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AI spending represents a significant budget for all businesses these days. For many organizations, it’s also a growing and unpredictable budget, as the cost varies according to your teams’ usage of AI models. But there’s a new way to rein in this spend: instead of working only with the premium AI models like Claude or GPT-4o, now it’s easy to route your queries to the best model for that specific input. The cost savings are dramatic: the premium AI models cost up to 188 times more than smaller models for each prompt processed while often delivering only marginal improvements – especially for routine tasks.
In this article, we’ll explain how it’s now possible to ensure superior results from your AI models every time and at the lowest possible cost.
When choosing the best AI model, most businesses prioritize selecting one that applies to the largest number of their use cases while balancing quality and cost efficiency. However, no single model is ideal for every prompt.
A high-powered model may offer superior output quality for complex queries, but for more straightforward routine tasks, a user pays the high cost without getting any significant value-add. Meanwhile, smaller-sized models sometimes fail to effectively handle the most complex tasks.
Businesses have had to choose between performance and cost-efficiency, with no real middle ground.Until now. With intelligent model routing by Arcee AI, you no longer have to work with just one model.
Arcee Conductor is our intelligent model routing platform that automatically routes each input to the optimal language model. Rather than relying on a single AI model that performs inconsistently across different scenarios, Conductor dynamically routes input between large language models (LLMs) and small language models (SLMs), maximizing cost efficiency without compromising performance.
You can directly invoke Arcee Conductor via API. The Conductor API uses an OpenAI-compatible endpoint, making it very easy to update current applications to use Conductor. You can seamlessly leverage it across diverse scenarios—from customer service and content generation to data analysis and document processing—letting Conductor automatically select the optimal model for each unique prompt, maximizing efficiency across all AI interactions in your applications.
Let's cut through the theory and see real results. The following analysis showcases Arcee Conductor in action with real-world examples, side-by-side model comparisons, and metrics that demonstrate exactly how much you can save without sacrificing quality.
A marketing team produces various types of content daily, including social media posts, email campaigns, and long-form articles.
Let's look at what happens when we compare the process of creating a LinkedIn post using two different approaches: Auto Mode in Arcee Conductor versus using a Single LLM (e.g., Claude-3.7-Sonnet).
Note: The Auto Mode in Conductor analyzes your prompt's task type, domain, and complexity and then automatically routes it to the most suitable AI model for that specific prompt.
For this task, Auto Mode selected Arcee-Blitz, a 24B-parameter model from Arcee AI distilled from DeepSeekV-3 giving it impressive general domain knowledge.
The results might surprise you as we explore how this intelligent routing approach performs compared to using a single LLM for all content generation needs.
Let’s look at this example:
Prompt: “Create one engaging LinkedIn post highlighting our new AI-powered analytics dashboard, focusing on its ability to transform complex data into instant visual insights.”
Output Comparison:
Comparison insights:
For this specific prompt, Arcee-Blitz delivers 99.38% cost savings ($0.00002038 vs $0.003282) with comparable quality output for straightforward marketing copy tasks. At this volume, Claude-Sonnet-3.7 costs $15 per million output tokens and $3 per million input tokens. In contrast, Arcee-Blitz costs just $0.05 per million output tokens and $0.03 per million input tokens, saving $17.92 per million tokens compared to running Sonnet exclusively. Imagine the impact if your team processes over 100M tokens monthly. That's nearly $21,504 annual potential savings in your marketing budget–money that can make an impact elsewhere.
It’s worth noting that Arcee-Blitz produced a higher number of output tokens (290 tokens vs. Claude's 212 tokens), which typically contributes to longer response times. But Arcee-Blitz still processed the prompt faster (4.26s vs. Claude's 4.43s). This demonstrates how specialized small language models can deliver both cost savings and speed benefits for marketing copy-generation scenarios.
Beyond these impressive results for this specific use case, Arcee-Blitz here serves as a preview of the broader story.
Benchmark comparisons (shown in the left-side graph above) provide compelling evidence for intelligent model routing. The Arcee Router on Auto mode in Conductor matches Claude 3.5 Sonnet's performance across essential metrics like MMLU, GPQA-D, and HumanEval – and on Math-500, it delivers superior results.
AI understands AI better, and Arcee Conductor knows how to leverage the pool of model options to best handle your request without compromising performance. This would be incredibly valuable when you use Conductor via API to manage a diverse range of scenarios efficiently.
With the reliability of Auto Mode now established, let's dive into more use cases to see more benefits in action.
Today's developer teams have widely integrated AI models into their daily workflows. While these models boost productivity, they've created a hidden problem: every day, developers send dozens of prompts to the same generic AI model, regardless of the task complexity. The shortcomings here are obvious: the developers lack options to get the best performance, and even more importantly for most users, unnecessary costs add up very quickly.
Arcee Conductor provides a comprehensive catalog of leading LLMs and SLMs, intelligently routing every prompt to the optimal model based on complexity while significantly reducing AI usage costs.
Let's examine real-world developer scenarios across different complexity levels. On the simpler end, developers working with small language models commonly ask about CPU vs. GPU inference tradeoffs to make informed implementation decisions. On the more complex end, they might request detailed explanations of advanced techniques, like the differences between logit-based distillation and hidden state distillation, complete with Python code examples.
Prompt 1: “Explain the pros and cons of CPU inference and GPU inference for small language models.”
Output Comparison:
Comparison insights:
The Arcee AI SLM Virtuoso-Medium handles routine developer questions with impressive speed and savings. It responded in 9.28s (vs. Claude's 9.69s) and cost only $0.00018229 instead of $0.007062. That's 97.4% in cost-savings per prompt.
