Building vs. Buying an LLM: Key Decision Factors

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Table of Contents

1. Building vs. Buying an LLM Key Decision Factors

2. Introduction

3. Cost Analysis

4. Security and IP Concerns

5. Scalability

6. Technical Feasibility

7. Industry Trends and Case Studies

8. Conclusion

Introduction

Organizations exploring Large Language Models (LLMs) face a critical choice: build a custom LLM in-house or leverage an existing model or API. This decision involves trade-offs across cost, security, scalability, and technical complexity. Below, we review recent literature (2024–2025) on these factors, including industry case studies and research insights, to provide an unbiased comparison. All considerations are industry-agnostic and focus on balancing in-house development of LLMs versus using open-source or third-party LLM services.

Cost Analysis

Upfront vs. Ongoing Costs: Developing an LLM in-house entails significant upfront investment. Training a state-of-the-art model from scratch can cost tens of millions of dollars (e.g. OpenAI’s GPT-4 is estimated at ~$78M to train (AI Cheat Sheet: Large Language Foundation Model Training Costs), and even the 175B-parameter GPT-3 reportedly cost $4.6–15M ). These costs arise from multiple components: compute infrastructure, energy, large-scale data acquisition/processing, and highly skilled engineering labor . Fine-tuning a pre-trained open-source model is far cheaper than full training – sometimes only on the order of thousands of dollars for smaller models (Stanford’s Alpaca 7B chatbot was produced for under $600 by fine-tuning Meta’s LLaMA model (Meet Alpaca: The Open Source ChatGPT Made for Less Than $600) ) – but still requires compute and expertise upfront. In contrast, using a third-party LLM API avoids big upfront costs; you typically pay per request or via subscription. This pay-as-you-go model means low initial expense but potentially high cumulative costs as usage scales (The LLM Dilemma: Self-Hosted vs. Public API Solutions). For example, one analysis notes that while an API call might only cost a few cents, at enterprise scale the annual API fees can reach six or seven figures .

Per-Query Costs and Scalability: Third-party LLM providers charge by tokens or calls, which makes costs directly proportional to usage. This is convenient for prototyping or sporadic use (no infrastructure to maintain), but costs can balloon with heavy load . OpenAI, Google, and others have adjusted their pricing tiers over time, introducing uncertainty in long-term budgeting . A recent comparison showed that self-hosting can become cost-effective at moderate to high usage: running a 7B open-source model on Hugging Face or similar infrastructure was about 50% cheaper than using OpenAI’s GPT-3.5 when utilized at full capacity . Hosting a 13B model (fine-tuned to a domain) was found to be 9× cheaper than GPT-4 Turbo API and 26× cheaper than GPT-4 for equivalent loads . These findings imply that if an application will consistently use a model at high throughput, the total cost of ownership (TCO) of an in-house or open-source model can undercut API fees in the long run . On the other hand, if usage is low or uncertain, third-party APIs may remain more economical due to zero maintenance overhead.

Compute Infrastructure – Cloud vs. On-Prem: Whether building or using open-source models, organizations must decide between cloud or on-premises deployment, which impacts cost structure. Cloud infrastructure offers flexibility and eliminates capital expenditure on hardware, but renting GPU time can be expensive at scale. A 2024 analyst study by Dell’s Enterprise Strategy Group found that hosting LLM inference on owned infrastructure was up to 4× more cost-effective than using cloud IaaS and up to 8× cheaper than calling a GPT-4 API for the evaluated use case (ESG-Economic-WP-Dell-Technologies-LLM-TCO-Apr-2024). This suggests that once utilization is high enough, investing in on-premise GPUs or optimized hardware can significantly reduce per-query costs. However, on-prem comes with its own ongoing costs: electricity and cooling, hardware maintenance, and the personnel to manage it. In essence, self-hosting shifts costs from usage fees to fixed infrastructure and labor. Companies must weigh if they have enough volume (or data sensitivity needs) to justify that shift (Choosing Between Open-Source LLM & Proprietary AI Model). Hybrid approaches are also emerging – for example, using cloud for initial development or peak traffic and on-prem for steady state – to optimize cost-efficiency.

