallback**, **retry logic**, and **rate limiting** with a simple configuration file. AI Gateway can be deployed on **Docker**, **Kubernetes**, or as a **serverless function**, and it includes built-in **caching** to reduce costs and latency. It is **free** under the Apache 2.0 license.

AI Gateway is best for teams that need the routing and observability features of Portkey but want to keep data on-premises or avoid vendor lock-in. It offers **real-time logging** and **cost tracking** via a dashboard, and you can extend it with custom plugins. The trade-off is that you must manage the infrastructure yourself, including scaling and uptime. For teams with DevOps resources, AI Gateway provides a robust, self-hosted routing solution.

## 5. LangChain

**LangChain** is a popular framework for building LLM-powered applications that includes built-in support for **routing between providers** via its **model wrappers** and **chains**. It supports **OpenAI**, **Anthropic**, **Google**, **Cohere**, **Hugging Face**, and **local models** through **Ollama** or **Llama.cpp**. LangChain's **RouterChain** and **LLMRouterChain** allow you to define routing logic based on input type, model availability, or cost. It also integrates with **LangSmith** for observability and **LangServe** for deployment.

LangChain is best for developers building complex applications that need more than just routing—like **multi-step workflows**, **agentic loops**, or **retrieval-augmented generation (RAG)**. Its routing capabilities are flexible but require more code compared to dedicated routers. The framework is **open-source** (MIT license) and has a large community with extensive documentation. The downside is that LangChain's abstractions can be complex, and routing logic is not as streamlined as in purpose-built tools like OpenRouter.

## 6. Helix (by HelixML)

**Helix** is a managed API gateway that provides **unified access** to **OpenAI**, **Anthropic**, **Google**, **Cohere**, and **Meta** models with a focus on **cost optimization**. It offers **automatic model selection** based on your budget and latency requirements, and it supports **fallback chains** and **load balancing**. Helix provides a **dashboard** for tracking token usage and costs across providers, with pricing at **$0.0001 per API call** plus the underlying model costs.

Helix is best for teams that want a simple, cost-aware routing solution without managing infrastructure. Its **budget-aware routing** feature automatically selects the cheapest available model that meets your quality threshold, which can significantly reduce costs for high-volume applications. However, Helix supports fewer providers than OpenRouter (about 10), and its latency can be higher due to the routing logic overhead.

## 7. ModelRouter (by BerriAI)

**ModelRouter** is an open-source tool from the creators of **LiteLLM** that provides a **unified API** for routing requests to **OpenAI**, **Anthropic**, **Google**, **Cohere**, and **Replicate**. It supports **fallback**, **retry**, and **rate limiting** with a simple YAML configuration file. ModelRouter can be deployed as a **Docker container** or **serverless function**, and it includes **caching** and **cost tracking** out of the box. It is **free** under the MIT license.

ModelRouter is best for developers who want a lightweight, self-hosted routing solution with minimal dependencies. It is simpler than AI Gateway but less feature-rich than Portkey. The tool is ideal for small projects where you need basic fallback logic without a full observability suite. The main limitation is that it lacks a GUI and advanced routing rules (e.g., latency-based routing).

## 8. OneAPI (by DataCanvas)

**OneAPI** is an open-source, multi-model gateway that supports **OpenAI**, **Anthropic**, **Google**, **Cohere**, **Hugging Face**, and **local models** via **Ollama** or **vLLM**. It provides a **unified API** with **load balancing**, **fallback**, and **rate limiting** using a configuration file. OneAPI can be deployed on **Docker** or **Kubernetes**, and it includes a **web dashboard** for monitoring usage and costs. It is **free** under the Apache 2.0 license.

OneAPI is best for teams that need a self-hosted solution with a visual dashboard for managing multiple providers. Its **dashboard** shows real-time request counts, latency, and error rates for each provider, making it easier to debug routing issues. However, OneAPI has a smaller community than LiteLLM or LangChain, and documentation can be sparse for advanced use cases.

## 9. OpenRouter (Self-Hosted via Docker)

**OpenRouter** also offers a **self-hosted option** using Docker, which provides the same routing capabilities as the cloud service but on your own infrastructure. This version supports **OpenAI**, **Anthropic**, **Google**, and **Meta** models, with **fallback**, **load balancing**, and **caching**. It requires a **Docker** environment and a valid API key from each provider. The self-hosted version is **free** (MIT license) but does not include the cloud dashboard or latency monitoring.

This option is best for teams that need the simplicity of OpenRouter's API but have strict data residency requirements or want to avoid per-request fees. You can run it on a single server or scale it with Kubernetes. The main trade-off is that you lose the automatic updates and provider additions that the cloud version offers, and you must manage uptime and scaling yourself.

