In-House AI Development vs. Outsourcing: Which Is Better?

What Does In-House AI Development Entail?

Building and maintaining an AI solution within the company commonly entails the following:

1. Assembling a skilled team — Most companies would need to hire AI specialists such as machine learning engineers, AI researchers, and data scientists.
2. Setting up an infrastructure — Investing in required hardware and software is necessary to have a secure and strong AI framework.
3. Data management — Involves collection, storing, and processing of large amounts of data sets needed for training. It’s important to ensure data quality and integrity for the best accuracy and results.
4. Model development — Includes initial design, training, and fine-tuning of the model to meet the company's specific tasks and needs.
5. Integration —  Integration into the existing workflow helps companies test and validate the model.
6. Testing and validation
7. Maintenance and updates
8. Security and compliance

What Does Outsourcing AI Development Entail?

Outsourcing AI development is a much easier process. Instead of dealing with technical aspects of in-house AI development, the company can focus more on defining requirements and the company’s business performance.

The process often looks like this:

1. Selecting a vendor — Choosing a reliable AI development partner with expertise and industry-specific knowledge is important. If you look at our solutions, you’ll notice that our industry-specific expertise lies within the banking, insurance, and healthcare industries.
2. Defining requirements — Outsourcing to a vendor like us also entails outlining the project, objectives, and deliverables, and we can even calculate an AI ROI for you.
3. Contracting and agreements — Contracting and agreements establish project timelines, costs, data handling, confidentiality, and more.
4. Data sharing — Before the development process begins, data sharing is a necessary step where we ensure proper data security and compliance measures.
5. Development and testing — Development and testing is where the model development and validation happens to ensure the model fits the company's specific needs and requirements.
6. Project management — Maintaining regular communication to monitor the progress, address issues, and ensure business goals align with the model is a necessary part of project management.
7. Integration and deployment — When a model is complete, the final stages include integration of the AI model within the company’s workflow, assistance with deployment, and ongoing support to ensure optimal performance.
8. Ongoing support

Outsourcing AI development can be cost-effective because it provides access to specialized expertise, which results in a faster time-to-market. However, it requires a careful selection process of a vendor that can align the company's strategic objectives and requirements with the AI model.

Outsourcing vs. In-House Development

Below, we’ll compare outsourcing vs. in-house AI development in more detail. We’ll especially focus on three key aspects:

• Hard costs
• Soft costs
• Quality & performance

Outsourcing can look different depending on the partner you work with so we’ll base most of our points on how we personally work with companies. Note that the process may differ if you partner with someone else.

Hard Costs

Hard costs include:

• Infrastructure
• Software licenses
• Talent acquisition
• Salaries & benefits
• Training & development

Infrastructure

We prefer to deploy AI Agents on client’s infrastructure, but we can also provide a subscription/licensing fee for our AI platform. In-house AI development requires the purchase and maintenance of servers, data storage solutions, and networking equipment.

Software Licenses

Most software licenses come included in our platform fee by default. In-house AI development will require you to purchase AI/ML software and tools, development environments, and analytical tools.

Talent Acquisition

When you hire us as a vendor, you won’t have to do any additional hiring for the development team. However, you might still want to hire someone to handle the company’s AI strategy and help with the adoption. On the other hand, in-house AI development requires data scientists, ML engineers, AI specialists, and DevOps.

Salaries & Benefits

We manage salaries and benefits for all talent required for successful AI development. In-house AI development usually requires competitive salaries for highly specialized roles, benefits, bonuses, incentives, and also other overhead costs such as additional office space, new equipment, and more.

Training & Development

We handle and provide continuous improvement of ongoing training for staff to keep up with AI advancements. In-house AI development requires ongoing training, which can also involve certifications and courses. This can be an additional cost on top of all other expenses of in-house AI development.