Summary of Cost Trade-offs: Choosing build vs. buy largely hinges on scale and duration of use:

• If you expect heavy, sustained usage, the economics often favor investing in an open-source model deployment (despite the upfront costs of training/tuning and hardware) because the marginal cost per query is low (The LLM Dilemma: Self-Hosted vs. Public API Solutions) . You gain cost predictability (your own infrastructure) and avoid continually rising API bills (Choosing Between Open-Source LLM & Proprietary AI Model).

• If your use case is small-scale, short-term, or exploratory, third-party APIs can be cost-effective and budget-friendly initially . They eliminate infrastructure spend and let you pay only for what you use, which is ideal for uncertain workloads. The downside is the risk of unpredictable pricing or rate limits as your usage grows (vendors have changed prices and terms, affecting enterprise budgets) .

• Either way, it’s crucial to account for all components of TCO: compute, storage, data curation, engineering time, model maintenance, and even compliance costs . Open-source solutions might save on usage fees but incur internal engineering and support costs, whereas proprietary services bundle those into the price. Each organization must calculate which approach yields a lower TCO over the project’s life.

Security and IP Concerns

Data Privacy and Sovereignty: Sending data to a third-party LLM API raises immediate privacy considerations. Sensitive or proprietary data (customer information, confidential documents, etc.) might be transmitted and temporarily stored on external servers outside your direct control. Even if providers have strict privacy policies, there is an inherent loss of direct oversight . Organizations in regulated sectors (finance, healthcare, government) often have compliance rules that restrict cloud data processing or require data residency in specific locales. In-house or self-hosted LLMs alleviate these concerns by keeping data on-premises or in a private cloud under the company’s control. No external party sees the raw prompts or outputs, which simplifies compliance with data protection regulations . Additionally, self-hosting allows applying advanced privacy techniques like differential privacy during training and strict access controls around the model, further securing sensitive data . In contrast, with public APIs one must trust the vendor’s security and sometimes accept that data may be logged or used to improve the service (unless explicitly opted out). The privacy trade-off is clear: third-party APIs offer convenience at the potential cost of data confidentiality, whereas open-source/in-house models offer data isolation at the cost of extra responsibility in securing that infrastructure.

Intellectual Property (IP) and Licensing: Another dimension is the ownership and use rights of the model itself and its outputs. Proprietary LLMs come with usage terms that can restrict what you can do – for example, some vendors disallow fine-tuning on their most advanced models or limit commercial reuse of the model’s outputs. As of late 2023, OpenAI did not allow customers to fully fine-tune GPT-4, illustrating how using a closed API can constrain customization (Choosing Between Open-Source LLM & Proprietary AI Model). There is also the issue of vendor IP vs. your IP: content you send to or receive from the model could be subject to the provider’s terms of service. Many companies worry about vendor lock-in, where your solutions become tightly coupled to a specific API’s capabilities and terms. If the vendor changes pricing, usage policies, or even discontinues a model (e.g. Google’s replacement of Bard with Gemini forced users to migrate ), your product could be impacted. In-house development with open-source models avoids these issues – you typically own or license the model weights and can use them as you see fit, with no external terms suddenly changing. Open-source licenses are often very permissive (Apache 2.0, MIT, etc.), allowing free modification and integration . For instance, models like Mistral 7B or Falcon are released under Apache 2.0, permitting full commercial use and customization, whereas Meta’s LLaMA models (v1) were released with research-only restrictions . It’s essential to vet the license of any model: ensure it allows your intended use (commercial deployment, on-prem use, etc.) . Overall, open-source models present fewer IP barriers – no required contracts or ongoing license fees – while closed models might impose strict rules on how the AI can be used, necessitating legal caution (Open-Source LLMs vs Closed: Unbiased Guide for Innovative Companies [2025]) (Open-Source LLMs vs Closed: Unbiased Guide for Innovative Companies [2025]).