Prompt 2: “Explain the difference between logit-based distillation and hidden state distillation. Show an example for both with Python code, with BERT-large as the teacher model and BERT-base as the student model.”
Auto Mode Output:
For complex tasks, our solution remains robust. Auto Mode intelligently routed this advanced NLP question to Claude 3.7 Sonnet to ensure optimal response quality.
While this level of expertise comes at a higher token cost, Arcee Conductor ensures you only pay premium rates when genuinely necessary. The system also provides transparency by explaining why a particular model was selected – with information about the domain, the task, and the complexity level of your prompt.
Overall. this intelligent model routing approach gives you the best of both worlds: lightning-fast, cost-effective responses for routine questions – and also high-quality, detailed responses for complex challenges.
For engineering teams averaging 30-50 AI prompts per developer every day, the cost implications are substantial: a 50-person engineering team might generate 1,500 prompts daily. Approximately 60% of these prompts are routine questions that Virtuoso-Medium or similar specialized models can handle effectively. This can lead to hundreds of thousands of dollars in annual cost reductions.
By intelligently routing prompts based on complexity, you get optimal responses every time while minimizing overall costs across your entire AI workflow.
Finance operations represent a major expense for most businesses, often costing millions annually. Large enterprises typically spend 0.56% to 1.6% of their revenue on finance operations; for a company with $5B in revenue, that represents a $28-$80M dollar budget. GenAI is already making a major impact in this sector, in some cases reducing labor time by 70-90%. But inefficient model selection is still leading to a lot of unnecessary spending. This is an example of where Arcee Conductor could make a huge difference. By intelligently routing financial analysis, reporting, and forecasting prompts to the most cost-effective AI model, Conductor not only reduces AI processing costs by up to 85%, but also enhances accuracy and efficiency—maximizing both savings and performance.
For large enterprises, this translates into millions in savings annually, ensuring financial teams operate smarter, not just cheaper.
Let’s take a look at this example:
Prompt: “Conduct an in-depth monthly analysis to identify hidden financial leakages in our global operations, inventory management systems, and procurement processes. Recommend actionable strategies to optimize budget allocation, eliminate redundant costs, and significantly enhance overall cash flow. Include AI-driven automation solutions for routine financial workflows, such as invoice management, expense approvals, and real-time spending alerts, along with projected annual cost-savings metrics.”
Output Comparison:
Comparison Insights:
Arcee-Blitz demonstrates significant cost-efficiency advantages over Claude-3.7-Sonnet in financial analysis applications. At just $0.00005888 per analysis compared to Claude's $0.017664, Blitz delivers a staggering 99.67% cost reduction while maintaining competitive output quality.
The performance metrics tell a compelling story beyond just cost. Blitz processes financial data 32% faster (14s vs. 20.71s), enabling near real-time financial decision-making without sacrificing analytical power. While Claude produces slightly more verbose outputs (1159 vs. 1027 tokens), Blitz achieves superior information density per dollar spent, delivering actionable insights with optimal economy of expression.
But remember, cost savings alone aren’t enough—AI must be scalable, adaptable, and seamlessly integrated into business operations. That’s why Arcee Conductor goes beyond just cost optimization. Its model-agnostic architecture allows enterprises to scale AI adoption across multiple departments, from complex financial forecasting to simple transactional monitoring. It benefits all business units, improving operational efficiency and ensuring that AI can be implemented and adapted seamlessly across even the largest organizations.
AI should be both powerful and cost-efficient. Relying on a single model for every task leads to unnecessary expenses without added value. Arcee Conductor ensures that you always use the right model for each task, optimizing cost without compromising performance. With seamless integration and up to 99% savings per prompt, you get the best of both worlds: high-quality AI at a fraction of the cost.
Sign up HERE before April 15th to receive $200 in credits (equivalent to approximately 400 million tokens) toward your AI model usage on Arcee Conductor.
Arcee Conductor is an intelligent model routing platform that directs each input to its ideal AI model based on complexity. By dynamically routing between large language models (LLMs) and small language models (SLMs), Conductor maximizes cost efficiency without compromising performance. You get the right models for each prompt every time.
Integration is seamless. You can directly invoke Arcee Conductor via API. The Conductor API uses an OpenAI-compatible endpoint making it very easy to update current applications to use Conductor. No major infrastructure changes are required, making implementation simple and efficient.
Businesses using Arcee Conductor can typically see cost reductions of up to 99% per prompt when compared to relying exclusively on high-cost models like GPT-4o or Claude-3.7. By intelligently routing prompts to lower-cost models when appropriate, companies avoid unnecessary expenses while maintaining performance.
Sign up Arcee Conductor before April 15th to receive $200 in credits (equivalent to approximately 400 million tokens) toward your AI model usage.
The world of LLMs (Large Language Models) has cooked up a storm in recent years, with the rise of OpenAI’s GPT and the increasing proliferation of open source language models. Much excitement abounds, and virtually everyone and their grandma are mesmerized by the fact that a chat-based LLM can
So you want to train a custom language model, and you do have the requisite large set of text data. But how do you know that the data is *really actually ready* for model training? Our researchers here at Arcee AI tell you what to look out for.
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Today we bring you exciting updates on two small language models (SLMs) we've been working on: our first reasoning model, Arcee-Maestro-7B-Preview, and a fast and efficient Mistral-based DeepSeek distillation we call Arcee-Blitz.
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