“Some leaders think that hiring external vendors is expensive, so they kind of decide ‘hey, let’s build it ourselves’. But this severely underestimates the costs of building AI in-house. You’ll need two or more of your full-time employees working on this for probably much longer than just 2 months, which is how long it takes us to complete a project. Now, add up their monthly salaries. It’s not that cheap, right? But let’s also now consider that they’re putting all their regular work on pause to work on this AI project.” — Andrew McKishnie, Senior NLP Engineer at Multimodal

Soft Costs

Soft costs include:

• Time to market
• Opportunity cost
• Project management
• Risk management
• Maintenance & support

Time To Market

We provide faster deployment with proven proof of concept (PoC). Our process consists of consultation where we can design a strategy for easier AI integration, customization where we train AI Agents on your chosen data sources, and deployment where we only need 2 months to deploy AI agents.

In-house AI development usually has longer development cycles and an iterative testing and validation process.

Opportunity Cost

In-house AI development diverts focus from core business activities, which can cost your business more in the long run.

We maintain focus on core competencies. While you might spend a bit more in upfront costs within the first few months by outsourcing AI development to us, you’ll save more money over the next 3 years, which we can also outline in AI ROI calculation.

Project Management

Our project management is simple. We plan for everything in advance and we focus on what the company’s users need from it. In-house AI development requires managing in-house AI projects and existing teams, which is time-constraining and hard to balance while managing the business.

Risk Management

We provide lower risk by adhering to the highest standards of security compliance. We are SOC2 certified and GDPR compliant. We rely on advanced security infrastructure that includes continuous monitoring, secure data management & governance, secure network architecture, and regular penetration testing to ensure your data is safe with us.

There’s a bigger chance of risk incidents, privacy issues, project delays, and obsolescence when relying on in-house AI development.

Maintenance and Support

In-house AI development requires continuous support, troubleshooting, system upgrades, and scalability. We provide optional ongoing AI support after deployment where we can help you maintain, monitor, and further improve your AI Agents over time.

Quality and Performance

Quality and performance include:

• Expertise
• Scalability
• Innovation
• Security & Compliance

Expertise

When partnering with us, you get access to our extensive experience and research & development. Expertise is likely to be limited when relying on an in-house AI development team.

Scalability

We pride ourselves on providing scalable solutions tailored to our clients. We already have expertise, talent, and efficient project management in place which makes scalability easier.

Scaling with an in-house AI development team can be challenging due to increased requirements of resources and personnel, which can increase costs.

Innovation

You can expect continuous innovation from us as a dedicated AI vendor, while an in-house AI development team often has a slower pace, which also gets limited by internal resources.

Security & Compliance

Our security and compliance are robust, while in-house AI development requires in-house data management, security, and compliance, which makes it more prone to vulnerabilities.

Pros and Cons of Outsourcing AI Development

Outsourcing AI development includes lower initial investment costs, quick deployment (with faster time to market), reduced operational burden on the team, access to cutting-edge technology, continuous improvement, and scalable and secure solutions tailored to your specific needs.

Pros of Outsourcing AI Development

• Cost efficiency — Reduced upfront investment in infrastructure, much more predictable costs, and a better long-term return on investment.
• Access to expertise — Ability to leverage specialized knowledge and skills of experienced AI developers while collaborating with AI professionals.
• Faster time to market — Faster project development with an experienced team already in place and reduced time for recruitment or training of in-house staff.
• Scalability — Easier to scale up or down depending on project requirements with flexibility to allocate resources to fit project needs.
• Risk mitigation — Vendor’s experience in managing and mitigating risk minimizes the project’s risk.
• Innovation and fresh perspective — Vendors can bring new ideas and solutions from external experts, which encourages fresh perspectives often overlooked by an internal team.
• Advanced technology — Vendors utilize advanced tools and platforms, which avoids the company’s need to invest in expensive AI software and hardware.

Cons of Outsourcing AI Development

• Loss of control — Oversight is slightly reduced while the dependency on the outsourcing partner is increased.
• Communication challenges — Potential for miscommunication.
• Quality concerns — Risk of varying quality standards.
• Security risks — Potential exposure of sensitive data and the company’s intellectual property.
• Knowledge transfer — Difficulty in transferring knowledge back to the in-house team.