Compliance and Auditing: Industries subject to strict regulations (e.g. HIPAA for health data, GDPR for personal data, or upcoming AI Acts) must consider how each approach affects compliance. Third-party services may have certifications (SOC 2, ISO 27001, etc.) and provide compliance support, but they offer less transparency into the model’s inner workings and training data. If you need to audit model behavior for bias or explainability, a closed model is essentially a black box – you must rely on the provider’s documentation and responsible AI practices. In contrast, an in-house model (especially if open-source) allows greater scrutiny and control. You can examine or constrain the training data, adjust the model to mitigate biases, and log all inputs/outputs internally for audit trails. This level of control can be important for risk mitigation and meeting legal obligations on algorithmic decision-making. Hugging Face’s 2023 report on open LLMs noted that open models enable easier scrutiny of biases and limitations, helping address ethical and compliance concerns (2023, year of open LLMs) . Moreover, self-hosting means you can enforce data retention policies (e.g. automatically purge certain inputs after processing) which might be impossible with a vendor API that retains data for service improvement.

Risk Mitigation and Vendor Dependence: Relying on a third-party model also means entrusting a piece of your product to an external entity’s reliability. Outages or security breaches on the vendor side are out of your control. For example, if a popular LLM API suffers downtime or an attack, your application may become unavailable (recent incidents like ChatGPT outages due to DDoS attacks illustrate this vulnerability) (The LLM Dilemma: Self-Hosted vs. Public API Solutions). Some organizations see this as a supply-chain risk. Running your own model insulates you from vendor outages – you have only your infrastructure to keep online. On the flip side, maintaining that infrastructure and security is a non-trivial responsibility. In summary, the security/IP decision often comes down to trust versus control: using a third-party LLM means trusting the provider’s platform and policies (with some loss of control and flexibility), whereas building in-house maximizes control over data and IP at the cost of taking on all the security and compliance duties internally.

Scalability

Performance and Latency: Scalability encompasses the model’s performance under load and the ease of scaling it to more users or higher throughput. Third-party LLM services are inherently built to scale – as a customer, you call an API and the provider handles load balancing, serving on multiple GPUs, etc. For many use cases, this provides effortless scalability: no need to architect a distributed system – you simply pay for more usage. However, there are trade-offs. Latency can be an issue with remote APIs; every call goes over the internet, and you may be sharing resources. In high-frequency, low-latency applications (e.g. real-time trading assistant or interactive tools), network latency and any rate limits could hinder performance. Self-hosting a model on-prem or on the edge can yield lower latency responses since the model is physically closer to the application and can be optimized on dedicated hardware . Furthermore, if a public API experiences slow-downs or request queueing under heavy load, you have little recourse; whereas with an in-house deployment you can scale out your serving infrastructure (adding more GPU servers) to maintain performance. In one comparison, companies noted that depending solely on a public API made them vulnerable to its performance issues, while specialized in-house setups (even using the same model) achieved more consistent, predictable latency .

Flexible Scaling and Custom Infrastructure: When you build with open-source LLMs, you have full freedom to choose how to scale – vertically (on bigger GPUs or optimized accelerators) and horizontally (across more instances). Many open LLMs can be run in scaled-down form for efficiency: for example, fine-tuning a smaller model to be domain-specific can allow it to reach comparable quality to a much larger general model, thus serving more efficiently. Recent reports highlight that while hosting a 1.7 trillion–parameter GPT-4 model yourself is infeasible for most, fine-tuning a compact model like Llama-2 (13B) on your domain data can achieve performance on par with larger models for that niche (The LLM Dilemma: Self-Hosted vs. Public API Solutions). Such a model is far easier to deploy and scale (requires less memory and compute per instance) than an API’s giant model. This means in-house solutions can be right-sized to your needs: you might not need a gargantuan model if a smaller one, properly fine-tuned, suffices. You can also use techniques like model distillation, quantization, and sharding to optimize serving. For instance, quantizing model weights (to 8-bit or 4-bit) can dramatically reduce memory and compute costs, increasing throughput. The PyTorch ecosystem in 2024 has emphasized quantization and optimized kernels for LLM inference, enabling large models to run on commodity hardware with minimal speed loss (How PyTorch powers AI training and inference - Engineering at Meta) . Such optimizations are under your control with open models, whereas closed APIs abstract that away (you get whatever speed/cost ratio they provide).

