Build
Multi-Model
AI
Without
the
Complexity.
PureRouter is the developer-first AI router that dynamically selects the most cost-effective LLM, with zero loss in performance.
Build Multi-Model AI Without the Complexity.
PureRouter is the developer-first AI router that dynamically selects the most cost-effective LLM, with zero loss in performance.
Automatically select the best LLMs based on cost, latency, and output, save up to 64% without sacrificing performance.
Integrate in minutes with Python, test in a built-in playground, and connect any model, no lock-in, no headaches.
From transparent billing to smart routing and upcoming custom model support, PureRouter grows with your stack.
Automatically select the best LLMs based on cost, latency, and output, save up to 64% without sacrificing performance.
Integrate in minutes with Python, test in a built-in playground, and connect any model, no lock-in, no headaches.
From transparent billing to smart routing and upcoming custom model support, PureRouter grows with your stack.
Rethink How You Use LLMs.
Whether you’re scaling your AI stack or optimizing a single use case, PureRouter lets you make smarter choices without rewriting your codebase.
Rethink How You Use LLMs.
Whether you’re scaling your AI stack or optimizing a single use case, PureRouter lets you make smarter choices without rewriting your codebase.
Why Choose
PureRouter?
Because building with AI shouldn’t mean choosing between cost, speed, and performance. PureRouter gives you the freedom to route based on what you value, all in a single, developer-friendly interface.
Why Choose PureRouter?
Because building with AI shouldn’t mean choosing between cost, speed, and performance. PureRouter gives you the freedom to route based on what you value, all in a single, developer-friendly interface.
Pay only what
you use
You heard right, PureRouter works with a credit system, so NO subscription traps and NO surprise bills. Just usage-based pricing that makes sense.
Pay only what
you use
You heard right, PureRouter works with a credit system, so NO subscription traps and NO surprise bills. Just usage-based pricing that makes sense.
Designed for Scalable Multi-Model AI Workflows
Open Source Model Deployment, Simplified
Our streamlined Deployment Setup screen supports rapid integration of open-source LLMs.
Coming soon: bring-your-own-model support, enabling you to deploy custom models directly into your routing stack.
Multi-Model Workflows
Easily build dynamic AI pipelines by selecting from a growing list of LLMs.
Configuring routing across multiple models intuitively, allowing you to balance cost, latency, and accuracy based on your own logic.
Performance
That Pays Off
Save between
on AI inference costs, without sacrificing performance.
Real-Time Testing, Future-Saving Insights
Quickly test prompts across multiple models and routing modes, choose between speed, quality, or balance and compare latency and cost instantly.
Coming soon: usage-based forecasting and visual comparisons to show how much you’ve saved with PureRouter, including monthly estimates, performance trends, and clear savings like “You saved $X.XX” alongside latency and cost breakdowns per model.
Designed for Scalable Multi-Model AI Workflows
Open Source Model Deployment, Simplified
Our streamlined Deployment Setup screen supports rapid integration of open-source LLMs.
Coming soon: bring-your-own-model support, enabling you to deploy custom models directly into your routing stack.
Multi-Model Workflows
Easily build dynamic AI pipelines by selecting from a growing list of LLMs.
Coming soon: bring-your-own-model support, enabling you to deploy custom models directly into your routing stack.
Performance That Pays Off
Save between
60% and 64%
on AI inference costs, without sacrificing performance.
Real-Time Testing, Future-Saving Insights
Quickly test prompts across multiple models and routing modes, choose between speed, quality, or balance and compare latency and cost instantly.
Coming soon: usage forecasting and visual comparisons to show savings, with monthly estimates, performance trends, and per-model cost and latency breakdowns.
Several models
and more on the way...
Several models
and more on the way...
Get started in minutes
No setup overhead. Install with pip, define your routing rules or plug into existing ones, and connect your models. PureRouter is designed for speed in development and in production.
Quick start videos
Get started in minutes
No setup overhead. Install with pip, define your routing rules or plug into existing ones, and connect your models. PureRouter is designed for speed in development and in production.
Quick start videos
Questions? You'll find the answers here
How does routing work?
PureRouter is an AI routing layer. It takes your input and automatically sends it to the best available LLM based on your defined logic (or default rules). It’s built to optimize cost, performance, or both.
Can I use my own models?
Not yet, but it’s on the roadmap! You’ll soon be able to bring and deploy your own model into the router environment.
What models are supported today?