Pros and Cons of In-House AI Development

In-house AI development provides greater control with full oversight of the development process, the ability to make rapid adjustments, easier collaboration, full ownership of the developed model, and better confidentiality.

Pros of In-House AI Development

• Greater control — Companies have full oversight of the development process with direct management over needed changes.
• Intellectual property ownership — Getting to keep full ownership of the developed AI model with greater protection of proprietary information.
• Internal knowledge and skill development — The in-house team gains AI expertise within the organization and continuously learns.
• Confidentiality and security — The company has better control over data security and privacy, which reduces the risk of breaches and leaks.
• Access to stakeholders — Building in-house gives companies easier access to stakeholders for feedback and input.

Cons of In-House AI Development

• High initial investment - Significant up-front costs for infrastructure, technology, and tools, as well as recruiting and training specialists.
• Longer time to market - Potential delays due to time required for hiring and training with extended development times compared to outsourcing.
• Resource constraints - Time to market is limited by the existing skills and capabilities of the team, with the challenges of scaling resources quickly to meet project demands.
• Operational overhead - Burden on management to oversee and coordinate AI projects.
• Risk of talent overturn - Dependence on key personnel who may leave the company.
• Limited external perspectives - Greater risk of becoming insular and missing out on innovative ideas with a higher potential for bias in problem-solving development.

Should You Outsource AI Development or Build it In-House?

Both in-house AI development and outsourcing have advantages and disadvantages, so it depends on a company to determine which approach best fits its needs.

Companies that would benefit the most from outsourcing AI development include:

• Startups and small businesses
• Non-technical companies
• Companies with short-term AI needs

Startups and small businesses often lack the financial resources and technical expertise to build a comprehensive in-house AI team. Instead, they are better off focusing on core business while outsourcing the development, as this would help them leverage AI expert capabilities with a faster time to market.

Non-technical companies such as healthcare, finance, or insurance don’t have in-house AI expertise. Therefore, outsourcing is a much better cost-effective option than working with an often-limited tech budget.

Companies that require short-term AI will benefit the most from outsourcing project-based work as they can scale up or down depending on their needs.

Companies that would benefit the most from in-house AI development include:

• Tech companies
• Large enterprises
• Companies with long-term AI strategies

Tech companies often have core competencies needed for successful in-house AI development, where they gain better innovation and control.

Large enterprises have resources and budgets with existing technical infrastructure to support an in-house AI development team. They also come with higher data security measures, suitable for keeping their data safe at all times during development.

Companies with long-term AI strategies with plans to integrate AI deeply into their operations and workflow can benefit from training and retaining in-house expertise. Such thinking also helps provide continuous improvement with ongoing development.

In-House Development vs Outsourcing: Things To Consider

The decision whether to outsource or to opt-in for in-house AI development will depend on the company’s specific needs, capabilities, and goals.

Assessing your current situation while paying attention to your future and available resources will make a decision much easier.

Additional things you can consider include:

• Skills and expertise of your team
• Budget and resources
• Project scope and duration
• Control and flexibility

Assessing existing talent and skills gaps will show you how strong your team is to handle in-house AI development, but also how much financial resources you might need for hiring additional staff with AI expertise.

Budget and resources play a huge role in the choice as in-house development can be cost-constraining. You don’t only need an experienced and skilled team, but also the infrastructure to leverage the team’s capabilities.

Shorter-term projects are almost always better suited for outsourcing, while longer-term projects will benefit companies with more resources.

Companies that have higher customization needs might be better off with an in-house development team as this leads to better control and flexibility.

Outsource Your AI Development To Multimodal

Would you like to outsource your AI development, but you need a reliable AI development partner with expertise and industry-specific knowledge? Please schedule a 30-minute call with our experts.

We can discuss your needs, understand your requirements, and show you how our AI solutions work live, so book a call today!