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The explosive rise of AI has driven a burgeoning portfolio of AI components, services, tools and capabilities. It has also spawned a new age of vendor lock-in.
Third-party technology providers can lower costs, speed time-to-market and enable vital capabilities that businesses can’t undertake on their own. While this ecosystem of technologies and services is healthy when businesses can pick and choose from a variety of providers and exchange them with minimal difficulty, it becomes problematic when a business must commit to a single provider. This can create an uncomfortable dependency on one provider, or the business can’t readily switch providers without incurring major costs, risks or technical limitations.
AI vendor lock-in is common across the technology industry as providers seek to differentiate themselves and compete for market share. Although business leaders might not be able to prevent some AI vendor lock-in, they can identify where it is most prevalent and take steps to mitigate its worst effects.
What vendor lock-in looks like in the AI space
AI vendor lock-in is the undesirable dependence a business has on a specific AI provider’s infrastructure, models, data or tools. Dependence occurs because different providers’ offerings are typically not interchangeable. A business is free to experiment with various providers to identify the offering best suited to its AI project needs, but implementing a certain provider’s offering typically requires a level of commitment — such as using its unique API or models.
AI vendor lock-in occurs in two main instances:
- A provider is the only source of vital infrastructure, models, data or tools that a business needs. There are simply no other options, so the business must deal with lock-in.
- Changing to an alternative provider requires costly and time-consuming recoding, retraining and operational disruption to the business or its AI platform. The pain of changing AI providers is often greater than staying with the current provider.
Lock-in is often by design. Leading technologies like AI are viciously competitive. In an industry where differentiation is often the key to a provider’s survival, there is little strategic incentive for different AI providers to collaborate on standardizations that would effectively make their competitors more accessible. Standardization only occurs after commoditization, when providers’ offerings are no longer a strategic differentiator.
AI vendor lock-in can put a business at a strategic disadvantage. For example, an organization might choose to use a large language model (LLM) from a third party to build an AI customer service platform. But the resulting AI platform can only be as good as the third-party LLM. If the LLM is not trained, tuned and updated in ways that optimize the business’s use case, AI accuracy and performance might suffer. Similarly, the uptime and availability of that LLM can directly affect the dependent AI platform.
Types of AI vendor lock-in
So, where does AI vendor lock-in occur? Several insidious traps can snag an unsuspecting business, including the following:
- Infrastructure. Machine learning (ML) models demand extensive IT infrastructure — often far more than a typical enterprise can provide and maintain. This scale alone can drive lock-in with public cloud providers. Additional lock-in can occur with specialized computing platforms such as advanced GPUs, TPUs or NPUs, which might be more costly or less available through other providers.
- APIs. An API enables the exchange of data and commands between two systems, as well as interoperability between a user and provider. Since every provider builds and runs its own proprietary API set, businesses must code their software to use the intended API effectively, locking them into that provider’s API. Switching to a different provider and using its different API requires recoding the AI system.
- Models. When ML models require talent and resources that a business can’t support, the business might opt to pay for a commercially available ML model. Using a third-party model creates a dependency on that model, and any disruption to its access adversely affects the business’s AI system.
- Data sources. Models require vast data sets that a business might not have in-house. Open source data can sometimes be obtained from recognized sources. In other cases, synthetic data can be obtained on demand from providers such as Gretel.ai, Mostly AI, Syntho and K2View. However, industry-specific or limited data might carry a hefty price tag and daunting intellectual property restrictions in the user agreement.
- Data storage. Cloud providers have long charged data egress fees for moving data out of their cloud infrastructure. These fees can be enormous for large ML data sets. Similarly, some data storage systems might use proprietary formats that can’t be readily exfiltrated outside the provider’s environment.
- Tools. AI providers routinely offer tools to aid in building, training, testing and monitoring resources, such as vector databases or logging tools. Those tools are typically proprietary to the provider’s environment and can become so deeply ingrained in business workflows that switching to new or different tools might seem daunting.
- Contractual obligations. AI providers frequently impose contracts that can include multiyear commitments, step-pricing structures and renewal terms that could create a financial burden for businesses seeking to switch providers. It’s important to review contracts carefully and negotiate terms that align with the organization’s goals.
