After years of vendors heralding the arrival of fine-tuned models to save enterprises from the latency, unpredictable costs, and strenuous upgrade lifecycles of cloud-based model APIs, the moment for fine-tuned models has finally arrived. While frontier models are massive, general-purpose, and must be run from cloud servers, fine-tuned models (mostly composed of post-trained open-weight models) are smaller, more efficient to run, and can be deployed on devices closer to the edge. Even more crucially: They’re adapted with companies’ own data to perform better on tasks related to that data. Because these fine-tuned models are based on open-weight models, any enterprise with the wherewithal to build them can do so, making them more accessible.

The opportunity isn’t simply that fine-tuned models have become more capable. The more significant change is that enterprises can increasingly use open-weight models as a starting point for specialization, adapting them with proprietary data and domain expertise at a cost that’s becoming practical for many more use cases. Historically, the economics and complexity of doing this put customized models out of reach for most enterprises. Better-performing open-weight models and new post-training tooling are now lowering that barrier.

Over the past year, we’ve seen a number of new companies (both vendors and enterprises) begin to release their own post-trained custom models (based upon open-weight models) combined with the valuable proprietary data and expertise of these companies. Thomson Reuters introduced Thomson, with training data focused on legal, tax, and regulatory content. Harvey released Tenet, a post-trained model also centering on legal expert data. Travelers announced TravelersLLM to support underwriting analysis and research. Optimizely launched models specialized for marketing applications. Tech Mahindra launched Project Indus, one of several models it’s tuning for Hindi and other specific languages.

The rise in companies training and leveraging their own domain-specific, fine-tuned foundation models comes in the wake of several others giving up on grander ambitions around more generalized models, realizing that there may be more value in the enterprise with specialization rather than generalization. Databricks announced that it was stopping efforts around its DBRX model after costs continued to run too high, ServiceNow recently ceased updating its model in favor of supporting other models from third-party providers, and even a frontier model provider like Cohere is shifting to provide more directed and specific models.

The development and release pace of post-trained models is still a relative trickle across most enterprises, but there’s significant enablement innovations that could be opening the floodgates. New tooling capabilities — starting with earlier efforts such as IBM’s InstructLab to more recent releases like NVIDIA’s new Lightning model — have significantly improved the efficiency of post-training fine-tuning to enable these advancements. And there are numerous other examples, including Axolotl, Unsloth, and Together AI, to add to this trend.

The opportunity for adoption of fine-tuned foundation models is no longer beyond the horizon for most enterprises, but the opportunity won’t arrive at the same rate for all of them. Those with the largest sets of data and IP that either differentiate them in the market or provide a unique cross-cutting view will be best positioned to leverage that into model behavior and business outcomes.

What Enterprise Technology Leaders Should Do

  • Assess how fine-tuned models can unlock value from your proprietary data. Is it in a specific knowledge domain, or is it tied to a process domain? What’s the relative lifecycle cost of a fine-tuned model versus a retrieval-augmented generation or agentic system?
  • Identify specific AI problems that are well suited for a fine-tuned model. Do you need to maintain data or process sovereignty in your AI operations? Do you have employees operating in the field or at remote facilities with limited connectivity to large cloud-based models?
  • Review your current model routing and failover strategy. Are there any places in your resiliency plan where a self-hosted, task-specific model could help maintain uptime as a failover?

Are you a Forrester client working to understand how fine-tuned models might fit into your AI strategy? Then please reach out to schedule a guidance session.

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