AI

AI as a Service (AIaaS) in the era of “buy not build”
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Imagine you are the CTO of a company that has just undergone a massive decade long digital transformation. You’ve built out a team of 100+ software engineers that are really starting to deliver substantial revenue growth and cost savings to the business.  

Each time you’ve faced a buy vs build decision your team of talented engineers has told you: “hold my beer, we’ve got this.” They argue “why spend hundreds of thousands on a SaaS vendor when our team can roll out an open source alternative on AWS and run it ourselves?” And it has typically worked, saving the company money that can be reinvested back into hiring more engineers.  

It was hard enough to do the total cost of ownership (TCO) calculus for traditional SaaS software: How much are we spending on AWS now and how come we need so many engineers just to keep the lights on? Is this really saving us money?

But now, AI is changing everything. Your engineering teams now want to start hiring data scientists and building AI capabilities. And you’re faced with more buy vs build decisions.The same SaaS vendors you thought you could out-build for less cost now have powerful new AI features and functionality. 

The good news is the buy vs build decision is more straightforward in the world of AI.

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Top Reasons why you should buy your AI service rather than build it yourself

  1. Talent: It’s really hard to build a great data science team
  2. Speed: It takes a long time to develop data science capabilities.
  3. Culture: You don’t have a culture of experimentation
  4. Data: You don’t have enough data to compete
  5. Agility: AI is moving very fast, can you keep up?
  6. Reliability: It’s hard running AI systems in production

1. Talent: It’s really hard to build a great data science team

Hiring a team of talented data scientists is really hard. The race for AI talent has driven up the overall costs of hiring and retaining staff, particularly from top university PhD programs. Are your company’s pockets as deep as big tech, and can your company’s brand compete with the Google’s and OpenAI’s of the world? 

There is a risk you might end up with a below average data science team if you can’t win the war for talent.

2. Speed: It takes a long time to develop data science capabilities.

It takes a long time to build a team from scratch, with hiring lead times at 6mo+. It takes data scientists an especially long time to onboard, as the complexities of the work and data require experimentation to gain experience. 

It takes even longer to mature a new capability like data science inside your organization: building tools, processes and capabilities that never existed before. You should expect 2 years or longer from the decision to hire to having the first products live. Can your company wait that long?  

3. Culture: You don’t have a culture of experimentation

To succeed in building AI, you need to have a culture that embraces scientific experimentation.  Learning in production is the only way to build and optimize models. This culture is very different from building software, where the solution (and delivery timelines) can often be explained in great detail before a line of code is written. 

AI, on the other hand, means failing (repeatedly) in production and not knowing if the next experiment is going to work or not. Many companies simply can’t develop and nurture this culture.

4. Data: You don’t have enough data to compete

You need a lot of data to train AI models from scratch. It is quite  likely you don’t have as much data as a vendor who is able to leverage the data from thousands of customers to create a model that performs significantly better than yours. 

Without scale in data it takes a long time to test our new experiments and your algorithms will improve slowly. Data is the lifeblood of an AI team and most companies simply don’t have the scale to compete.

5. Agility: AI is moving very fast, can you keep up?

The AI industry is moving extremely fast. If you choose to build your own capabilities, you’ll only be able to adapt as fast as your team. Can they keep up with the latest research papers, attend the conferences and keep on the bleeding edge of what’s possible? 

By making the choice to buy  rather than building, you can adapt through vendor selection and piggy back on your vendors’ AI teams’ knowledge of the cutting edge.

6. Reliability: It’s hard running AI systems in production

Running AI systems in production is extremely challenging. They require high speed, real time pipelines of data and large amounts of compute resources. When demand spikes are you able to scale up quickly and then ramp down before the costs pile up? Do you have specialist reliability engineers that know how to run and quality control AI models?  

The AI vendor advantage 

Vendors can aggregate demand from a range of customers and spread the load with spikes happening at different times. Vendors can afford to hire specialist reliability engineers to run AI systems at scale.

Even Microsoft — a company with the resources and talent to build AI — chose to buy part of their AI solution for Bing. 

The reasons for buying instead of building an AI solution check the same boxes as gaining the competitive advantage. You can match the talent and speed of the biggest names in tech while gaining the insights and system reliability that enable your organization to sustain the pace of business in the age of AI. 

Catch and keep up with the state of AI search with these rundowns from the AI search experts at Algolia.

Reach out to our team so we can discuss the benefits of buying over building AI together.

About the authorSean Mullaney

Sean Mullaney

CTO @Algolia

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