Deployment Strategies – Cloud, On-Prem, Hybrid: How easily each option scales also depends on deployment environment. Third-party APIs are typically cloud-only (you send requests to their cloud). If your strategy involves edge deployment or on-prem integration (for data locality or privacy), open-source models give you the option to deploy anywhere – on your own servers, edge devices, or a private cloud. We are seeing hybrid strategies where companies use open models on-prem for sensitive or high-volume tasks and fallback to an API for special queries or overflow capacity. On the scalability front, cloud APIs excel at elastic scaling – if your workload spikes, the vendor likely can accommodate it (with higher fees), whereas an in-house setup needs provisioning for peak load in advance. Some literature suggests evaluating short-term vs long-term scaling needs: “Will you outgrow the use cases a closed model brings? Can you afford the costs of scaling with an open-source model?” (Open-Source LLMs vs Closed: Unbiased Guide for Innovative Companies [2025]) (Open-Source LLMs vs Closed: Unbiased Guide for Innovative Companies [2025]). This implies thinking about not just technical scaling but functional scaling – an API might offer continually improving models (scaling in capability), while an open model you deploy might become outdated unless you update or retrain it. Ensuring scalability is not only about handling more traffic but also adaptability over time. Open models give you the freedom to upgrade or switch models as the field progresses (since many new models can be adopted in the same infrastructure), whereas with a single vendor you are tied to their upgrade cycle and offerings.

Efficiency and Cost of Scaling: Another consideration is the cost-performance curve when scaling. Proprietary services abstract away infrastructure, but at a premium cost per request. Self-hosting requires investing in capable infrastructure upfront, but once in place, scaling to more queries can be very cost-efficient. Studies indicate that for a given budget, you might achieve higher sustained throughput with a self-hosted model than by purchasing API calls, especially if you optimize the model for inference (The LLM Dilemma: Self-Hosted vs. Public API Solutions) . However, not all organizations have the expertise to achieve maximum efficiency – it requires engineering skill to utilize features like batching, caching of prompts, or even using a proxy setup that directs requests to different models based on complexity (an idea explored by researchers to reduce cost by routing easy queries to cheaper/smaller models and hard ones to expensive models) ( LLMProxy: Reducing Cost to Access Large Language Models). In summary, third-party APIs offer effortless scaling but at financial and flexibility costs, whereas in-house solutions offer tailorable scaling (you choose model size, deployment locale, concurrency levels) but demand engineering effort to realize and maintain that scalability.

Technical Feasibility

Talent and Expertise Requirements: Building or even just self-hosting an LLM in-house requires a significant level of ML and engineering expertise. Organizations must have (or hire) a multidisciplinary team: data scientists to curate and prepare training data, machine learning engineers to handle model training/fine-tuning, MLOps engineers to deploy and monitor the models, and even domain experts and prompt engineers to guide model behavior (Choosing Between Open-Source LLM & Proprietary AI Model) . A recent overview of requirements for open-source AI deployment notes that you’ll likely need a “full-stack AI infrastructure” and dedicated MLOps & AI engineering teams to manage training, versioning, and serving of models . In contrast, leveraging a third-party model or API dramatically lowers the skill barrier – your team can be smaller, focusing mostly on application integration (calling the API and handling responses) rather than model internals. Essentially, using a managed API offloads the hardest ML tasks to the vendor. This is why many teams start with an API for speed to market, especially if they lack deep AI expertise. If you don’t have in-house ML talent, building your own LLM is likely not feasible without a steep learning curve or external help. One guideline suggests “Opt for an API if you lack in-house expertise; building and maintaining an LLM solution requires significant technical skills” (Self-hosted vs Third-party LLMs - NineTwoThree Studio). On the other hand, if AI is core to your business and you want to cultivate that expertise, investing in an in-house team to develop custom models might be strategically worthwhile despite the upfront effort.

