Small Language Model
News & Insights
Marketing automation

In a marketing automation use case, a business uses language models to personalize email campaigns, generate content for social media, or recommend products. Small language models (SLMs) and large language models (LLMs) differ in terms of efficiency, speed, and cost when it comes to automating these tasks.
Use Case: Personalized Email Campaign Generation
Scenario
A company wants to automate the generation of personalized email campaigns for its customers based on their behavior and preferences. Both SLMs and LLMs are employed to create email copy, product recommendations, and tailored messaging for different customer segments.
Key Metrics for Comparison
Latency: Time taken to generate a personalized email.
Memory Usage: RAM is required to generate content.
Accuracy: Precision in generating relevant, personalized, and coherent content.
Throughput: Number of emails generated per second.
Scalability: Ability to handle large campaigns with minimal overhead.
Cost Efficiency: Compute resources required for content generation.
Metric
Model Size
Latency (average)
Memory Usage (RAM)
Compute Power
Content Accuracy
Throughput
Small Language Model (SLM)
120M parameters
0.08 seconds/email
300 MB
CPU only
88%
12 emails/second
Large Language Model (LLM)
1.5B parameters
2.1 seconds/email
10 GB
GPU/High-end CPU
96%
0.48 emails/second
Technical Insights
Latency: The SLM processes and generates emails in 0.08 seconds per email, which is 26x faster than the LLM’s 2.1 seconds. This speed advantage is crucial when generating large-scale personalized email campaigns where even a few seconds of delay per email can result in significant cumulative slowdowns.
Memory and Compute Efficiency: The SLM requires 300 MB of RAM, making it ideal for running on standard servers or even on-premise hardware. On the other hand, the LLM needs 10 GB of RAM, necessitating powerful cloud instances with GPU support. The low memory footprint of the SLM ensures it can be deployed at a fraction of the cost, without significant compute infrastructure investment.
Throughput: The SLM is able to handle 12 emails per second, and it offers higher throughput compared to the LLM’s 0.48 emails per second. This is particularly important in high-demand scenarios, such as during holiday marketing campaigns or flash sales, where timely delivery of personalized content is crucial to maximizing engagement and conversions.
Content Accuracy: The LLM produces content with 96% accuracy, meaning it generates more sophisticated, nuanced, and personalized messaging. However, the SLM’s 88% accuracy is sufficient for many marketing applications, especially for straightforward product recommendations, sales promotions, or general marketing emails. For use cases where high precision and creativity are not paramount, the SLM offers a strong balance between quality and efficiency.
Business Insights
Cost Efficiency: Running an SLM for marketing automation is far more cost-effective than deploying an LLM. SLMs require minimal hardware and can run on existing servers, while LLMs require expensive GPU instances. This allows businesses to automate their marketing campaigns without incurring significant infrastructure or cloud costs, making it ideal for small to medium-sized businesses.
Speed in Campaign Generation: The SLM can generate 12 emails per second, enabling businesses to rapidly create and send personalized email campaigns to large customer segments. This high speed ensures that businesses can respond to events (e.g., flash sales, product launches) in real-time, allowing marketing teams to engage customers quickly without delays.
Scalability: The higher throughput of the SLM ensures that it can scale to meet the demands of even the largest campaigns. Whether sending thousands of personalized emails daily or during peak shopping periods, the SLM can generate content quickly and efficiently. Additionally, the low memory requirements mean it can be deployed across multiple servers without needing specialized infrastructure.
Good Enough Personalization: While the LLM delivers slightly better personalization, the 88% accuracy of the SLM is more than sufficient for most marketing automation tasks. For businesses generating standard promotional emails, product recommendations, or reminders, the SLM delivers personalized messaging that resonates with customers without the added costs and complexity of an LLM.
Benchmarking Example
Consider a business that needs to generate 500,000 personalized emails for a marketing campaign:
SLM Processing Time: 0.08 seconds/email → 11.11 hours to process the entire campaign.
LLM Processing Time: 2.1 seconds/email → 12.15 days to process the entire campaign.
With the SLM completing the task in under half a day, compared to the LLM taking over 12 days, businesses can see immediate value in speed and responsiveness during time-sensitive marketing efforts.
Conclusion
For marketing automation use cases, such as generating personalized emails at scale, small language models (SLMs) provide significant advantages over large language models (LLMs) in terms of speed, efficiency, and cost. The SLM’s ability to generate emails quickly with minimal resource usage makes it ideal for businesses that need to engage large customer bases without investing heavily in infrastructure. While LLMs offer higher accuracy and nuanced personalization, the trade-off in cost and speed makes them less practical for routine marketing automation tasks where good enough personalization (88%) can achieve high engagement levels.
SLMs are particularly suitable for businesses with high-volume marketing needs that prioritize timeliness, scalability, and cost-efficiency over minor improvements in message accuracy and personalization.