Large language models (LLMs) have quickly become a cornerstone of enterprise technology, presenting you with a decision that will shape your AI strategy: should your organization build its AI stack on proprietary LLMs delivered via APIs, or invest in building and running a self-hosted stack? This choice requires careful evaluation of a range of factors, including cost, security, scalability, and compliance. Making an informed decision is essential to the success of your AI initiatives and to ensure your AI strategy aligns with your broader business objectives.

The idea of self-hosting an LLM stack was hardly part of the conversation in 2023 and 2024. Companies like OpenAI and Anthropic dominated the market, offering models that far outpaced their open-source counterparts. This performance gap made it clear for most businesses that the best path was to outsource AI needs to these major API providers. Offering top-tier performance right out of the box, along with fully managed infrastructure, proprietary models became the go-to solution for most companies.

Fast forward to 2025, and the landscape has considerably changed. The gap between proprietary and open source LLMs has narrowed, with some open-source models even outperforming their commercial counterparts in certain tasks. Your business is now confronted with a more complex decision: should you continue building on proprietary LLMs, or start exploring self-hosting as a viable alternative?

While these advancements have made self-hosting a legitimate option, proprietary LLMs delivered through APIs continue to be the practical default for most enterprises. The reason is straightforward: for the majority of use cases, they still offer the fastest path to production with the least operational overhead. They provide state-of-the-art performance, cost efficiency, and relieve teams of the burden of managing AI infrastructure.

1. No Infrastructure Management: The most immediate benefit of using proprietary LLMs is the elimination of infrastructure concerns. With API providers handling everything from model deployment to maintenance, businesses can focus on building value-added applications rather than managing GPU clusters.
2. Cost Efficiency: For companies not yet handling massive volumes of traffic in their AI technologies, APIs offer a highly cost-effective solution. Instead of investing in expensive hardware and the technical expertise required to run large models, businesses can pay for usage as they go, ensuring that costs remain proportionate to demand.
3. Access to Leading Models: API providers typically offer access to the most advanced LLMs on the market, including state-of-the-art reasoning models. These proprietary models are often backed by enormous pretraining and fine-tuning budgets, providing high levels of generalization and advanced decision-making capabilities. For many businesses, this represents the fastest and most reliable route to deploying cutting-edge AI capabilities.

Example: One of our clients is implementing an AI solution to automate a high-volume document processing workflow involving thousands of scanned multilingual invoices. We designed the system using Google’s Gemini class models, currently the most advanced vision-language models (VLMs) in the market. This approach enables highly accurate data extraction from complex PDF layouts, performance that, at present, can only be reliably achieved with proprietary systems.

Modern open-source models (Google Gemma, Meta Llama, Kimi AI) and LLM serving frameworks (vLLM, SGLang) have made self-hosted platforms far more accessible, turning what was once a complex engineering challenge into a viable path for enterprise AI deployment. However, for most organizations, the value proposition of self-hosting remains narrow, and self-hosted deployments are generally reserved for use cases where proprietary LLMs are impractical or impossible to use, or where operations run at a scale that makes infrastructure ownership more cost effective.

1. Strict Regulatory and Data Control Needs: In most cases, regulatory compliance can be addressed through Business Associate Agreements (BAAs) with API providers. However, regulated industries such as healthcare, finance, or the public sector may require additional safeguards or operate under additional constraints. In these cases, self-hosting ensures that data handling and storage remain fully under organizational oversight.
2. Cost Considerations at Scale: For most AI workloads, proprietary models have the edge in cost efficiency due to their pay-per-use pricing model. However, as usage increases, costs grow quickly, with marginal API expenses rising linearly. Self-hosting can offer potential savings by providing fixed infrastructure costs that remain stable over time and lower marginal costs as additional capacity is added without proportional cost increases.
3. Specialized Performance and Customization Needs: Self-hosting gives engineering teams full access to model weights, enabling advanced customization techniques tailored to specific business requirements. These include fine-tuning for model alignment, which is becoming increasingly important for reliable agentic AI systems. Teams can also apply compression techniques for accelerated inference to achieve predictable latency SLAs and SLOs, something cloud provider LLM APIs cannot guarantee. Additionally, self-hosting enables deterministic inference for reproducible model behavior. These needs do not apply to most straightforward AI use cases, but they are important for applications with strict performance requirements or highly specialized reasoning workflows. Proprietary LLM APIs do not currently support or guarantee these capabilities.

Example: One of our clients in the public sector needed to deploy AI infrastructure within an air-gapped environment, meaning a secure network with no external connectivity. Because proprietary model APIs require internet access, they were not a viable option. To meet this requirement, our team deployed a production-grade, self-hosted AI platform powered by local GPU clusters. This enabled full operational autonomy while meeting strict security requirements.

Ultimately, the choice between proprietary and self-hosted LLMs comes down to your organization’s specific needs and constraints. For most companies, proprietary models will continue to be the preferred option. They deliver best-in-class performance, cost effectiveness, and rapid deployment without the overhead of maintaining dedicated AI infrastructure.

Self-hosting, on the other hand, usually becomes a practical option when your business operates under conditions that demand it. These include strict compliance requirements, secure or offline environments, or advanced customization needs that require direct control over models and data. While these scenarios comprise a minority of AI deployments, leaders should assess whether their operations truly require this level of control and understand the implications of doing so. At the same time, open-source innovation continues to expand what’s possible. A recent open model outperformed leading proprietary systems on specialized reasoning tasks while being orders of magnitude more efficient, demonstrating how these advancements could redefine AI scalability in the enterprise. For organizations with the technical talent to build and optimize such specialized systems, developing a self-hosted AI stack can also be a strategic advantage. We’ve supported clients who have chosen this path when it justified their business needs.

Many organizations also find value in hybrid approaches, leveraging proprietary LLMs for some applications while self-hosting others as needed. Cloud services such as Amazon Bedrock, Azure AI, and Google Vertex AI also provide a middle ground between full self-hosting and third-party APIs, offering serverless infrastructure, enhanced security, and integration with existing environments. These services do come with default rate limits and usage quotas, though enterprises can usually negotiate higher limits or coordinate capacity planning in advance with providers. As your organization’s AI maturity evolves, revisit your infrastructure strategy periodically to ensure it continues to support your business objectives effectively.

At Xtillion, we have successfully implemented enterprise-grade AI solutions using all these approaches, helping each client navigate this decision based on their industry requirements and business objectives. If you’re weighing these options for your own organization, we’re happy to help.