Benchmarking ad-labelling AI against AWS-native models

A SaaS B2B business working with Cloud Combinator on AWS. The client is anonymised at their request.

The challenge had three focus areas.

Cloud Combinator structured the work in four phases with clear decision points, so the client only progressed to fine-tuning and deployment once the evidence justified it.

The engagement gives the client an evidence-based path off its external API dependency: a documented benchmark against the OpenAI baseline, an AWS model recommendation with confirmed fine-tuning support, a validated training-data schema, and a fine-tuning pipeline configured for repeatable retraining, all running within the client's own AWS environment. Figures below reflect the volume projections and pricing assumptions set out in the scope of work.

• A measured baseline, not a guess — The client's OpenAI workflow is benchmarked for accuracy, consistency, latency, throughput, and cost, giving an objective bar for every AWS option.
• Model selection on evidence — Bedrock and SageMaker candidates are evaluated with an AWS-based framework, and only a model that clears the benchmark and supports fine-tuning proceeds.
• Fine-tuned to the client's taxonomies — The chosen model is adapted to the client's own labelled data, improving accuracy and consistency on both global and bespoke taxonomies.
• Built to scale affordably — A batch-heavy, provisioned-throughput approach is designed to hold cost down as volumes climb from thousands to millions of requests a month.
• Handover — Cloud Combinator delivered the fine-tuning pipeline and configuration documentation for repeatable retraining, leaving the client able to run and improve the model in its own AWS account.

With a benchmarked, fine-tuned AWS labelling capability, the client is positioned to scale into its LinkedIn Marketing API migration and later Meta and Google Ads expansion, offering enterprise customers a closed, controllable environment as request volumes grow.