PromptUnit

AI inference proxy that automatically routes requests to the cheapest model meeting quality requirements while cutting costs by 40–70%.

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About PromptUnit

PromptUnit is an AI inference proxy designed to reduce AI spending without sacrificing quality or introducing risk. It sits between applications and model providers, automatically routing each request to the least expensive model that meets a user-defined quality threshold. The tool provides real-time cost visibility, breaking down spend by model, feature, and user segment, enabling teams to identify and optimize inefficient usage patterns. PromptUnit includes quality regression alerts that monitor response quality in real time and circuit breaker controls to limit or block anomalous spend spikes. It supports 10 major model providers and requires only a one-line code change to integrate, making it accessible for teams running large-scale AI workloads seeking predictable, optimized expenses. The service offers a 14-day observation period with pay-only-on-savings pricing, ensuring users only pay when they see verified cost reductions.

Key features

AI-powered request classification
Quality benchmarking across models
Prompt compression to reduce token usage
Token-inflation defense to prevent overbilling
Multi-model routing to cheapest qualifying model
Per-call cost and usage visibility
Billing attack prevention
14-day observation period
Pay-only-on-savings pricing model
Zero-risk trial with verified savings

Use cases

Optimizing LLM inference costs for production applications
Preventing runaway cloud bills from billing attacks
Benchmarking and comparing model performance and pricing

Pros

Automatically routes requests to the least expensive model that meets quality requirements, reducing AI inference costs by 40-70%
Provides real-time cost visibility and breakdowns by model, feature, and user segment without requiring code changes
Includes quality regression alerts and circuit breaker controls to prevent billing spikes and maintain performance
Offers a 14-day observation period with pay-only-on-savings pricing (20% of verified savings)
Supports 10 major model providers (OpenAI, Anthropic, Google, Groq, DeepSeek, Mistral, Together, Perplexity, xAI, Cohere)

Cons

Requires switching to a proxy-based architecture, which may introduce slight latency (median 41ms added)
Savings depend on traffic mix and quality thresholds, which may not align with all use cases

Frequently asked questions about PromptUnit

What is PromptUnit and how does it work?

PromptUnit is an AI inference proxy that routes each request to the cheapest model meeting your quality threshold. It sits between your application and model providers, requiring only a base URL change to integrate. The tool logs every call, provides real-time cost analytics, and automatically optimizes routing to reduce spend.

Who should use PromptUnit?

PromptUnit is designed for teams running large-scale AI workloads who want to reduce inference costs without sacrificing output quality or introducing risk. It suits organizations using multiple model providers and seeking better cost visibility and control.

How does PromptUnit's pricing model work?

PromptUnit operates on a pay-only-on-savings model, taking 20% of verified savings. There are no subscriptions, flat fees, or upfront costs. Users can start with a free audit and cancel anytime without contracts.

Does PromptUnit support all major AI model providers?

Yes, PromptUnit supports 10 major model providers, including OpenAI, Anthropic, Google, Groq, DeepSeek, Mistral, Together, Perplexity, xAI, and Cohere. It works with existing API keys and endpoints without requiring SDK changes.

Can PromptUnit help prevent billing attacks or unexpected cost spikes?

Yes, PromptUnit includes circuit breaker controls that allow users to set hourly and daily spend limits, define spike thresholds, and choose to auto-downgrade or block anomalous traffic before costs reach the provider.

How do I get started with PromptUnit?

Getting started involves connecting your provider API keys, swapping your base URL, and running in observation mode for 14 days to see savings forecasts. After review, users can enable smart routing with one click, with no further code changes required.