Over the past several years, the strategy for deploying artificial intelligence (AI) within enterprises has undergone a significant transformation. Organizations are rethinking how they approach AI projects and are choosing to partner with expert agencies rather than build complex AI systems entirely within their walls. In this article, we examine trends that span AI partnerships, return-on-investment (ROI) considerations, the cost challenges associated with developing and hosting large language models (LLMs) in-house, essential criteria for selecting AI vendors, and the emerging practice of multi-vendor collaborations.
Partnering with Expert AI Agencies vs. In-House Development
Recent surveys and industry reports have made it abundantly clear: most enterprises now believe that leveraging the expertise of external AI specialists is more efficient than investing heavily in developing AI solutions entirely in-house. A vivid illustration of this trend comes from a November 2023 survey conducted by Menlo Ventures. In this survey of 450 executives, a striking 80% of companies reported that when it came to adopting generative AI technology, they preferred to “buy” third-party solutions rather than develop them internally. Similarly, a 2023 KPMG survey on AI strategy noted that only 12% of organizations were building generative AI solutions from scratch. Instead, 50% were buying or leasing ready-to-use solutions, and nearly 29% were opting for a hybrid approach—combining internal development with external partnerships.
This preference for external partnerships is driven primarily by factors such as speed, skill availability, and access to proven technology. When a company uses an off-the-shelf or as-a-service AI solution provider, it avoids many of the pitfalls associated with in-house development. Research indicates that the immediate availability of “ready-made AI capabilities” substantially reduces both the time-to-market and capital risk. The investment required for a fully internal AI build can be steep due to high costs, lengthy development cycles, and the scarcity of specialized talent. Even major tech leaders, such as Salesforce, are increasingly turning to collaboration as a solution, forming associations with notable AI providers such as IBM, Nvidia, and Google. These multi-stakeholder partnerships enable organizations to bring advanced AI-powered products to market rapidly by leveraging external expertise, rather than risking the time and capital required for a solo effort.
In essence, the “build vs. buy” debate in the AI era has tilted heavily in favor of “buying” or partnering. The data clearly signals that harnessing external resources is not only cost-effective but also positions enterprises to be more agile, allowing them to access cutting-edge AI capabilities without taking on excessive risk. This external-first approach underscores a broader trend of leveraging specialized expertise to accelerate innovation and strategic transformation across industries.
Pre-Trained LLMs & Agentic Frameworks vs. Proprietary LLMs
When it comes to achieving a strong return on investment (ROI) with AI projects, many studies have reached a similar conclusion—pre-trained models and external AI services often lead to faster and more substantial returns when compared to the creation and maintenance of proprietary LLMs. An analysis from February 2024 by EY experts provides one of the strongest arguments in this regard. According to EY, the cost to develop a state-of-the-art AI model like ChatGPT runs into the region of $10 million just for the training process alone (excluding data collection, preparation, devops, talent, etc.). Such substantial investments, the report argues, would make it nearly impossible for a large majority of organizations to generate a meaningful ROI. Instead, companies are increasingly choosing to integrate externally developed LLMs or AI frameworks that can be customized to meet specific operational requirements. This approach not only cuts costs significantly but also yields a faster path to generating business value.
The advantages of operating with pre-trained AI models are multifold. First, they have already been validated on massive amounts of data, so they have solid foundational language generation capabilities. Enterprises can then apply fine-tuning or retrieval augmented generation (RAG) techniques to tailor these models to their particular needs, whether that is for enhancing customer service, streamlining operations, or bolstering security measures. Additional surveys conducted in 2023 further support this outlook. Companies that selected cloud-based or third-party AI services reached positive ROI more quickly than those attempting to build their own systems from scratch. The primary reasons for this were lower upfront costs and the benefit of immediate deployment, factors that together accelerate efficiencies throughout the organization.
To sum up the financial narrative, companies benefit from choosing external pre-trained models by bypassing the enormous sunk costs associated with bespoke LLM training. Instead of investing tens of millions of dollars into infrastructure, these organizations invest in faster, off-the-shelf solutions that deliver immediate improvements in efficiency, revenue generation, and customer satisfaction. The cumulative financial data underscores a broader consensus across industries: the ROI from buying or leasing AI services far outweighs the uncertain economic returns from launching fully proprietary AI systems.
The High Cost of In-House LLM Infrastructure (and Why It’s Often Unjustifiable)
One of the most challenging aspects of developing AI in-house is the considerable financial and operational burden associated with hosting and continuously training large language models. Academic research and industry analyses over the past two years point toward the reality that building and operating a cutting-edge LLM is oftentimes a financially prohibitive venture. An academic study published in April 2025 offers a particularly blunt assessment: training a state-of-the-art LLM is “an expensive endeavor accessible to only a handful of companies.” The study goes on to detail that even without including the costs of data acquisition and curation, the required compute—often involving thousands of GPUs running continuously for weeks or months—creates a formidable barrier for any enterprise aiming to develop their own models.
