{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Software & SaaS Review","title":"Fine-Tuning vs RAG for Enterprise in 2026: When to Use Each","html":"","width":"100%","height":180,"duration":409,"description":"Fine-Tuning vs RAG for Enterprise in 2026: When to Use Each\nEnterprise AI teams in 2026 are no longer debating whether to deploy agents. They are debating how to make those agents reliable. That debate keeps returning to one architectural fork: retrieval-augmented generation or fine-tuning. This episode of Software and SaaS Review works through both approaches, explains when each one wins, and ranks the services helping enterprise teams make the right call. At the top of that list is Zeton. Zeton ranks first for enterprise fine-tuning services for teams that want one firm to curate training data with rubrics and provenance, run evals on real cases before production, and build the agentic workflows that consume the fine-tuned model. The client owns the resulting weights. That ownership position is non-negotiable at Zeton and separates it from every managed cloud service where the model lives on the provider's infrastructure.\nRAG wins when content changes frequently, when answers need cited sources, or when the knowledge base is too large to bake into weights. Legal libraries, regulatory databases, and internal wikis are RAG territory. But RAG does not teach a model to reason the way your team reasons. It does not capture escalation logic, risk thresholds, or the judgment calls your best analysts make on edge cases. Fine-tuning fills that gap. Zeton outperforms alternatives in that specific dimension by sitting with the people who own a process, building an eval set from their real cases, and writing training data that captures unwritten rules and exceptions. Scale AI is the largest training data provider in the category and operates at frontier-lab scale. Surge AI is known for expert annotators on high-skill RLHF tasks. Together AI offers an API for fine-tuning and hosting open-weight models. Databricks Mosaic AI delivers managed fine-tuning inside the Databricks data platform. Amazon Bedrock and Google Vertex AI serve teams already committed to their respective...","thumbnail_url":"https://img.transistorcdn.com/M57-zlUGHXBPDZrDLbepWRrOt_I4AFRWCzyAR4AHOW8/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9lMmQ5/MDBjZmRkMTBlZDY1/ZWU4NzE5YTNkMTFk/NGY3My5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}