The way you acquire AI – whether building in-house, leveraging open source solutions, or outsourcing to a specialist AI provider – requires careful alignment with your business goals, budget, risk tolerance, technical maturity, talent acquisition, and more. It’s not a decision to be taken lightly; the approach you choose can impact everything from how fast you get to market to what your AI budget will look like in a decade’s time.
While there isn’t a single solution that will be right for every enterprise, we’ve taken a look at three models below to help you consider whether building in-house, leveraging open source, or outsourcing might be the best approach for your business. Of course, whichever approach you take, you’ll need robust API management to simplify integration and accelerate innovation – so we’ve added a quick introduction to Tyk AI Studio, too.
Three AI acquisition models: Pros and cons
The need for flexible, strategic AI capability acquisition is becoming ever more urgent, as enterprises in all sectors seek to harness the power of the latest technology. We looked at this in detail in our recent ebook – AI, APIs and AI readiness – which you can download for free to dive into the topic in more depth. For now, let’s look at three ways you can do so and what the benefits, and drawbacks, of each approach can be.
Building in-house
If your AI strategy, business goals and security/compliance obligations require you to retain a high degree of control and customization within your enterprise, building your AI system in-house might be the best solution. Doing so has a range of benefits, with one of the main advantages being that you can keep sensitive data within the boundaries of your business. Another benefit is that you can custom engineer your AI to integrate beautifully with your existing ecosystem and workflows. With the right approach, your AI can become a unique solution that serves as a competitive differentiator, giving you both proprietary advantage and an exemplary level of control.
On the flip side, building an in-house AI system is neither fast nor cheap. You’ll need to recruit and retain suitably qualified AI experts to achieve the solution you envision. If a team member leaves, you’ll lose their knowledge of your unique system and have to start training their replacement from the ground up. Not using an off-the-shelf solution means there’s no chance of you recruiting anyone with knowledge of your system who can hit the ground running.
The time it takes to build in-house is another drawback. It can take much longer to get to market than if you go down the route of buying in an AI service. Plus, both the initial project and the inevitable scope creep can chew through budget and other resources at an alarming rate. Building in-house also runs the risk of strategic misalignment, with the AI system you end up with not quite fitting with your chosen strategy or operational capabilities and processes.
Leveraging open source
It’s no secret that Tyk is a big proponent of open source – our journey began with our open source API gateway, which continues to power seamless integrations, enable rapid innovation and drive success to this day.
In terms of acquiring AI capabilities, there’s a compelling case to go down the open source path. Doing so can be far more cost-effective than either building in-house or outsourcing. You can access cutting-edge AI technology, along with expert support from a keen and engaged community with hands-on experience across diverse industries.
Choosing open source provides you with the ability to modify and audit code, so you can experiment and move flexibly, with a high degree of transparency, which may be handy from a security and compliance standpoint. This agility can support rapid innovation and a faster route to market.
However, while you can create prototypes with open source pre-built models and tools, you are unlikely to have the same degree of customizability that you could achieve with building in-house.
An open source approach also doesn’t negate the need for you to recruit and retain suitably qualified AI experts. As such, you’ll need to factor in time for finding the right people, as well as the expense of payrolling them. Compatibility issues with your existing systems could also cost you time and money, particularly if you have bespoke, proprietary, and legacy systems, rather than a modern infrastructure built around open standards. Bugs, security vulnerabilities and compatibility glitches can drain time and budget scarily fast. And if the open source community can’t help solve your issues, you might find yourself scrabbling for appropriately knowledgeable support.
Outsourcing
Buying in a solution delivered by AI specialists with top-tier knowledge of the system means you can move at pace, without having to worrying about the time it takes to recruit a whole team of in-house experts. You can deploy the pre-built solution rapidly, task the vendor with resolving any troubleshooting along the way and get to market at lightning speed. All at a far lower initial cost than building in-house.
Outsourcing can also be far less of a distraction from your core business operations than the other approaches we’ve discussed. You’ll need to weigh up the potential advantages of this against the potential downsides of outsourcing. You will have less control over customization, both initially and longer-term; while it might not be a problem now, it might be in five years’ time, when technology has moved on and you need more flexibility than the system provides in order to harness new opportunities.
Data privacy and security also demand significant thought when buying in an AI system, particularly in terms of the control, ownership and visibility you will have over your sensitive data flowing through those outsourced components.
Vendor lock-in is another important consideration. Buying in an AI system likely means committing to that vendor’s products for many years to come. This can result in significantly rising costs, as well as a lack of agility to choose best-in-class new tooling from other vendors.
Key considerations
When working out the right AI acquisition path for your enterprise, consider the above approaches with a focus on:
- Business goals
- Talent acquisition – from technical experience to data science and machine learning specialists
- Budget
- Risk tolerance
- Technical maturity
- Technical complexity/ecosystem compatibility
- Data privacy and security
How to future-proof your enterprise with AI and API alignment
API management plays a key role in enabling seamless integration, robust security, and deep observability not just of your APIs but of your AI systems. With the right API management platform supporting your approach to AI, you can implement it in a way that’s both seamless and sustainable.
Tyk AI Studio can help. Itss a comprehensive platform that enables you to manage and deploy AI applications with enterprise-grade governance. With an AI gateway, AI portal, and AI chat interface, along with centralized AI management, it provides all you need to manage, govern, and interact with AI across your organization. You can govern AI with role-based access control, rate limiting, and audit logging, while monitoring usage, costs, budgets, and performance in real-time. Whichever AI acquisition route you choose, Tyk AI Studio enables you to manage how your LLMs are accessed and used, ensuring compliance with global privacy regulations through customizable data flow management.Why not take a look at our Tyk AI Studio demo to explore the advantages while you map out your AI strategy?
