The enterprise world is rapidly growing its usage of open source large language models (LLMs), driven by companies gaining more sophistication around AI – seeking greater control, customization, and cost efficiency.

While closed models like OpenAI's GPT-4 dominated early adoption, open source models have since closed the gap in quality, and are growing at least as quickly in the enterprise, according to multiple VentureBeat interviews with enterprise leaders.

This is a change from earlier this year, when I reported that while the promise of open source was undeniable, it was seeing relatively slow adoption. But Meta's openly available models have now been downloaded more than 400 million times, the company told VentureBeat, at a rate 10 times higher than last year, with usage doubling from May through July 2024. This surge in adoption reflects a convergence of factors – from technical parity to trust considerations – that are pushing advanced enterprises toward open alternatives.

“Open always wins,” declares Jonathan Ross, CEO of Groq, a provider of specialized AI processing infrastructure that has seen massive uptake of customers using open models. “And most people are really worried about vendor lock-in.”

Even AWS, which made a $4 billion investment in closed-source provider Anthropic – its largest investment ever – acknowledges the momentum. “We are definitely seeing increased traction over the last number of months on publicly available models,” says Baskar Sridharan, AWS' VP of AI & Infrastructure, which offers access to as many models as possible, both open and closed source, via its Bedrock service.

The platform shift by big app companies accelerates adoption

It's true that among startups or individual developers, closed-source models like OpenAI still lead. But in the enterprise, things are looking very different. Unfortunately, there is no third-party source that tracks the open versus closed LLM race for the enterprise, in part because it’s near impossible to do: The enterprise world is too distributed, and companies are too private for this information to be public. An API company, Kong, surveyed more than 700 users in July. But the respondents included smaller companies as well as enterprises, and so was biased toward OpenAI, which without question still leads among startups looking for simple options. (The report also included other AI services like Bedrock, which is not an LLM, but a service that offers multiple LLMs, including open source ones — so it mixes apples and oranges.)



But anecdotally, the evidence is piling up. For one, each of the major business application providers has moved aggressively recently to integrate open source LLMs, fundamentally changing how enterprises can deploy these models. Salesforce led the latest wave by introducing Agentforce last month, recognizing that its customer relationship management customers needed more flexible AI options. The platform enables companies to plug in any LLM within Salesforce applications, effectively making open source models as easy to use as closed ones. Salesforce-owned Slack quickly followed suit.

Oracle also last month expanded support for the latest Llama models across its enterprise suite, which includes the big enterprise apps of ERP, human resources, and supply chain. SAP, another business app giant, announced comprehensive open source LLM support through its Joule AI copilot, while ServiceNow enabled both open and closed LLM integration for workflow automation in areas like customer service and IT support.

“I think open models will ultimately win out,” says Oracle's EVP of AI and Data Management Services, Greg Pavlik. The ability to modify models and experiment, especially in vertical domains, combined with favorable cost, is proving compelling for enterprise customers, he said.

A complex landscape of “open” models

While Meta's Llama has emerged as a frontrunner, the open LLM ecosystem has evolved into a nuanced marketplace with different approaches to openness. For one, Meta's Llama has more than 65,000 model derivatives in the market. Enterprise IT leaders must navigate these, and other options ranging from fully open weights and training data to hybrid models with commercial licensing.

Mistral AI, for example, has gained significant traction by offering high-performing models with flexible licensing terms that appeal to enterprises needing different levels of support and customization. Cohere has taken another approach, providing open model weights but requiring a license fee – a model that some enterprises prefer for its balance of transparency and commercial support.

This complexity in the open model landscape has become an advantage for sophisticated enterprises. Companies can choose models that match their specific requirements – whether that's full control over model weights for heavy customization, or a supported open-weight model for faster deployment. The ability to inspect and modify these models provides a level of control impossible with fully closed alternatives, leaders say. Using open source models also often requires a more technically proficient team to fine-tune and manage the models effectively, another reason enterprise companies with more resources have an upper hand when using open source.

Meta's rapid development of Llama exemplifies why enterprises are embracing the flexibility of open models. AT&T uses Llama-based models for customer service automation, DoorDash for helping answer questions from its software engineers, and Spotify for content recommendations. Goldman Sachs has deployed these models in heavily regulated financial services applications. Other Llama users include Niantic, Nomura, Shopify, Zoom, Accenture, Infosys, KPMG, Wells Fargo, IBM, and The Grammy Awards.

Meta has aggressively nurtured channel partners. All major cloud providers embrace Llama models now. “The amount of interest and deployments they're starting to see for Llama with their enterprise customers has been skyrocketing,” reports Ragavan Srinivasan, VP of Product at Meta, “especially after Llama 3.1 and 3.2 have come out. The large 405B model in particular is seeing a lot of really strong traction because very sophisticated, mature enterprise customers see the value of being able to switch between multiple models.” He said customers can use a distillation service to create derivative models from Llama 405B, to be able to fine tune it based on their data. Distillation is the process of creating smaller, faster models while retaining core capabilities.

Indeed, Meta covers the landscape well with its other portfolio of models, including the Llama 90B model, which can be used as a workhorse for a majority of prompts, and 1B and 3B, which are small enough to be used on device. Today, Meta released “quantized” versions of those smaller models. Quantization is another process that makes a model  smaller, allowing less power consumption and faster processing. What makes these latest special is that they were quantized during training, making them more efficient than other industry quantized knock-offs – four times faster at token generation than their originals, using a fourth of the power.