Software/Hardware Infrastructure: Training or serving large models demands specialized hardware (GPUs, high-memory instances, or TPUs). Companies opting to build in-house need to acquire and maintain this hardware or provision equivalent cloud resources. Modern LLM training often uses GPU clusters or machine learning supercomputers. For example, an enterprise looking to fine-tune a model like LLaMA-65B will need at least one high-memory GPU (such as an NVIDIA A100 80GB) and ideally multiple for distributed training. The inclusion of new tools has made this more approachable – techniques like QLoRA (QLow-Rank Adaptation) allow fine-tuning 65B models on a single 48GB GPU by using 4-bit quantization ( QLoRA: Efficient Finetuning of Quantized LLMs). This kind of innovation reduces hardware barriers, but it’s still non-trivial to set up. By contrast, if using a third-party model, the entire hardware stack is abstracted away. You do not need to worry about GPU drivers, library compatibility, or scaling clusters – that’s the provider’s responsibility. However, note that even when using open-source models, you don’t have to manage hardware yourself if you use cloud platforms properly: many cloud providers and services (like AWS SageMaker, Azure ML, or Hugging Face Inference Endpoints) allow deployment of open-source LLMs on managed infrastructure. This can be a middle ground: you leverage open-source models (avoiding vendor lock-in on the model itself) but let a platform handle the serving infrastructure. The key point is that in-house LLM development demands a robust engineering environment – containerization, distributed training frameworks (PyTorch/Fairseq, DeepSpeed, etc.), and continuous integration for ML (CI/CD for models). MLOps tooling is crucial: data pipelines (ETL tools like Spark or Airflow for preparing data) , experiment tracking, and monitoring systems for model performance in production. If your company already has mature data engineering and DevOps, extending to MLOps for LLMs is easier; if not, the lack of this foundation will make custom LLM projects slow and risky.

Framework and Ecosystem Considerations: Nearly all cutting-edge LLM work is done in open-source frameworks (primarily PyTorch, with TensorFlow playing a smaller role in recent years). PyTorch’s popularity means abundant community resources and libraries for LLMs – such as Hugging Face Transformers, which provides pre-trained models and utilities, or PyTorch Lightning/Accelerate for managing training. Adopting an open-source model likely means working in this PyTorch ecosystem. Thankfully, PyTorch has become the dominant deep learning framework (with ~63% adoption for model training according to a 2024 survey PyTorch Grows as the Dominant Open Source Framework for AI and ML: 2024 Year in Review | PyTorch). The framework also continues to evolve to support large models (e.g. PyTorch 2.0 introduced compilation and better performance for Transformers, and libraries for quantization-aware training are now available Quantization-Aware Training for Large Language Models with PyTorch | PyTorch). Still, your team needs to be comfortable debugging low-level issues (CUDA memory errors, performance tuning) if pushing the limits. Using a third-party API, in contrast, limits you to whatever interface they provide (often a simple REST or SDK call). It’s technically much easier to integrate (standard REST calls can be done from any language stack), but you lose the ability to tinker under the hood. For some companies, that is fine – they treat the LLM as any other third-party service. For others (especially those with research or unique IP needs), that lack of transparency is unacceptable. Modern MLOps tooling has lowered the barrier to deploy models: there are off-the-shelf solutions for serving (TensorRT, ONNX Runtime, NVIDIA Triton, etc.) and for monitoring (e.g. intercepting prompts and responses to a logging dashboard). Adopting these tools is part of the technical lift of in-house deployment.

Maintenance and Evolvability: Another feasibility aspect is ongoing maintenance. LLMs are not “set and forget” – models can become stale as new data or knowledge emerges, and new model architectures with better capabilities appear rapidly. If you build your own model, you take on the burden of updating it. This could mean periodic re-training on fresh data, fine-tuning to fix shortcomings, or even overhauling your approach if a breakthrough model comes out. By using a third-party API, you offload that worry: vendors will continuously improve their models (for example, OpenAI upgraded from GPT-3.5 to GPT-4 in their API – as a user you could just switch endpoints to get a boost, or they may do it behind the scenes). However, that also means less control over when changes occur; a vendor might update a model and change its behavior in ways that affect your application, and you might have limited options if you can’t self-host the previous version. Some enterprises mitigate this by having a two-pronged strategy: use the API for general capabilities but maintain a smaller in-house model for critical or custom tasks as a fallback that they fully control. Technically, maintaining an in-house LLM will require continuous evaluation and QA processes – essentially treating the model like a software product that needs updates. This is feasible only if you have allocated long-term engineering effort for it. If not, relying on a managed service where the “model ops” is handled by the provider could be more realistic. In summary, the technical feasibility question boils down to whether your organization has the appetite and resources to become an AI infrastructure provider to itself. If not, leveraging existing models (open-source via a partner, or closed via API) can accelerate deployment and reduce risk.

