Why Local Custom Built Solutions Outperform API-Based LLM Services

Have We Been Oversold on Potential LLM Applications?

Large Language Models (LLMs) like ChatGPT, Claude, and DeepSeek are impressive tools with world-shifting implications. Every day, we see real-world effects — some good, some bad. Meanwhile, there’s a wave of hype and speculation about the “agentic” future of AI. Headlines, fearmongering, and misunderstandings often lump every AI technology together in ways that dramatically shape both public perception and corporate strategy.

I don’t blame companies or corporate America for showcasing their LLMs as potentially becoming AGI “soon.” After all, many of them lose money running these models — subscription fees can offset only so much, and the profitability path for LLM-heavy businesses is murky right now.

We’re collectively being oversold on what current LLMs can truly accomplish and at what cost. Yes, they can be mind-blowing, especially when guided by someone knowledgeable about a given subject. But that doesn’t mean they’re remotely ready for full production usage in the ways sensationalist media and certain CEOs imply. These systems have real utility, yet handing them oversight capabilities, full network access, or real power in critical workflows is largely a pitch to impress investors — one disconnected from what actual research reveals.

Despite talk of an AGI race, true AGI is likely far off and would require a multi-modal approach involving more than language alone — refined to a point where it becomes cost effective at scale. Meanwhile, many commercial LLMs remain opaque, with biases potentially introduced by their makers or through accidental overfitting. There’s no guarantee a generalized LLM provides consistently accurate, cost-effective solutions.

There’s More Than LLMs & Generative AI

LLMs are just one branch in a broader AI ecosystem. While they excel at generating human-like text, they’re not a universal solution and can be prone to inaccuracy, especially with specialized or brand-new data. This is often referred to as “hallucination,” though I find that term too anthropomorphic.

Some research suggests LLMs could “scheme,” another example of anthropomorphizing. In reality, these models don’t self-motivate; their outputs reflect the biases, data, and directions given by humans. We are creating these models from nearly all written knowledge — fiction and non-fiction — plus deep software and system documentation in every language on Earth. They’re not “thinking”; they’re transforming data inputs into outputs.

For tasks like image recognition, anomaly detection, predictive maintenance, or IoT processing, specialized AI models typically excel. Relying solely on a generalized LLM risks inconsistent results, higher costs, and data exposure to third parties. In contrast, specialized AI solutions — particularly those fine-tuned and deployed locally — tend to be more reliable, secure, and cost-effective.

Data Privacy and Security

One major advantage of local or on-premises AI solutions (including OSPR setups) is data control. Keeping training and inference within your private network significantly reduces the risk of data leaks or misuse that can occur when sending information to external API services.

This matters if your data is proprietary or involves customers — maintaining it in-house lowers the chance of exposing critical information. Many startups build around ChatGPT or Claude APIs, offering niche benefits but still relying on generalized models. Those aren’t designed for heavy industrial or mission-critical contexts and remain purely web-based for narrow tasks. If data security and industrial-grade stability matter, local solutions are often the better choice. Keeping your proprietary data off large, generalized public models is a key benefit of solutions like OSPR.

Tailored Training for Better Accuracy

Custom-trained models can be repeatedly fine-tuned with real-world data from your environment. This domain-specific approach often yields higher accuracy in detection, classification, and optimization, compared to a single large model generalized across millions of scenarios.

For example, an OSPR model trained on images of your own products can detect defects, anomalies, or workflow issues far more reliably than an external, general-purpose system. This specialization also leads to faster processing, as the model doesn’t waste resources on irrelevant data.

LLMs and VLMs — Prone to Hallucination

As noted earlier, despite their capabilities, LLMs like ChatGPT, Claude, and DeepSeek are not foolproof and are prone to what is commonly referred to as “hallucination” - though it does not actually resemble human hallucination in how it occurs. LLMs can output misinformation, simply because they rely on patterns from massive training sets — lacking real-time fact-checking or actual comprehension.

In mission-critical deployments, even one nonsensical or fabricated response can cause serious problems, from misinformation and downtime to security issues. Without adequate supervision, constant human review, or targeted fine-tuning, integrating an LLM into essential workflows can be extremely risky.

Cost Effectiveness and Scalability

A striking illustration of LLM overhead is OpenAI’s claim that its o3 model reached 87.5% on the ARC-AGI (Abstraction and Reasoning Corpus) test, which assesses whether an AI can handle new, unseen tasks with human-like reasoning. While such results sound promising, the resources required to scale and run these massive models effectively can be exorbitant with OpenAI’s own numbers showing a cost of $6,677 to conduct the test in high efficiency mode scoring 82. - with the low efficiency mode scoring of 91.5%. OpenAI did not list the total cost for low efficiency mode but says it uses 172x the compute power of low compute (high efficiency) mode. This means OpenAI paid over $1,000,000 to run the test and reveals that a typical request to the model could cost as much as $1000 per query or perhaps much more.

Even less computationally intensive models like 4o and Claude Sonnet are typically quite expensive for businesses to run over API as a solution to their IoT or internal needs with the API services being better suited for SaaS businesses for creatives and offices looking to streamline certain processes with signicantly lower query volume and users compared to enterprise production environments.

It is expensive to run these LLMs at enterprise production scale over API, even when using less computationally taxing models. Financially it is not feasible for most businesses, especially if the plan is to use them for complex business tasks at the edge. It especially does not make sense when local, specialized AI solutions are more predictable and efficient. They can run on modest hardware without incurring the potentially high fees or variable resource demands of cloud-based APIs. This allows companies to manage costs more effectively while maintaining the reliability and performance required for critical operations.

Compute Is On The Way!

Over the coming decade, one can expect all of the largest companies in artificial intelligence to expand their computing capabitilities with new datacenters and more chips at their call and in some cases their own power plants to run it all. Thus, one can expect the pricing for using some of these models to go down over time.

As that happens we will see more proliferation of these LLMs as the underlying mechanism in various apps, software products and experiences - but that doesn’t mean that they’re the solution or the best choice for all of the unique applications for AI out there - especially business applications at scale. LLMs should be treated like any other AI tool in a developers toolbox, and companies should always consider the full array of technologies available.

Conclusion

Between now and the fabled time when true agentic AGI is achieved there is a mountain of research, development and computing advancement that still needs to be done while the AI solutions that most businesses need for their business processes are technologies that are much longer standing, more applicable than LLMs - and much cheaper to deploy and run.

For more precise, domain-specific operations where accuracy, reliability, and confidentiality are paramount, localized AI models excel. By deploying custom solutions trained on your own operational data, you keep your data internal preventing round trips over the internet to LLMs, prevent the usage of your data in large generalized public facing models, boost the predictive power of the models you deploy, drastically cut operating costs, and you gain genuine peace of mind.

Generalized LLMs and VLMs are handy in certain text-focused scenarios, but they simply cannot match specialized local models that know your environment and have well defined goals. If you value data security, high performance, and predictable costs, a localized, domain-focused AI strategy with OSPR is a clear way forward.