In brief
LLM.co says its new study finds enterprise adoption of large language models moving from experimentation toward decisions about long-term infrastructure. The report describes broad organizational use of AI alongside a continued concentration of production workloads in closed source systems. It says enterprises are also adopting open source tools for flexibility, cost considerations and private deployment, with many planning to expand use or consider switching when performance is comparable. The study highlights hybrid stacks as one approach to balancing cost, performance, governance and vendor risk. LLM.co concludes that organizations are increasingly weighing portability, financial efficiency and regulatory needs alongside model capability. The report draws on enterprise survey data, market research and adoption benchmarks.
Key takeaways
- LLM.co reports that closed source models account for roughly 87% of deployed workloads in mid-2025.
- The study says 41% of organizations plan to increase open source LLM use, while another 41% would switch at performance parity.
- The report identifies hybrid AI stacks as a way to balance cost, performance, governance and vendor risk.
- LLM.co says open source models can support private deployment and tighter data controls, while closed platforms offer turnkey deployment and support.
Adoption trends signal accelerating open-source growth as enterprises rethink long-term AI infrastructure strategy
Seattle, Washington, United States, March 12, 2026 -- LLM.co today released a new industry study examining how enterprises are choosing between open source and closed source large language models (LLMs), revealing a market that is rapidly maturing beyond experimentation and into long-term infrastructure strategy.
The report synthesizes enterprise survey data, market research, and adoption benchmarks to clarify where the market stands — and where it’s heading.
One thing is clear: AI is now operational. Recent research shows that 78% of organizations use AI in at least one business function, while generative AI adoption has climbed to 71%, signaling that LLMs are becoming foundational business tools rather than experimental technology.
Yet beneath that growth lies a strategic divide.
McKinsey research indicates that more than half of enterprises now report using open source AI tools somewhere in their stack — often in data science, modeling workflows, or internal development environments — attracted by lower licensing costs, customization flexibility, and the ability to deploy models within private infrastructure.
At the same time, production workloads remain heavily concentrated among proprietary platforms. Mid-2025 enterprise usage data shows closed source LLMs still account for roughly 87% of deployed workloads, with commercial providers continuing to dominate high-reliability, customer-facing, and non-technical use cases.
According to Samuel Edwards, Chief Marketing Officer at LLM.co, this imbalance reflects a practical reality rather than a philosophical one.
“Enterprises are making pragmatic decisions,” Edwards said. “Open source LLMs are gaining traction because they offer flexibility, control, and economic advantages. But closed models still deliver faster deployment and proven performance for many mission-critical applications. The future isn’t open versus closed — it’s strategic use of both.”
That strategic balance appears to be shifting.
Enterprise sentiment studies show that 41% of organizations plan to expand their use of open source LLMs, while another 41% say they would transition from proprietary models once performance parity is achieved. Analysts note that if those intentions materialize, enterprise AI ecosystems could move toward an evenly balanced open-to-closed model mix within the next several years.
This shift is also driving architectural change. Forbes reports that 37% of enterprises now advocate hybrid AI stacks that combine open and closed models to optimize for cost, performance, governance, and vendor risk.
LLM.co’s research finds that the underlying decision factors are increasingly economic and regulatory. Open source models allow organizations to run AI workloads on-premises, maintain tighter data governance controls, and avoid vendor lock-in — priorities that matter in regulated industries and data-sensitive environments. Closed source platforms, however, continue to offer advantages in turnkey deployment, enterprise support, and time-to-value for non-technical teams.
For revenue leaders, those tradeoffs translate directly into long-term operating leverage.
“Enterprises are no longer testing LLMs — they’re operationalizing them,” said [CRO Name], Chief Revenue Officer at LLM.co. “That means model decisions now affect cost predictability, compliance posture, infrastructure strategy, and total cost of ownership. The most sophisticated organizations are designing AI frameworks that capture the strengths of both ecosystems rather than committing to one camp.”
The study concludes that LLM adoption is entering a second phase: from experimentation to optimization. As performance gaps narrow and open ecosystems mature, enterprises are expected to prioritize flexibility, portability, and financial efficiency alongside raw model capability.
The full study is available at:
https://llm.co/blog/why-enterprises-choose-open-source-llms-for-private-ai
About LLM.co
LLM.co is an enterprise AI research and advisory platform focused on applied large language model strategy, private AI deployment, and next-generation automation frameworks. Built by the team that brought you DEV.co, the company supports organizations in evaluating, implementing, and optimizing LLM technologies across business operations.
Contact Info:
Name: Samuel Edwards
Email: Send Email
Organization: Digital.Marketing
Website: https://digital.marketing
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Questions around this news
What does the study say about enterprise adoption of open- and closed-source LLMs?
The study describes a split between growing interest in open-source models and continued reliance on closed-source platforms for production workloads. It reports that more than half of enterprises use open-source AI tools somewhere in their technology stack, while closed-source LLMs still account for roughly 87% of deployed workloads in mid-2025. It also says some organizations plan to expand or potentially shift toward open-source use.
Why might an enterprise use both open- and closed-source AI models?
The release says open-source models can offer flexibility, control, lower licensing costs, and deployment within private infrastructure. Closed-source platforms may provide quicker implementation, enterprise support, and established performance for customer-facing or mission-critical work. Combining the approaches can let an organization weigh cost, performance, governance, and vendor risk across different needs.
What is a hybrid AI stack?
A hybrid AI stack combines different kinds of AI models or services rather than relying on a single approach. An organization might use models it can inspect or run in its own environment for some tasks, and externally provided models for others. The design can reflect differing requirements for performance, data handling, cost, and operational support.
What is the difference between an open-source and a closed-source language model?
An open-source model makes specified components, such as model code or weights, available under terms that permit some forms of use or modification. A closed-source model keeps key components restricted and is generally accessed through a provider’s service or licensed software. The exact permissions and available components vary by model and its license.