Disadvantages of AI vendor lock-in
Some amount of lock-in is commonplace across the technology industry, and it can be unavoidable in high-end, rapidly evolving technologies such as AI. But it’s important to understand some of the specific disadvantages of AI vendor lock-in, which include the following:
- Less innovation. A business is limited to the features and capabilities of the providers’ offerings. If a provider’s offering is less competitive, resulting AI performance might be less competitive, and changes or updates might arrive at a lower frequency. Compare similar offerings and consider providers’ roadmaps for future offerings.
- Regulatory risk. AI is increasingly subject to regulatory requirements that demand transparency into AI components. A proprietary third-party AI provider is unlikely to provide this insight, leaving the business at risk of compliance violations due to black-box AI behavior. Ensure contractual agreements with AI providers support any required transparency and explainability.
- Data migration challenges. Different AI providers might use unique data formats. This proprietary environment — and the tools designed to operate within it — can lock data to one provider and make it challenging, or sometimes impossible, to migrate data to another AI vendor. Look for open data formats, open source tools and vendor-agnostic APIs to help minimize data lock-in.
- Business disruptions. Service outages can directly affect a business’s revenue and reputation. A provider might also elect to change or deprecate certain services that are vital to a business’s AI system. Changing or deprecating a vital capability can be catastrophic for an AI project — especially if an alternate provider is not readily available. Maintain open lines of communication with the provider and watch for changing trends in its business operations that might signal instability or future disruptions.
- High costs. Healthy competition among providers helps keep their costs lower. Environments where competition is slight and lock-in is common typically lead to price inflation with little negotiating power.
How to avoid AI vendor lock-in
While some AI vendor lock-in might be unavoidable, these seven strategies can help mitigate the dangers of lock-in and minimize disruptions when change is needed:
1. Understand dependencies
Vendor lock-in can be insidious, gradually taking hold in small degrees over time. Business and technology leaders can get ahead of disruption by conducting regular dependency audits to identify which AI system components depend on third-party providers. Recognizing these vulnerabilities early can lead to future build vs. buy discussions aimed at reducing lock-in.
2. Create a vendor offramp
Don’t wait for provider disruptions or business pain to plan an exit strategy. For each dependency, consider available alternatives and plan for migration as part of AI system design. Proactively experiment with and test alternative providers, and be ready to implement them before a provider relationship sours. For example, negotiate exit clauses in AI vendor contracts that ensure data portability. This might not prevent provider disruption, but it can minimize impacts.
3. Use modular software architecture
Design and build AI systems using modular software architecture, such as microservices. Platforms like Docker and Kubernetes not only facilitate modular software, but they also enable packaging dependencies and orchestrating container operations across multiple infrastructures, reducing reliance on specific vendor environments. Modular AI stacks also make it easier to replace a third-party provider’s services while minimizing the work required to update the rest of the AI system.
4. Use abstraction layers
Avoid direct connections between the AI application and third-party components such as ML models. Abstract this relationship using mechanisms such as AI gateways to abstract APIs or frameworks such as LangChain. Abstracting — or designing the AI platform in layers — breaks direct dependencies and enables businesses to swap other components more easily by changing configurations rather than underlying code.
5. Adopt open standards
Lock-in can be most dangerous with proprietary infrastructures and platforms, so consider focusing on open source models and frameworks for AI components. Common open source elements include Open Neural Network Exchange, Model Context Protocol (MCP), OpenLLM and Hugging Face Transformers. Further, consider adopting standard data formats intended to provide data portability, such as Apache Parquet, or open source observability frameworks, such as OpenTelemetry. Open source elements are community-developed and can be readily modified to meet specific business needs.
6. Adopt a multi-cloud strategy
Cloud providers are vital suppliers of AI-related infrastructure and services. A multi-cloud strategy serves two principal goals. First, it enables backups or alternative cloud services in the event of downtime or disruption by switching between providers. Second, it enables the use of best-of-breed infrastructure and AI resources by using multiple providers simultaneously.
7. Follow standardization trends
Standardization of AI technologies and services requires close collaboration from AI industry leaders. Fledgling collaboration efforts are already taking shape through industry groups such as the Agentic AI Foundation (AAIF). The AAIF launched in 2025 with input from OpenAI, Anthropic and Block and provides an open, vendor-neutral foundation that can drive standards and protocols such as MCP to ensure interoperability, open access and freedom for AI developers and adopters.
Stephen J. Bigelow, senior technology editor at TechTarget, has more than 30 years of technical writing experience in the PC and technology industry.