Reduce the carbon footprint and the unit cost of each operation while maintaining a high level of service.
Cost per interaction
Carbon footprint
Average latency
Small Language Models (SLMs) are preferred for structured and repetitive tasks, such as standardized summarization, intent classification, and entity extraction. These lighter models consume 10 to 100 times less energy than LLMs while still delivering sufficient quality for 80% of use cases.
Large Language Models are only used when business value warrants it: complex cases requiring reasoning, nuanced sentiment analysis, and agent quality monitoring. This intelligent orchestration reduces infrastructure costs by 60% compared to an “LLM-first” approach.
Ongoing optimization pipeline: model compression (quantization), pruning of unnecessary parameters, and knowledge distillation from LLMs to more efficient SLMs. Goal: to reduce the carbon footprint per conversation by 20% each year.
Settings
1-10B
Energy / Request
~0.1 Wh
Response time
< 500ms
Cost per 1 million tokens
~$0.50
Settings
70-175B
Energy / Request
~10 Wh
Response time
2–5 seconds
Cost per 1 million tokens
~$10–20
Estimated values for standard generation models (input + output).
Zaion’s cost-effective approach enables large enterprises to scale up agent-based AI without a surge in infrastructure costs, while aligning with CSR goals to reduce their digital carbon footprint.
-60%
Infrastructure costs vs. the LLM-first approach
-70%
Carbon footprint per conversation
10 million+
Interactions per day that can be scaled up
By combining SLM for most tasks and LLM for high-value-added cases, you can reduce the cost per interaction by 60% while maintaining a high level of service quality. You’ll see a return on your AI investments in 12–18 months instead of 3–5 years.
The lightweight architecture enables the system to handle millions of daily interactions without requiring an oversized infrastructure. You can gradually expand automated workflows without worrying about an exponential increase in operating costs.
Reducing the digital carbon footprint is a major challenge for businesses. By optimizing the energy consumption of your AI systems, you can contribute to your carbon neutrality goals while benefiting from a more cost-effective solution.
