--- title: "CLM vs. LLM: To Build a Walled Garden or Not?" description: Exploring the benefits and drawbacks of Custom Language Models for marketers, and how tools like Google Deep Research can aid in making informed decisions. image: https://signal78.ai/hubfs/DALL%C2%B7E%202025-01-15%2010.50.44%20-%20A%20geometric%2c%20low-poly%20style%20image%20depicting%20a%20serene%20and%20safe%20walled%20garden.%20The%20garden%20is%20surrounded%20by%20tall%2c%20symmetrical%20walls%20made%20of%20polished%20ston.webp --- [Skip to content](https://signal78.ai/insights/clms-vs.-off-the-shelf-llms#main) [![Signal78 home](https://signal78.ai/hs-fs/hubfs/signal78-site/logo-signal78.png?width=95&height=30&name=logo-signal78.png)](https://signal78.ai/) [Home](https://signal78.ai/) [Platform](https://signal78.ai/platform) [Services](https://signal78.ai/services) [About](https://signal78.ai/about) [Insights](https://signal78.ai/insights) [Book a demo](https://signal78.ai/insights/clms-vs.-off-the-shelf-llms#demo) [← Insights](https://signal78.ai/insights) AI StrategyJanuary 15, 20255 min read # CLM vs. LLM: To Build a Walled Garden or Not? ![Matt Cyr](https://signal78.ai/hubfs/signal78-site/headshot-matt-cyr.jpg)**Matt Cyr** · Founder & President, Loop ![](https://signal78.ai/hubfs/DALL%C2%B7E%202025-01-15%2010.50.44%20-%20A%20geometric%2c%20low-poly%20style%20image%20depicting%20a%20serene%20and%20safe%20walled%20garden.%20The%20garden%20is%20surrounded%20by%20tall%2c%20symmetrical%20walls%20made%20of%20polished%20ston.webp) In this article Share ## Using Google Deep Research to Help Me Understand the Pros and Cons of Custom Language Models ### AI-Created TL;DR Version of the Post *Most marketing teams are using AI tools, but leaders often don't know what data is being shared, risking exposure of sensitive information. Custom Language Models (CLMs) offer a secure, tailored alternative, but come with higher costs and implementation challenges. To explore the trade-offs, I used Google Deep Research to help me understand the pros and cons. CLMs can be a smart option for balancing AI potential and data privacy, but careful consideration is essential.* ### Full Matt-Written Post Earlier this week, I talked about [starting small with AI](https://signal78.ai/insights/one-small-step-getting-started-with-ai). Today, I want to go big and discuss Custom Language Models. Whenever I talk with marketing teams about their AI use, one thing immediately becomes clear: *everyone* is using ChatGPT in one way or another. This isn’t surprising – ChatGPT is a really simple, powerful and exciting way to engage with AI. But the other thing that immediately becomes clear is that marketing leaders have *no idea* what kind of information their teams are putting into ChatGPT or any other AI tool. It could be something benign like a first draft of a blog post, but it could also be your organization’s strategic plan, with sensitive financial and competitive information, or protected patient or student information. So what’s a marketer to do? You could do a deep-dive read into the data privacy and usage policies for every AI tool to understand what they’re doing with your data, but we know that isn’t realistic. And the tools themselves make it hard to figure out what’s happening with the data you put in. In ChatGPT, for instance, there’s a toggle switch in Settings that lets you turn off model training. But what does that *actually* mean? (And is it *actually* true? OpenAI’s record on copyright protection [isn’t great](https://www.npr.org/2025/01/15/nx-s1-5258946/the-new-york-times-takes-openai-to-court-for-copyright-infringement).) One potential solution I’ve been thinking about for a while is creation of Custom Language Models (CLMs) that give all the benefits of a large, well-trained language model plus the comfort of knowing that your sensitive data won’t end up in the public domain. What is a CLM you ask? I’m not a technical person, so I think of it as a walled garden; a large language model that’s been walled off, data protected and trained on an organization’s specific information, brand and context. See the graphic I created in the image slider below. 👇 Ideally, this allows brands to use AI models freely, knowing not only that their data is safe, but that the CLM actually knows your brand – it isn’t giving generic solutions that could apply to any business. But of course there are drawbacks to the idea, like the cost and time of building a custom solution. Like any buy vs. build discussion, there are pros and cons to each side. ![Google Deep Research](https://signal78.ai/hs-fs/hubfs/Google%20Deep%20Research.jpeg?width=368&height=207&name=Google%20Deep%20Research.jpeg)So I decided to use the latest flashy AI tool – [Google Deep Research](https://blog.google/products/gemini/google-gemini-deep-research/) – to help me sort it out. If you haven’t tried it yet, Deep Research is a tool that, once prompted, scours the web for relevant information and comes back to you with a comprehensive research report. It’s pretty incredible. (Also check out my post about [Google Notebook LM](https://signal78.ai/insights/notebooklm-mind-blown) 🤯) Here’s the prompt I used: “I’d like to see a research report on the pros and cons of custom language models vs off-the-shelf products for marketers in healthcare and higher ed. Please include elements like brand management, data privacy, cost, implementation effort, etc. Do you need additional info from me to complete this report?” And here’s the "research plan" the system gave back to me for approval before beginning work: 1. Find information on custom language models and off-the-shelf language models. 