How we cut LLM costs by 80%

Aether – Cutting LLM Costs Without Sacrificing Intelligence

One of the biggest challenges we’ve observed among our clients is the rising cost of large language models (LLMs) in AI agent workflows. When every prompt is sent to large thinking models like sonnet-4.5, costs can quickly spiral out of control, especially for enterprise-scale deployments.

We realised a simple but powerful insight: not every prompt requires a large “thinking” model. Many tasks can be handled just as effectively by smaller, faster models — if only the system could decide which to use.

This insight led us to create Aether, our model routing abstraction layer.

What is Aether?

Aether is a lightweight, intelligent routing layer for AI agents. Its main role is to:

Analyze the complexity of each prompt.
Determine the most appropriate model to handle it — whether that’s a smaller local model for simple tasks or a large model for complex reasoning.
Relay the prompt to the selected model and return the response seamlessly.

The result is an AI system that thinks efficiently, not extravagantly.

How Aether Works

Prompt Classification
Aether quickly assesses incoming prompts, categorising them based on length, task type, and complexity.
Dynamic Model Selection
- Small tasks → lightweight models like gemma-7B or distilled open-source models.
- Medium tasks → mid-tier models for balanced performance and cost.
- Complex tasks → large models like gpt-5 or specialised agent pipelines.
Response Handling
Each model receives a tailored system prompt to optimise for conciseness, clarity, or depth. Responses are returned through Aether as if a single intelligence handled the task.

The Impact

Integrating Aether into your current AI agent workflows can lead to dramatic cost savings, without sacrificing the quality or intelligence of responses:

LLM cost reduction: up to 80% on high-volume workflows.
Performance maintained: complex reasoning tasks still get the depth they require.
Infrastructure efficiency: smaller models reduce inference latency and resource usage.

This approach allows enterprises to scale AI-driven agents without scaling costs proportionally.

Key Takeaways

Intelligence doesn’t have to be expensive.
Right-sizing models per task is a powerful lever to reduce LLM costs.
Aether abstracts complexity, letting AI agents operate efficiently while maintaining high-quality outputs.

By combining smart routing with multi-model orchestration, Aether exemplifies the future of cost-aware, agentic AI systems.