Furthermore, the operational costs do not end with the initial model training. The continuous cycle of maintenance, performance tuning, and upgrading infrastructure adds to an already steep financial commitment. In many cases, the specialized engineering talent required to manage these tasks is both scarce and expensive. This creates a scenario in which only tech giants and well-funded startups can justify the use of in-house LLMs.
Industry analysts echo these findings. This points to a crucial strategic decision—if the hardware, energy costs, recurring engineering support, and the risks tied to maintaining a supercomputer-scale project all add up, then the financial justification for in-house LLM deployment becomes extremely weak. For most enterprises, cloud-based AI solutions and development frameworks represent a far more sensible and financially viable path forward.
Vendor Selection Criteria: Extensibility, Data Governance & Open Architecture
Selecting an AI vendor—or even an entire AI platform—is no trivial matter. As enterprises venture further into the realm of AI, ensuring that their chosen solutions are not only cutting-edge but also sustainable over the long haul becomes paramount. Experts are clear on this point: organizations should prioritize vendors that offer solutions with high degrees of extensibility, robust data governance, and open architectures.
The rationale behind these criteria is straightforward. With the rapid pace of technological advancement, an AI solution that is flexible enough to integrate seamlessly into an organization’s current tech stack provides a significant strategic advantage. For instance, a 2025 data governance overview recommends that companies “look for scalability, flexibility, extensibility … and an open architecture to future-proof your governance efforts”. Such systems are designed to work in harmony with open APIs and third-party applications, allowing organizations to extend functionalities to meet new business needs and evolving challenges.
Another important concern is data governance. Data—the lifeblood of any AI system—must be managed with rigorous oversight to ensure not only security but also regulatory compliance. This becomes even more critical when deploying AI models at scale. Inadequate data governance can result in security vulnerabilities or compliance breaches, which can be extremely costly both financially and reputationally. Modern AI platforms are expected to incorporate features such as robust access controls, detailed audit trails, and sophisticated metadata management to ensure that data remains secure and properly managed throughout its lifecycle.
An open architecture plays a crucial role in avoiding vendor lock-in—a situation in which traditional companies become overly dependent on a particular vendor’s ecosystem. By selecting platforms built on open standards, enterprises can achieve “endless extensibility” that allows for smoother integrations with a wide range of systems, including databases, cloud services, and business intelligence tools. This open framework not only supports interconnectivity but also facilitates the exchange of data and functionality across different systems, ensuring that organizations remain agile and adaptable as technology evolves.
In a similar vein, guidance from Gartner for 2024–2025 stresses that organizations should align their AI products with strict governance and security protocols from the outset. This means ensuring that every layer of an AI system—from the underlying algorithms to data storage and user interfaces—complies with industry standards and regulatory requirements. When these factors are met, companies are much better positioned to extend their AI capabilities over the long term while minimizing risks associated with data breaches or compliance failures.
Ultimately, selecting an AI vendor with extensibility, strong data governance, and open architecture is not just about technical specifications. It is about future-proofing an organization’s investment in AI technology. With the rapid evolution of AI, having a solution that is adaptable, secure, and interoperable makes it possible to pivot as business needs change.
Partner Spotlight: GPT-trainer
As enterprises evaluate AI platforms for long-term viability, GPT-trainer stands out as a framework designed to address three non-negotiable priorities: extensibility, enterprise-grade governance, and architectural openness. Unlike rigid AI solutions that lock organizations into proprietary ecosystems, GPT-trainer prioritizes interoperability and scalability while ensuring compliance at its core.
Extensible by Design
GPT-trainer’s architecture is built to future-proof AI investments. By abstracting model dependencies behind a unified API, it enables seamless integration with leading LLMs—including GPT, Gemini, Claude, and DeepSeek—as well as custom fine-tuned models (for enterprise clients) hosted on major cloud platforms. This “no lock-in” philosophy extends to all aspects of the platform: enterprises can build versatile AI agents that use any combination of supported models and connect to existing business logic via APIs, MCPs, or services like Zapier and Make. For sensitive use cases, GPT-trainer offers hybrid deployment options that keeps non-AI ETL tasks on in-house infrastructure, while offloading computationally heavy tasks onto reputable cloud service providers. For agencies and consultancies, the platform’s white-label capabilities transform AI deployments into branded SaaS offerings, unlocking new profit centers and recurring revenue streams. To learn more about GPT-trainer's offerings, book a call here.