2. Find information on the pros and cons of custom language models for marketers in healthcare and higher ed. 3. Find information on the pros and cons of off-the-shelf language models for marketers in healthcare and higher ed. 4. Find information on brand management, data privacy, cost, and implementation effort for both custom and off-the-shelf language models. 5. Find case studies or examples of how custom and off-the-shelf language models have been used in healthcare and higher ed marketing. 6. Based on the information you find, create a research report that compares and contrasts the pros and cons of custom and off-the-shelf language models for marketers in healthcare and higher ed. ![Structure of a CLM iamge](https://46328726.fs1.hubspotusercontent-na1.net/hub/46328726/hubfs/Screenshot%202025-01-15%20at%2011.50.00%20AM.png?width=1200&length=1200&name=Screenshot%202025-01-15%20at%2011.50.00%20AM.png) This is how I envision the structure of a CLM. ![Google Deep Research Prompt and Research Plan](https://46328726.fs1.hubspotusercontent-na1.net/hub/46328726/hubfs/Screenshot%202025-01-15%20at%2011.42.44%20AM.png?width=1200&length=1200&name=Screenshot%202025-01-15%20at%2011.42.44%20AM.png) This is the prompt I gave Google Deep Research and the research plan it gave me for approval before it ran off to do its thing. ![Google Deep Research Report](https://46328726.fs1.hubspotusercontent-na1.net/hub/46328726/hubfs/Screenshot%202025-01-15%20at%2011.31.01%20AM-1.png?width=1200&length=1200&name=Screenshot%202025-01-15%20at%2011.31.01%20AM-1.png) This is the beginning of the report Deep Research created on the pros and cons of Custom Language Models (CLMs). It took about 10 minutes to complete the report. ![Sources used to create the report](https://46328726.fs1.hubspotusercontent-na1.net/hub/46328726/hubfs/Screenshot%202025-01-15%20at%2011.40.56%20AM.png?width=1200&length=1200&name=Screenshot%202025-01-15%20at%2011.40.56%20AM.png) Deep Research referenced 68 separate resources to create the report. I didn't give it any of these materials. It found them entirely on its own. I gave Deep Research the virtual thumbs up and off it went. After about 10 minutes and a review of nearly 70 separate sources (none of which I gave it), it came back to me with [this report](https://g.co/gemini/share/9e1cb2778730%20%20Copy%20link). It’s quite good, and captures many of the pros and cons of each option. So what I’m left with after all of this is that CLMs could be a good option for brands that are looking to take advantage of the potential of AI while mitigating the risks. But, as with any other decision involving technology, strategy and data privacy, there are a lot of factors to consider. I also learned that Google Deep Research and other tools like it that will inevitably follow are incredibly powerful partners to individuals and teams. If you haven’t already, give it a road test and see how it can help you answer some of your stickiest marketing questions. ### Want to discuss whether a CLM is right for you? Get in touch. ![Matt Cyr](https://signal78.ai/hubfs/signal78-site/headshot-matt-cyr.jpg) Founder & President ### Matt Cyr Nearly 30 years leading marketing teams, from Boston’s hospitals and universities to eight years running strategy and new business at a 100-person agency. Founded Loop in 2024. ## More insights. [See all insights→](https://signal78.ai/insights) ### [![](https://signal78.ai/hubfs/Gemini_Generated_Image_tlr2qftlr2qftlr2.jpeg) AI StrategyOctober 6, 2026 It’s Q4 — How’s Your AI Plan Coming? Read the post→](https://signal78.ai/insights/its-q4-hows-your-ai-plan-coming) ### [![](https://signal78.ai/hubfs/2026-09-28_asset_linkedin-article-header-platform-update-fall-2026.png) Signal78September 28, 2026 Signal78 Is Live. Here's What It's Proven — and What's Next. Read the post→](https://signal78.ai/insights/notes-from-the-build-where-signal78-stands-this-fall) ### [![](https://signal78.ai/hubfs/37-78.png) AI for AgenciesAugust 11, 2026 AI Will Keep Making Move 37. We Named the Company After Move 78. Read the post→](https://signal78.ai/insights/ai-will-keep-finding-move-37.-we-named-the-company-after-move-78) Every week ## Stay in the Loop. Get next week’s issue in your inbox: what changed in AI, and what it means for your agency. ![Signal78](https://signal78.ai/hubfs/signal78-site/logo-signal78.png) The agentic operating platform for agencies. It runs the routine and signals your people when their judgment matters. 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