In the world of Large Language Models (LLMs), there is a growing debate between using a vendor’s API vs. a self-hosted model. As the CEO of a tech company deeply invested in LLM, I’ve had the opportunity to explore both. While APIs certainly offer convenience and speed, I firmly believe that, for the long term, self-hosted solutions are far superior in terms of scalability, reliability, and cost-effectiveness.
Serving customers who adopt AI to revolutionize their operations, we at Cybergizer evaluate the whole picture, not only the cost of development or speed. Very often, requirements focus on more complicated critical needs. There are at least 8 reasons why our customers choose self-hosted LLMs over vendors’ APIs.
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1. Data privacy, security, and compliance
Using third-party LLM APIs often means sending sensitive or proprietary data to external servers, raising privacy and compliance concerns. For industries like healthcare, finance, or legal services, this introduces serious regulatory challenges, increasing the risk of data exposure. A 2025 survey by Cisco found that 64% of respondents worry about unintentionally sharing sensitive information when using generative AI.
In contrast, self-hosted LLMs offer full control over data, ensuring that it stays within your infrastructure. This is crucial for companies dealing with personally identifiable information, intellectual property, or classified documents. Self-hosting enables stricter data governance, easier regulatory compliance, and reduced risk of breaches, making it a strong choice for privacy-sensitive apps.
2. The flexibility of customization and fine-tuning
Yes, some API providers (like OpenAI, Anthropic, Cohere, etc.) support fine-tuning of their models, but it comes with certain trade-offs. To name a few: only specific models are available for fine-tuning, increased costs, infrastructure lock-in, fewer hyperparameter tuning options, etc. Besides, fine-tuning a large-scale model on proprietary data can be extremely costly, especially if you need to retrain frequently.
Instead, self-hosted models (e.g., LLaMA, Mistral, Falcon, etc.) can be fine-tuned and customized to meet specific needs with greater flexibility. The training costs will largely be limited to GPU time and storage, with a one-time investment for each fine-tuning task. A 2023 survey from the Linux Foundation found that 57% of organizations plan to customize and enhance GenAI models.
3. Cost efficiency over time
API services like OpenAI, Cohere, or Google Cloud’s Vertex AI charge based on the number of tokens processed. With thousands of large documents to be analyzed within a large enterprise daily, or with chatbots processing tens of thousands of requests per day, this can result in millions of tokens per month.
While self-hosting, indeed, requires higher upfront costs for the initial setup, the cost-per-token and operational costs become cheaper as you scale. Once your infrastructure is ready, ongoing costs include cloud service fees, storage, and energy consumption. So, for high-volume usage, self-hosting can become more cost-effective. This can make a difference for e-commerce platforms, banks, payment systems, governmental organizations, telecom, etc. Millions of users, thousands of documents, myriads of tokens.
4. Availability under control
However, when it comes to a large number of tokens, cost isn’t the only concern. With APIs, there are uncertainties around availability, stability, and reliability, especially when you deliver new releases, experience a sudden spike in traffic, user activity, and the number of API calls. You have no control when the provider has internal issues, and sometimes no visibility into root causes. In addition, LLM APIs may change models behind the scenes or deprecate endpoints, which can lead to unexpected behavior or incompatibility.
Self-hosting eliminates dependency on external services, potentially leading to better reliability and uptime. If the internal network is stable, the LLM service remains available. On-premises setups can be built with highly available clusters, load balancers, and failover nodes, all under your control.
5. Lower latencies
No matter how much you optimize, API-based services require sending data to remote servers, inevitably introducing latency. In high-traffic or real-time applications, even a small delay can accumulate at scale, leading to poor performance. In critical applications, this lag can affect user experience.
When hosting the model locally or closer to the user, latency is much lower, especially when utilizing edge servers or colocated GPUs. If you host your model on a dedicated machine with high-speed local storage and an optimized network configuration, you can achieve real-time processing and reduce the end-to-end response time.
6. Scalability without vendor restrictions
Scaling your usage of an API often comes with restrictions and price increases. There’s a risk of API rate limits and service interruptions during high-demand periods, which can impact your application’s availability and reliability. When you hit these limits, requests are delayed or rejected, which is totally unacceptable for real-time applications.
With self-hosting, you can scale horizontally (by adding more machines) or vertically (by upgrading hardware) without worrying about rate limits. You are also in complete control of load balancing and distribution, allowing for more consistent performance during peak demand periods.
