Can Owning the AI Inference Layer Reduce the Cost of Building AI Applications?

As AI workloads become more complex, startups are exploring alternatives to external language models to control costs and infrastructure. The challenge is proving that proprietary inference technology can deliver comparable results at a lower total cost.F

AI models are getting cheaper, but building and running AI applications is not necessarily becoming more affordable. Gartner warned in August 2026 that inference costs per agentic AI workflow could rise more than fivefold by 2028, as increasingly complex tasks consume more computing resources despite falling model prices. The forecast highlights a growing challenge for AI developers: lower costs per model call do not automatically translate into lower operating costs when applications require multiple rounds of reasoning, analysis and execution.

That tension is creating an opening for companies exploring alternatives to AI systems built around external large language models (LLMs). Turkish startup MLVisor is pursuing one such approach, developing its own inference engine to handle core computation rather than relying on a third-party language model for the entire process. Its proposition is that owning this underlying technology could give businesses greater control over cost, accuracy and data handling. Whether that translates into a measurable economic advantage, however, will depend on how the technology performs against established alternatives.

Berkay Başeski, co-founder of Turkish AI startup MLVisor, believes the competitive advantage will increasingly depend on the technology beneath the user interface. “Generating a chart or a model from a sentence is quickly becoming a commodity. What we think will matter is whether the result can be trusted, how much it costs to get there, and where the data has to live,” Başeski said while conversing with AsiaTechDaily.

Inference is the process through which a trained AI model processes inputs to produce outputs. For applications using LLMs, costs can include model API charges, computing infrastructure, and the repeated model calls required to complete complex tasks. Businesses have several options for managing these expenses. They can use externally hosted models, deploy open-weight models on their own infrastructure, or optimize how existing models run. Another approach is to develop specialized inference technology that can perform particular tasks without depending on a general-purpose LLM.

The last option is the approach MLVisor is pursuing. However, eliminating external API fees does not automatically make an application cheaper. Proprietary technology still requires development, infrastructure, maintenance, and testing. The relevant measure is the total cost of producing a reliable result, not simply the cost of an individual inference request.

McKinsey’s June 2026 analysis of AI inference economics identified model optimization, specialized chips, and improvements across computing infrastructure as important opportunities for reducing costs. These developments suggest that startups must compete against increasingly efficient AI systems, not just the pricing of external model providers.

MLVisor’s bet on proprietary inference technology

MLVisor is developing an AI production studio designed to automate data preparation, analysis, model building, and visualization. Its underlying technology, VisorEngine, is described by the company as a probabilistic inference engine that uses mathematical search rather than relying on an external LLM for its core computation.

“Our most defensible part is the engine. VisorEngine is a probabilistic inference engine we have been developing for about three years. It is not a wrapper around a language model. The language model only understands the request, while the engine searches for and verifies the solution,” Başeski said while conversing with AsiaTechDaily.

The distinction is important. Conventional inference software, such as systems that optimize the serving of existing LLMs, improves the efficiency of running those models. MLVisor’s stated approach is different: it aims to solve suitable analytical problems through its own inference process. If effective, such an architecture could reduce reliance on external model providers for particular workloads. The company also says VisorEngine can run within customer-owned infrastructure, potentially appealing to organizations that want greater control over data processing. But a specialized engine is not necessarily a replacement for an LLM across every application. Its commercial value depends on the range of tasks it can solve, the quality of its results, and the resources required to operate it.

Lower costs must be demonstrated, not assumed

MLVisor says internal testing on a real e-commerce dataset produced accuracy close to that of large general-purpose models at a lower cost per task. The company has explicitly acknowledged that these tests have not undergone an independent audit.

“In our own tests (we want to be clear these are our tests, not an independent audit) this approach reached accuracy close to the large general-purpose models on a real e-commerce dataset at a much lower cost per task. Because we own the engine, we can also run the same technology inside a customer’s own infrastructure, which matters for data-sensitive organizations,” Başeski said while conversing with AsiaTechDaily.

The claim is potentially significant, but its implications depend on the methodology. A meaningful comparison would need to establish which models were tested, what tasks they performed, how accuracy was measured, and which infrastructure costs were included. The competitive landscape also complicates the proposition. Open-weight models and improvements in inference software offer alternatives to expensive proprietary APIs without requiring companies to develop an entirely new engine. For MLVisor, the question is whether its approach delivers a measurable advantage over these options.

Benchmarking will determine the commercial case

Independent evaluation will be critical to establishing whether proprietary inference technology can provide a sustainable cost advantage. Benchmarks need to measure not only accuracy, but also execution time, resource consumption, reliability, and the cost of completing equivalent tasks. MLVisor’s roadmap lists a public benchmark for VisorEngine v3.0 in the fourth quarter of 2026, followed by a planned production-studio release in January 2027. These remain planned milestones unless the company confirms their completion. A transparent benchmark could help potential customers assess the technology beyond its internal performance claims. It would also clarify which workloads benefit most from the architecture and where conventional LLMs remain more suitable.

Owning the inference layer can give startups greater control over their technology, deployment choices, and cost structure. But ownership alone is not a competitive advantage if an alternative engine cannot match the quality, reliability, or economics of established systems.

The opportunity may lie in choosing the right architecture for each task rather than expecting one model to handle everything. General-purpose LLMs will remain useful for many language-intensive applications, while specialized inference engines could offer advantages for particular analytical workloads. For MLVisor, the next step is to demonstrate those advantages through reproducible results and real-world deployments. More broadly, the economics of AI application development will depend not simply on who owns the inference technology, but on who can deliver reliable results at a lower total cost.

Quick Takeaways

• AI inference costs could rise more than fivefold by 2028 for agentic workflows, despite falling model prices, according to Gartner.
• MLVisor is developing a proprietary inference engine, VisorEngine, designed to handle core computation without relying on an external LLM for the entire process.
• The startup aims to reduce the cost per task while maintaining accuracy comparable to general-purpose AI models.
• Its approach could give businesses greater control over data processing by enabling deployment within their own infrastructure.
• The key challenge is proving the cost advantage. MLVisor’s current performance claims are based on internal tests, not an independent audit.
• Independent benchmarks and real-world deployments will be crucial to establishing whether owning the inference layer offers a viable alternative to existing AI solutions.