PureRouter currently supports a wide range of proprietary and open-source models, and we’re actively implementing more. The current list is:
– jamba-instruct-en
– jamba-large-en
– jamba-mini-en
– llama3.1-8b
– llama-3.3-70b
– qwen-3-32b
– command-r-plus
– command-r
– command
– command-light
– deepseek-r1-distill-llama-70b
– llama-3.3-70b-versatile
– llama-3.3-70b-specdec
– llama-guara-4-12b
– llama2-70b-4096
– llama3-8b-8192
– llama-3.2-3b-preview
– llama-3.2-11b-text-preview
– llama-3.2-90b-text-preview
– llama3-70b-8192
– llama-3.1-8b-instant
– llama-3.3-70b-versatile
– meta-llama/llama-4-scout-17b-16e-instruct
– meta-llama/llama-4-maverick-17b-128e-instruct
– mistral-saba-24b
– gemma2-9b-it
– moonshotai/kimi-k2-instruct
– Meta-Llama-3.1-8B-Instruct
– Meta-Llama-3.1-405B-Instruct
– Llama-4-Maverick-17B-128E-Instruct
– Meta-Llama-3.3-70B-Instruct
– Qwen3-32B
– DeepSeek-R1-Distill-Llama-70B
– DeepSeek-R1
– DeepSeek-V3-0324
– meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo
– meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo
– meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo
– meta-llama/Llama-3.3-70B-Instruct-Turbo
– meta-llama/Llama-3.3-70B-Instruct-Turbo-Free
– mistralai/Mixtral-8x7B-Instruct-v0.1
– mistralai/Mistral-7B-Instruct-v0.1
– meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8
– meta-llama/Llama-4-Scout-17B-16E-Instruct
– meta-llama/Llama-3.2-3B-Instruct-Turbo
– Qwen/Qwen2.5-7B-Instruct-Turbo
– Qwen/Qwen2.5-72B-Instruct-Turbo
– deepseek-ai/DeepSeek-V3
– deepseek-ai/DeepSeek-R1
– mistralai/Mistral-Small-24B-Instruct-2501
– moonshotai/Kimi-K2-Instruct
– gpt-4-0613
– gpt-4
– gpt-3.5-turbo
– davinci-002
– babbage-002
– gpt-3.5-turbo-instruct
– gpt-3.5-turbo-instruct-0914
– gpt-4-1106-preview
– gpt-3.5-turbo-1106
– gpt-4-0125-preview
– gpt-4-turbo-preview
– gpt-3.5-turbo-0125
– gpt-4-turbo
– gpt-4-turbo-2024-04-09
– gpt-4o
– gpt-4o-2024-05-13
– gpt-4o-mini-2024-07-18
– gpt-4o-mini
– gpt-4o-2024-08-06
– gpt-4o-2024-11-20
– gpt-4o-search-preview-2025-03-11
– gpt-4o-search-preview
– gpt-4o-mini-search-preview-2025-03-11
– gpt-4o-mini-search-preview
– gpt-4.1-2025-04-14
– gpt-4.1
– gpt-4.1-mini-2025-04-14
– gpt-4.1-mini
– gpt-4.1-nano-2025-04-14
– gpt-4.1-nano
– gpt-3.5-turbo-16k
– claude-opus-4-20250514
– claude-sonnet-4-20250514
– claude-3-7-sonnet-20250219
– claude-3-5-sonnet-20241022
– claude-3-5-haiku-20241022
– claude-3-5-sonnet-20240620
– claude-3-haiku-20240307
– claude-3-opus-20240229
– deepseek-reasoner
– deepseek-chat
– deepseek-r1
– deepseek-v3
– deepseek-coder
– mistral-tiny
– mistral-small
– mistral-small-latest
– mistral-medium
– mistral-medium-latest
– mistral-medium-2505
– mistral-medium-2312
– mistral-large-latest
– mistral-large-2411
– mistral-large-2402
– mistral-large-2407
– pixtral-large-latest
– pixtral-large-2411
– pixtral-12b-2409
– open-mistral-7b
– open-mixtral-8x7b
– open-mixtral-8x22b
– codestral-latest
– codestral-2405
– open-mistral-nemo
– open-mistral-nemo-2407
– devstral-small-2505
– devstral-small-2507
– devstral-medium-2507
– magistral-medium-latest
– magistral-medium-2506
– magistral-small-latest
– magistral-small-2506
– sonar
– sonar-pro
– sonar
– sonar-pro
– sonar
– sonar
– sonar-pro
– sonar-pro
– sonar
– sonar-pro
– sonar-reasoning
– sonar-reasoning
– sonar-deep-research
– gemini-1.5-flash-latest
– gemini-1.5-flash
– gemini-1.5-flash-002
– gemini-1.5-flash-8b
– gemini-1.5-flash-8b-001
– gemini-1.5-flash-8b-latest
– gemini-2.5-flash-preview-05-20
– gemini-2.5-flash
– gemini-2.5-flash-lite-preview-06-17
– gemini-2.0-flash-exp
– gemini-2.0-flash
– gemini-2.0-flash-001
– gemini-2.0-flash-thinking-exp-01-21
– gemini-2.0-flash-thinking-exp
– gemini-2.0-flash-thinking-exp-1219
– gemma-3-1b-it
– gemma-3-4b-it
– gemma-3-12b-it
– gemma-3-27b-it
– gemma-3n-e4b-it
– gemma-3n-e2b-it
– qwen/qwen3-32b
How are credits calculated?
?
Is PureRouter open-source?
PureRouter is not currently open-source, but we’re considering hybrid models. PureCPP, our first tool, is fully open and will integrate nicely.
How do I deploy to production?
PureRouter is production-ready out of the box. It works using a bring-your-own-key (BYOK) model, meaning you can connect your own API keys from providers like OpenAI, Mistral, Cohere, and others.
From the dashboard, you can access the Access Keys section, where you can:
Add and manage your PureRouter API keys;
Set credit limits and usage restrictions for each key;
Create separate keys for different environments (e.g., staging and production) or internal users.
This setup gives you full flexibility, usage control, and an easy path to securely integrate PureRouter into your production environment.
Latest updates
stay up to date with our latest news, events and announcements.
- AI Infrastructure, Developer Tools, Open Source
- AI Infrastructure, Developer Tools, Machine Learning, Open Source, Technical Deep Dives