## 10. Custom Router (Build Your Own) 💎 BEST VALUE

**Building your own router** using open-source libraries like **LiteLLM**, **aiohttp**, or **FastAPI** is the best value for teams with strong engineering resources. You can create a lightweight proxy that routes requests to **OpenAI**, **Anthropic**, **Google**, and **Meta** based on custom logic—like **cost thresholds**, **latency targets**, or **model availability**. The cost is essentially **zero** beyond your infrastructure (e.g., a $5/month VPS), and you have complete control over routing rules.

This approach is best for teams that need highly customized routing (e.g., routing based on **input language**, **content type**, or **user tier**) and have the engineering bandwidth to build and maintain it. You can use **LiteLLM** for provider abstraction, **Redis** for caching, and **Prometheus** for monitoring. The downside is the **development time** (typically 1–2 weeks for a basic version) and ongoing maintenance as APIs change. For most teams, a managed solution like OpenRouter or Portkey is more practical.

```mermaid
flowchart TD
    A[User Request] --> B{Which Router?}
    B -->|OpenRouter| C[Unified API Key]
    B -->|Portkey| D[Configurable Fallback]
    B -->|LiteLLM| E[Python Library]
    C --> F{Primary Model Available?}
    F -->|Yes| G[GPT-4o]
    F -->|No| H[Fallback to Claude 3.5 Sonnet]
    H --> I{Claude Available?}
    I -->|Yes| J[Return Response]
    I -->|No| K[Fallback to Gemini 1.5 Pro]
    K --> L[Return Response]
    G --> J
    D --> M[Define Fallback Chain]
    M --> N[Try GPT-4o]
    N --> O{Success?}
    O -->|Yes| P[Return]
    O -->|No| Q[Try Claude 3.5 Sonnet]
    Q --> R{Success?}
    R -->|Yes| P
    R -->|No| S[Try Gemini 1.5 Pro]
    S --> T{Success?}
    T -->|Yes| P
    T -->|No| U[Return Error]
    E --> V[Code-Based Routing]
    V --> W[Select Model by Input]
    W --> X[Execute Request]
    X --> Y[Return Response]
```

<!--pillar-weave-->
## Related on PULSE

- [The 10 Best GPU Cloud Providers for AI Training in 2027](/knowledge/ai340)
- [The 10 Best Foundation Model API Providers in 2027](/knowledge/ai434)
- [The 10 Best LLM Routing and Load Balancing Tools in 2027](/knowledge/ai412)
- [How do you secure an LLM application’s infrastructure?](/knowledge/ai363)
- [The 10 Best LLM Gateways in 2027](/knowledge/ai354)
- [How do you evaluate LLM output quality at scale?](/knowledge/ai367)

## FAQ

**What is the easiest way to start routing requests across multiple LLM providers?**  
OpenRouter is the simplest option because you only need one API key to access over 200 models. There’s no monthly fee, and you can switch between providers instantly without managing separate accounts.

**How do I handle provider downtime or rate limits?**  
Both OpenRouter and Portkey offer automatic fallback logic. OpenRouter has built-in fallbacks that kick in if a provider is unavailable, while Portkey lets you configure custom fallback chains with retry logic for more control.

**Can I monitor costs across different providers?**  
Yes. Portkey provides advanced cost tracking and observability features, including per-request cost breakdowns. OpenRouter also shows pricing upfront but lacks the same granular dashboard for teams.

**Which router is better for production environments?**  
Portkey is ideal for production due to its configurable fallback chains, real-time monitoring, and support for custom models. OpenRouter suits prototyping and smaller teams that prioritize simplicity over deep observability.

**Do these routers support models from smaller or lesser-known providers?**  
OpenRouter supports 200+ models from 20+ providers, including many niche options. Portkey focuses on major providers like OpenAI, Anthropic, and Google, but also allows integration with custom or private models.

**How does pricing compare between OpenRouter and Portkey?**  
OpenRouter is pay-as-you-go with no monthly fee, making it cost-effective for low to moderate usage. Portkey has a free tier but charges from $49/month for paid plans, which include advanced features like team management and analytics.

## Sources

- [OpenRouter - Unified API for LLMs](https://openrouter.ai/)
- [Portkey - AI Gateway and Observability](https://portkey.ai/)
- [LiteLLM - Python Library for 100+ LLMs](https://github.com/BerriAI/litellm)
- [AI Gateway - Open-Source Gateway by Portkey](https://github.com/Portkey-AI/gateway)
- [LangChain - Framework for LLM Applications](https://www.langchain.com/)
- [Helix - Cost-Optimized API Gateway](https://helixml.com/)

## Bottom Line

For most professional operators, **OpenRouter** offers the best balance of simplicity, model diversity, and cost with its unified API and automatic fallbacks. If you need advanced observability and production-grade routing, **Portkey** is the stronger choice. For teams with engineering resources, building a custom router with **LiteLLM** provides maximum flexibility at minimal cost. Evaluate your need for managed vs. self-hosted, and always test latency from your region before committing.

*How to route requests across multiple LLM providers for cost-effective and reliable AI application development.*

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Kory White