7. Edge AI and offline scenarios
At the same time, there are scenarios where relying on APIs doesn’t make sense even if you don’t need to comply with regulations or the request volume is low. APIs are not designed for environments with limited or no connectivity, which makes them unsuitable for field deployments in industrial IoT, mobile health devices, or offline autonomous systems.
Vice versa, self-hosting allows you to deploy LLMs directly on edge hardware. Models can be optimized (e.g., quantized) to run on smaller devices while maintaining high accuracy. This ensures low-latency, private, and resilient AI—ideal for use cases like offline document summarization, localized assistants, or compliance-sensitive apps where data must stay on-premises.
8. Avoiding vendor lock-in and dependence
Finally, relying on a third-party provider means that your operations are tied to their terms of service, pricing changes, and the availability of certain features. If the provider changes their pricing model or discontinues a key service, your business could face significant challenges. Add here costs of migration, cost of delay, etc.
Instead, with self-hosting, you control updates, upgrades, and infrastructure, so you are not dependent on the provider’s decisions.
Actually, this list can be continued, but these were the main concerns raised by our customers and business partners.
What to choose?
I truly believe that there’s no single solution for complex problems, and choosing between self-hosted LLMs and APIs depends on specific requirements. From a C-level perspective, companies should conduct a thorough cost-benefit analysis and consider their unique needs before making a decision.
Here’s a comparative table to help you make the choice.
| Criteria | Self-hosted LLMs | API-based LLMs |
| Data storage | On-premises or private cloud | Vendor’s servers |
| Data access | Controlled by you, access logs and permissions management | Vendor has access, governed by their terms of service |
| Compliance and privacy | Easier to meet regulatory requirements like GDPR or HIPAA | Controlled by you, access logs, and permissions management |
| Model training | Full control over training data and process (e.g., using PyTorch) | Limited or no control, often a black box |
| Model fine-tuning | Highly customizable with techniques like LoRA or QLoRA | Limited or restricted to provided parameters |
| Initial cost | Higher (hardware/GPUs) | Lower (pay-as-you-go for tokens) |
| Long-term cost | Fixed or lower incremental costs (electricity, maintenance) | Variable, scales with usage, can become high for large volumes |
| Dependency | Independent, relies on internal infrastructure | Dependent on vendor service and their infrastructure |
| Uptime | Controlled by you, can aim for 99.99% with redundancy | Depending on vendor’s practices and server location, data exposure risks |
| Latency and response times | Lower, data doesn’t travel outside the network, processed locally | Subject to the vendor’s uptime guarantees, which may vary |
| Real-time use | Optimized for real-time response with full control over latency and throughput | Not reliable for real-time apps—rate limits and API lag may cause disruptions |
| Scalability and throughput | Fully controlled by your team, scale horizontally or vertically | Limited by vendor’s rate limits and quota policies |
| Performance at peak load | Resources are dedicated and tuned to your workloads | Can degrade due to shared infrastructure or throttling |
| Offline capability | Works without Internet, ideal for remote or secure environments | Requires constant connectivity, unusable offline |
| Edge device flexibility | Deployable on edge hardware, models can be optimized | Fixed to vendor infrastructure, limited to cloud environments |
| Control over infrastructure | Full control over infrastructure, updates, and features | Variable, depends on the network and the vendor’s server load |
| Cost stability | Predictable costs tied to infrastructure and scale | Subject to price changes, pay-per-use rates, and potential tier limits |
| Migration risks | No dependency means no forced migrations or vendor-driven disruptions | Bound to the provider’s roadmap, terms, and feature availability |
In brief, for quick, low-volume usage (for instance, for evaluating feasibility), APIs can be more convenient. However, if data privacy, compliance, customization, fine-tuning, availability, and long-term benefits are priorities, self-hosting is often the better choice.
When operating at scale or in need of customization, self-hosting provides the necessary control over data, performance, and budget. The initial investment in infrastructure pays off as your AI usage grows, and with the ability to fine-tune models and scale as needed, self-hosting gives you a level of flexibility and performance that APIs simply can’t match.
About the author
Sergy Sergyenko is CEO at Cybergizer, a software company specializing in app scaling and helping businesses grow faster. Find out how Cybergizer can support you—by scheduling a call via cal.com.
