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July 11, 2025

Embedded LLMs: The New Backbone of Modern SaaS

AI is influencing the way SaaS products function. Here’s a quick overview of how embedded LLMs power software solutions.

Alex Drozdov

Software Implementation Consultant

The collaboration of software as a service (SaaS) and artificial intelligence was quite expected. AI is quickly entering various industries and taking key roles there, especially if these industries are closely related to software technologies. And what is more software-related than SaaS? Almost every known SaaS solution is already using AI within its products. Salesforce has Einstein, HubSpot has Breeze, and GitHub has Copilot. These are only the most high-profile and well-known examples. There are actually many more.

All these tools are based on so-called embedded models that provide the majority of smart SaaS features. In this article, we will tell you what embedded LLMs are and how exactly they are changing the SaaS world.

What are embedded LLMs?

When people hear “LLM,” they think of cloud-based APIs like Claude. They are powerful general-purpose models that you can access over the internet. But embedded LLMs are something different.

Embedded LLMs in SaaS

Embedded LLMs run directly within your product’s environment (on a user’s device, on your company’s private infrastructure, or even partially within the app’s cloud backend). Unlike traditional hosted models, embedded LLMs are integrated more deeply into your software architecture and function as a part of the product.

What are the key traits of an embedded LLM?

  • Local/private deployment: These solutions can run on the edge (in-browser or mobile) or on your own servers.

  • Custom-tuned for your product: Many embedded models are prompt-engineered or fine-tuned for specific use cases.

  • Low-latency, offline-friendly: Some embedded LLMs can respond in milliseconds and even without online access.

Embedding LLMs changes AI from “a cloud service add-on” to a core part of your product experience. You will also get more secure data (no third-party API involved), lower operational costs (no per-token charges), and a tighter UX fit (the AI feels native).

What drives the shift

SaaS companies didn’t suddenly wake up one day and decide to embed language models. A few trends have made embedded LLMs increasingly necessary. Here is what you need to know about them:

AI in SaaS stats
Source: Acropolium

Open-source LLMs are getting good

Not long ago, the only real option for LLM integration was calling a closed LLM API like GPT-4. Now, you have high-performance open-source models like Mistral, LLaMA, Phi, and Gemma that run on consumer-grade GPUs and even smartphones.

These models are easy enough to embed and can be tailored to the tasks you need them to complete. They also eliminate vendor lock-in. With a closed-source LLM API, you are significantly limited by their pricing model and rate limits. Open-source LLMs are released with permissive licenses and all the data they learned during training. That means:

1) You can run them anywhere.

2) You can fine-tune/customize them as you wish.

3) You’re not at the mercy of a provider.

Cloud API costs don’t scale

Hosted LLMs are powerful but pricey. Even the smallest interaction with them may involve many tokens, and they can add up fast. For many SaaS products, this cost model is unsustainable.

Embedded models change the economics:

  • No per-token fees

  • Predictable infrastructure costs

  • Higher ROI

It’s almost like the difference between renting a car and owning one. Embedding just makes more sense long-term.

Privacy concerns

Many industries (especially data-sensitive ones) have strict requirements around where data lives and how it's processed. Even with such requirements and laws present, the concerns about data safety do not subside. Data breaches and hacker attacks are still considered huge risks for AI. And these risks are not cheap: The average global data breach costs around $4.9 million.

Embedded LLMs offer a SaaS business better security. They provide on-device or on-premise inference and total control over the data pipeline. That way, you will be able to comply with regulations like GDPR, HIPAA, and SOC 2 with ease.

How embedded LLMs are changing SaaS

Embedded LLMs are redefining how SaaS products are designed, built, and experienced. Just adding some AI features to “impress users” is not enough anymore. It’s a deeper transformation across many fronts.

How embedded LLMs are changing SaaS

From “tools” to “assistants”

Before embedded LLMs, SaaS apps provided a static set of features: dashboards, input forms, filters, configs, and rule-based automation. The user had to know what to do with the spreadsheets, which settings to configure, and how to manually operate every part of the UI.

With the help of embedded LLMs, those utilities were turned into dynamic assistants. Instead of digging through filters, users can just say, “Show me all projects over budget last quarter and tell me why.” Such assistants can interpret ambiguous intent and handle multi-step flows like summarizing activity or generating content, all in one interaction.