Industry Trends and Case Studies

The decision to build or buy an LLM is playing out across industries in various ways, with recent trends indicating a growing interest in open-source LLMs for enterprise use. A TechTarget–ESG survey in mid-2023 found that the most popular strategy (30% of organizations surveyed) was to utilize an open-source LLM and develop a generative AI solution in-house (ESG-Economic-WP-Dell-Technologies-LLM-TCO-Apr-2024) . In comparison, about 28% planned to rely on a third-party provider’s proprietary model via API, and 23% to use a third-party service built on open-source models . Only a small minority (9%) were aiming to develop a brand new LLM entirely from scratch in-house , which reflects the huge resource requirement for that path. These numbers suggest a significant shift toward open-source: many companies are choosing to fine-tune and host existing open models rather than depend solely on the big AI vendors’ APIs. Drivers for this include cost efficiency and data control (as discussed), but also customization – enterprises often want to inject their domain knowledge or values into an AI model, which is easier when you have the model weights to tweak.

Open-Source Proliferation: The years 2023–2024 have seen an explosion of high-quality open-source LLMs, which in turn has made the “build in-house” option more approachable. Meta’s Llama 2 release in 2023 was a watershed moment – a powerful 70B-parameter model openly available for commercial use (with a relatively permissive license), signaling that even Big Tech sees value in open ecosystems. Meta explicitly took an “open-source route to encourage other developers to use and improve” their models (AI Cheat Sheet: Large Language Foundation Model Training Costs), aiming to benefit from community contributions. Startups like Mistral AI (which released a potent 7B model in late 2023 under Apache 2.0) and projects like Falcon (from UAE’s Technology Innovation Institute) further expanded the menu of open models. This trend means companies no longer face a binary choice between “build a giant model from scratch or use someone else’s” – they can take a pretrained open model and adapt it. Case studies like Stanford’s Alpaca demonstrate the power of this approach: researchers took Meta’s base model and, using a relatively small synthetic instruction dataset, created a ChatGPT-like system for a few hundred dollars (Meet Alpaca: The Open Source ChatGPT Made for Less Than $600) . Following this, enterprises like Databricks built Dolly (an instruction-following model) on open data to avoid vendor lock-in for AI-assisted interfaces. Even large cloud providers are embracing open models: Amazon’s Bedrock service offers not just proprietary models but also hosts models like Stability AI’s and Anthropic’s, indicating demand for diversified model sources . The open-source LLM boom in 2024 also led to a rich support ecosystem (Hugging Face Hub, model registries, etc.), making it easier to find and deploy models tailored to specific needs (e.g. coding, legal, biomedical LLMs) without starting from scratch.

Third-Party Model Advancements: On the other side, third-party LLM providers (OpenAI, Anthropic, Google, etc.) have been rapidly iterating and offering new features that appeal to enterprises. For instance, OpenAI introduced fine-tuning for GPT-3.5 and planning for GPT-4, allowing customers some customization on their closed models. They also rolled out an enterprise tier with guarantees on data privacy (no training on client data by default) and higher throughput. These moves are meant to reduce the barriers that previously pushed companies to open-source (such as data misuse fears and lack of customizability). Additionally, API providers often bring in value-added features like RLHF (Reinforcement Learning from Human Feedback) tuning for better aligned responses, plug-in ecosystems, or multi-modal inputs – features that might be non-trivial to reproduce in-house. Integration with enterprise software is another selling point: for example, Microsoft’s Azure OpenAI Service bundles OpenAI’s models with enterprise security compliance and integration into Azure’s cloud, making it attractive for corporations already using Azure. The trend here is a bit of a convergence: third-party providers are trying to offer more customization and assurances (to feel more like in-house), while the open-source community is producing models that are more capable and user-friendly (to feel more like a ready service).