Governance as a Foundation
In an era where data breaches and regulatory missteps carry existential risks, GPT-trainer’s compliance-first approach provides audit committees with immediate assurance. The platform holds SOC II and ISO 27001 certifications, alongside full GDPR adherence, ensuring data handling meets stringent global standards. Its proprietary AI Supervisor layer adds another governance safeguard, monitoring conversations for robust intent-based routing, user frustrations, and pre-defined compliance triggers. As noted in Atlan’s 2025 data governance report, “scalable AI systems must embed governance at every layer”—a principle GPT-trainer operationalizes through role-based access controls, metadata tracking, and dedicated hosting options.
For more information, you can request access to GPT-trainer's trust center.
Open Architecture, Scalable Outcomes
GPT-trainer avoids the pitfalls of closed ecosystems through its open API strategy (check out GPT-trainer's public API documentation). The platform’s versatile deployment models further supports data privacy, control, and scalability. This allows enterprises to balance capital expenditure (on-prem resources) with operational costs (cloud inference).
Partnership Models for Co-Innovation
Beyond technology, GPT-trainer’s service ethos reflects the collaborative atmosphere of modern AI projects. GPT-trainer houses an elite team of US-based generative AI experts and brings together top talent from MIT, Caltech, Microsoft, Accenture, and NASA. With deep roots in Natural Language Processing (NLP) long before the 2022 generative AI boom, we offer unparalleled expertise in large language models (LLMs) and tailored enterprise solutions.
GPT-trainer's generative AI specialists will work with you to:
- Align your strategic vision
- Define precise project scope
- Implement custom AI solutions
- Facilitate seamless adoption through comprehensive training and support
Book a call with us to learn more.
Multi-Vendor Partnerships as an Enterprise Trend
The AI landscape is rapidly evolving, and one clear trend is the shift toward multi-vendor collaborations. Instead of relying on a single, all-encompassing AI platform, many enterprises are now building ecosystems comprised of multiple specialized solutions. This approach not only diversifies risk but also allows companies to tap into the best-of-breed technologies available for each specific function.
According to a Spring 2025 report from MIT Sloan Management Review, there is an “unprecedented” level of partnering among organizations, including collaborations with traditional competitors. The report suggests that this trend is driven by the need for greater creativity and domain-specific expertise, which often cannot be provided by one vendor alone. When financial institutions, for example, face complex challenges such as fraud detection, customer service enhancement, and risk analysis, they might opt to work with one vendor for fraud modeling, another for conversational AI in customer service, and yet another specialized consultancy for risk management.
This multi-vendor approach helps ensure that each segment of an enterprise’s AI ecosystem is backed by a partner who is a subject matter expert in that area. As organizations increasingly deploy AI across various business units, a single vendor may not excel in every domain. In fact, Gartner’s March 2025 analysis notes that “AI will continue to come from all parts of the business and in a variety of formats,” further suggesting that decentralized, multi-source AI environments will become the norm.
Organizations that adopt this multi-vendor strategy benefit from greater flexibility in their AI deployments. By integrating complementary AI solutions from diverse vendors, businesses can adjust more quickly to rapidly changing market conditions and emerging technologies. This distributed approach also inherently reduces the risk of vendor lock-in, ensuring that enterprises are not overly dependent on a single technology provider—a factor that is increasingly important in today’s fast-moving digital landscape.
Balancing Flexibility with Cohesion
As we look towards the future, the evolution of enterprise AI strategy shows clear signs of continuing along the trajectory of external partnerships, pre-trained models and agentic frameworks, and multi-vendor ecosystems. Enterprises that are quick to embrace these trends now will likely find themselves better positioned than competitors when it comes to agile decision-making and rapid innovation.
One key strategic consideration is the ongoing balance between flexibility and integration. As organizations accumulate an array of AI services from different vendors, the ability to integrate these diverse systems becomes paramount. Companies that invest in scalable middleware solutions or adopt protocols like the MCP are best positioned to ensure that their AI deployments remain coherent and responsive. The importance of this cannot be overstated: without a strategic plan for integration, the advantages of multi-vendor partnerships could be diluted by system silos and data fragmentation.
Conclusion
The enterprise AI landscape in 2025 has evolved dramatically, reshaping how companies think about strategy, development, and partnerships. Our collective research findings show that external partnerships with expert AI agencies are overwhelmingly preferred over solely in-house development—a trend driven by the need for speed, cost-awareness, and access to deep technical expertise. Third party LLMs and agentic frameworks, coupled with external partners, consistently provide a higher ROI compared to the high costs and uncertainties of developing in-house generative AI capabilities. Furthermore, the financial and operational barriers associated with building and hosting AI infrastructure internally serve as a potent reminder that not all AI journeys are meant to be tackled with a one-size-fits-all approach.
Enterprises are increasingly recognizing that successful AI deployments rely on choosing the right vendors—those who offer extensible, secure, and open architectures capable of integrating with diverse systems.
Finally, the emerging trend of multi-vendor partnerships allows companies to tap into specialized expertise across different facets of AI, fostering a culture of collaborative innovation that is essential for addressing the dynamic challenges of various business domains.