Product features are becoming conversations

Large language models provide a new manner of interaction: natural language interfaces. Traditional SaaS products are based on predefined processes and require structured inputs. A touch of AI enables natural-language-first product design: Users speak or type what they need, the model parses it, figures out the correct action, and explains what it's doing. If it’s necessary, the LLM confirms the intent or offers more options.

SaaS products become easier to use, especially for non-technical users. Conversations replace clicks, and clicks become optional.

Constant feedback loops inside the app

The usual user feedback can be implicit (clicks, drop-offs) or require open surveys and support tickets. It was based on static features that don’t change in real-time in response to user behavior.

With embedded AI features, you can get live and context-sensitive feedback. The dialog form of communication will allow your model to ask clarifying questions, suggest alternatives, and adjust tone, length, or content on the fly. This creates a “living” experience where every user interaction becomes a signal. Over time, the model adapts to individual users, and product teams gather insights on how features are actually used.

Built-in privacy and trust

As we already mentioned, privacy is a huge concern when it comes to AI. If you use a third-party LLM API, you have to send the information (sometimes sensitive) to someone else. This raised red flags for enterprise clients with strict compliance policies and companies from regulated industries like healthcare and banking. Also, it can be risky for your SaaS product if your internal AI tools deal with trade secrets or confidential IP.

Now, LLMs are ready to provide you with a new level of flexibility. Embedded models can run on-premise, on VPCs, or even on edge devices. They keep data entirely within your infrastructure, so you no longer have to worry about compliance headaches and data safety questions.

Here’s a quick summary of how embedded LLMs are changing the way SaaS solutions work:

Before embedded LLMsAfter embedded LLMs
Click-heavy UXConversational UX
Static rules and logicDynamic, adaptive agents
Cloud-only LLM callsOn-device, in-app intelligence
Users trained to use the appApp adapts to the user's language and needs
One-size-fits-all featuresContext-aware experiences

Use cases and real-life examples

Embedded LLMs are already bringing plenty of benefits to the world of SaaS. Several industries have adopted this approach for their products. These industries include:

  • Software development: Writer created an AI-based open-source platform that helps its users create web applications within seconds.

  • Analytics: Act-On integrated an embedded GenAI solution that lets users ask natural-language questions about marketing performance.

  • Customer support: Doordash uses embedded LLM-powered chatbots that handle support queries using RAG (Retrieval-Augmented Generation). 

  • Legal and compliance: Ernst & Young deployed a private LLM (“EYQ”) across 400k employees so that they can interact with enterprise data with the help of natural language, which boosted productivity by 40%.

  • E‑commerce: Thanks to an LLM, UrbanEase Apparel now writes SEO-optimized product descriptions at scale, which boosts content output.

  • Education: Khanmigo by Khan Academy is an AI tutor that “talks” with students and adapts explanations based on responses. 

  • Sales and CRM automation: DeltaForge, an industrial components manufacturer, uses AI to generate custom proposals and RFP responses, which cuts preparation time in half.

  • Personal productivity: Reor is an AI personal management app that organizes notes, shows related content, and answers questions with the help of semantic search.

  • Business intelligence: ThoughtSpot offers conversational analytics where users can ask, “Why did churn spike this quarter?” and receive accurate answers and charts.

Challenges and risks

Embedded LLMs in SaaS products unlock incredible value, but they also come with a set of challenges that product teams must know about. Here’s a list of what you should be prepared for:

  • Model hallucinations: LLMs can very confidently generate incorrect or misleading information. It will make your users lose trust in your products and can even lead to legal liabilities.

  • Infrastructure and cost overhead: Running large models locally or managing infrastructure for inference is expensive and complex. Companies can underestimate GPU needs or, on the other hand, overspend on AI infrastructure that ends up not being used.

  • Model versioning and maintenance: Models evolve fast, and keeping your embedded LLM updated without breaking UX or business logic becomes harder.

  • Integration complexity: Embedding LLMs into legacy SaaS platforms requires re-architecture since they need access to real-time databases and context from user sessions. Not all older systems can support such changes.

To sum up

Embedded LLMs are powerful. However, they are not plug-and-play. Their success in SaaS depends on thoughtful integration and constant iteration. If done right, these AI solutions will expand what your product can do.

If you want to embed an LLM into your SaaS product, we are here to help. Yellow is ready to provide you with AI development services that will bring your product to a new level. Drop us a line to book a call with us!

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