Case Studies: Many companies have publicly shared their journeys: some started with an API and later shifted to open-source for cost or control reasons. For example, the fintech startup Textio revealed that using OpenAI’s API in production became prohibitively expensive as usage grew, prompting them to fine-tune an open model which cut their inference costs by an order of magnitude (while still delivering acceptable accuracy) (The LLM Dilemma: Self-Hosted vs. Public API Solutions) . On the other hand, companies like Snapchat and Instacart integrated AI features powered by OpenAI models in a matter of weeks – speed that was possible because they sidestepped ML development and tapped into a pre-existing API. Another illustrative case is a large bank that needed an AI assistant but had strict data confidentiality rules: they prototyped with a cloud API to test the concept, then invested in an on-prem GPU cluster to deploy a fine-tuned LLM so that no customer data ever left their network. This two-phase approach (prototype with API, then internalize the solution) is increasingly common, as it combines the fast iteration cycle of “buy” with the long-term benefits of “build.”

Industry surveys also indicate the reasons behind these choices. According to one 2024 report, control and customizability were cited as primary reasons enterprises lean toward open-source LLMs (each cited by ~37% of respondents), with cost effectiveness also a significant factor (26%) (Open-Source LLMs vs Closed: Unbiased Guide for Innovative Companies [2025]). In contrast, those favoring closed third-party models often do so for ease of use and support – having dedicated support and assured maintenance from the provider can “justify the cost, ensuring the model remains updated” for teams without AI expertise (Open-Source LLMs vs Closed: Unbiased Guide for Innovative Companies [2025]) (Open-Source LLMs vs Closed: Unbiased Guide for Innovative Companies [2025]). Notably, an increasing number of tools aim to bridge the gap: for instance, services now exist that host open-source models for you (so you get a private instance of an LLM without having to manage the hardware yourself), combining privacy/control of open models with the convenience of an API. This points to a future landscape where the line between building and buying blurs, offering more nuanced options (like managed open-source).

Conclusion

Deciding between building a custom LLM in-house vs. leveraging an existing model/API is a multidimensional decision. Cost-wise, one must weigh upfront training and infrastructure investments against ongoing API fees and consider long-term TCO under expected usage levels (Choosing Between Open-Source LLM & Proprietary AI Model). Security and IP considerations often push organizations with sensitive data or stringent compliance needs toward in-house, open-source solutions for greater control (The LLM Dilemma: Self-Hosted vs. Public API Solutions) (Open-Source LLMs vs Closed: Unbiased Guide for Innovative Companies [2025]). Scalability and performance requirements will determine if a ready-made API’s convenience outweighs the ability to tailor and optimize an open model deployment for specific latency or throughput needs . The technical feasibility hinges on available talent and infrastructure – building your own LLM stack is rewarding but resource-intensive, whereas APIs provide a shortcut at the cost of some flexibility . Current industry trends show a growing movement toward open-source LLM adoption in enterprises, enabled by the improving quality of open models and the need for data sovereignty, even as API providers enhance their offerings to address enterprise concerns.

In practice, many organizations are finding a balance: prototyping or even deploying with third-party models to get quick value, while simultaneously developing capabilities with open-source LLMs for cost savings, customization, and risk management in the long run . The literature suggests there is no one-size-fits-all answer – the optimal choice is highly context-dependent. Businesses should evaluate their specific use case, budget constraints, regulatory environment, and strategic goals. In a real sense, this is a “build vs buy” analysis where the classic considerations apply: speed and simplicity vs. control and long-term cost . By carefully reviewing the factors outlined above, organizations can make an informed decision that aligns with their needs, possibly even adopting a hybrid approach to leverage the best of both worlds. The good news is that whether one builds or buys, the accelerating advancements in LLM research and tooling mean more options and support than ever before to successfully incorporate AI language models into business operations.