Open Source in AI SaaS: Licensing and IP Risks in M&A — And How to Prepare

In today’s AI-driven SaaS landscape, leveraging open-source libraries and pre-trained models is not just common — it’s foundational. From TensorFlow and PyTorch to Hugging Face transformers and scikit-learn, these tools accelerate development and reduce costs. But when it comes time to sell your company or raise capital, what once felt like a technical shortcut can become a legal and financial landmine.

At iMerge, we’ve advised on numerous software and AI transactions where open-source usage became a central diligence issue. If your product incorporates open-source components — especially if you’ve fine-tuned or modified them — it’s critical to understand the licensing implications and how they may affect your valuation, deal structure, or even deal viability.

Why Open Source Raises Red Flags in M&A

Buyers — particularly strategic acquirers and private equity firms — are increasingly cautious about open-source software (OSS) usage. Their concern isn’t philosophical; it’s legal and financial. Improper use of OSS can expose the acquirer to:

• License violations that require code disclosure or restrict commercial use
• IP contamination that undermines proprietary claims
• Unclear ownership of derivative works or fine-tuned models
• Litigation risk from rights holders or contributors

These risks can delay a deal, reduce the purchase price, or lead to post-closing indemnification claims. In some cases, they’ve caused buyers to walk away entirely.

Key Licensing Issues to Watch

Not all open-source licenses are created equal. Some are permissive and business-friendly; others are “copyleft” licenses that impose strict obligations. Here are the most common categories:

1. Permissive Licenses (e.g., MIT, Apache 2.0, BSD)

These licenses allow you to use, modify, and distribute the code — even in proprietary products — with minimal restrictions. Apache 2.0, for example, includes an explicit patent grant, which is attractive to acquirers. These licenses are generally low-risk in M&A.

2. Copyleft Licenses (e.g., GPL, AGPL, LGPL)

These licenses require that derivative works also be open-sourced under the same license. The GNU General Public License (GPL) is particularly problematic in commercial settings. If your SaaS product includes or links to GPL-licensed code, you may be obligated to release your source code — a non-starter for most acquirers.

The Affero GPL (AGPL) goes even further, applying to software accessed over a network — a direct hit to SaaS models. If you’ve fine-tuned an AGPL-licensed model and deployed it via API, you may be in violation unless you’ve open-sourced your modifications.

3. Model-Specific Licenses (e.g., OpenRAIL, BigScience, Meta’s LLaMA)

AI models often come with custom licenses that restrict commercial use, redistribution, or fine-tuning. For example, Meta’s LLaMA models are released under a non-commercial license, and OpenAI’s models are proprietary. Even open models like BLOOM or Falcon may include clauses that limit usage in certain industries or require attribution.

Buyers will scrutinize whether your use of these models complies with their terms — especially if you’ve built a commercial product on top of them.

How to Prepare for Diligence: A Strategic Checklist

To avoid surprises during due diligence, founders should proactively audit and document their open-source usage. Here’s how:

1. Conduct a Full OSS Inventory

Use automated tools (e.g., FOSSA, Black Duck, Snyk) to scan your codebase and identify all open-source components, including transitive dependencies. Don’t forget Docker images, scripts, and infrastructure code.

2. Map Licenses to Usage

For each component, document:

• The license type (MIT, GPL, etc.)
• How it’s used (linked, modified, embedded, etc.)
• Whether it’s included in distributed code or only used internally

This mapping helps assess exposure and informs your legal strategy.

3. Review Fine-Tuned Models

If you’ve fine-tuned open-source models (e.g., BERT, Stable Diffusion), determine:

• Whether the base model allows commercial fine-tuning
• If your modifications constitute a derivative work
• Whether you’ve redistributed the model or exposed it via API

Some licenses, like OpenRAIL-M, require that fine-tuned models carry forward the same restrictions. Violating these terms can jeopardize your IP claims.

4. Clean Up IP Ownership

Ensure all contributors — employees, contractors, or third parties — have signed IP assignment agreements. This is especially important if they’ve modified open-source code or trained models. Without clear ownership, you can’t transfer rights in a sale.

5. Create an Open Source Policy

Buyers want to see that you’ve institutionalized OSS governance. A written policy should cover:

• Approval processes for new OSS components
• License compliance procedures
• Security patching and update protocols

Firms like iMerge often help clients implement these policies as part of exit business planning strategy.

How This Affects Deal Structure and Valuation

In M&A, open-source issues can influence both the structure and economics of a deal:

• Stock vs. Asset Sale: Buyers may prefer an asset sale to avoid inheriting OSS-related liabilities. (See: Asset versus Stock Sale)
• Reps and Warranties: Expect detailed reps around OSS usage, license compliance, and IP ownership. Breaches can trigger indemnification or escrow claims.
• Valuation Haircuts: If your core IP is built on restrictive OSS, buyers may discount your valuation or require code rewrites post-close.

In one recent transaction we advised, a SaaS AI company had fine-tuned a model under a non-commercial license. The buyer required a full model retraining on a commercially licensed base — delaying the deal by 60 days and reducing the purchase price by 15%.

Positioning for a Clean Exit

Open-source software is not inherently a problem — but unmanaged OSS is. The key is transparency, documentation, and proactive remediation. Founders who address these issues early can avoid costly surprises and preserve leverage in negotiations.

At iMerge, we routinely help SaaS and AI companies prepare for diligence by conducting pre-sale audits, cleaning up IP chains, and advising on license compliance. This work not only protects value — it often increases it by reducing perceived risk.

For more on preparing your company for sale, see our Due Diligence Checklist for Software (SaaS) Companies and Top 10 Items to Prepare When Selling Your Website.

Founders navigating valuation or deal structuring decisions can benefit from iMerge’s experience in software and tech exits — reach out for guidance tailored to your situation.