AI spending keeps climbing, and most of it goes to getting AI built. Gartner’s January forecast puts worldwide AI spending at $2.52 trillion in 2026, up 44%. AI services alone account for about $588.6 billion of it.
Many companies still struggle to turn that spending into results. AI development services come in several models, and the wrong model slows a project down. A team that needs senior engineers may buy a full project. Meanwhile, a team that needs a full solution may hire engineers and wait. In practice, the three common models are the AI pod, project-based development, and solution design.
Key Takeaways
- AI development services fall into three models: embedded teams, project delivery, and solution design.
- An AI pod is a small senior team that joins the client’s roadmap. It fits companies that already know what to build.
- The best model depends on who defines the “what” and who defines the “how.”
- Nearshore teams in Latin America work in US hours. As a result, many companies hire LATAM developers for AI work.
- Proof matters. Companies should ask for production cases, not demos.
What Are AI Development Services?
AI development services are paid engineering services that design, build, and run AI systems for a client. They cover LLM applications, RAG, AI agents, computer vision, data engineering, integrations, and evaluation. Some vendors sell a finished product. Others place engineers inside the client’s team.
The market also uses many labels for the same work, because vendors name services in their own way. Vendors call it custom AI development services, AI engineering, or AI consulting. Therefore, the label says little. The engagement model says much more.
The Three Models at a Glance
Folder IT separates AI development services by one question. Who defines the “what” (the need), and who defines the “how” (the technical solution)?
| Model | Who defines the what | Who defines the how | Best fit |
|---|---|---|---|
| AI pod | Client | Client | Clear roadmap, missing senior AI talent |
| AI project development | Client | Folder IT | Clear problem, open solution |
| AI solutions builder | Folder IT | Folder IT | Unclear need, early stage |
AI Pod: Senior Engineers Inside the Client Team
An AI pod is a small, cross-functional team of three to five senior engineers, led by a forward deployed engineer (FDE). The pod works inside the client’s operation and follows the client’s roadmap. In this model, the client keeps control of the what and the how.
Forward deployed engineers own an outcome, not a backlog. Monterail describes the FDE as an engineer who works inside the customer’s operation instead of the product organization. The same article argues that a single isolated FDE often fails. Its fix is a pod with a back line, tooling, and engineering supervision attached. Note that Monterail sells this model itself, so the view is not neutral.
An AI pod works best in four cases:
- The roadmap is clear, but senior AI talent is missing.
- The company wants speed without a long hiring cycle.
- Internal teams need AI engineers who can pair with them.
- The scope may change, so a fixed project would not fit.
AI pods are part of AI staff augmentation. See the AI Pods page for the Folder IT model.
AI Project Development: A Clear Problem, an Open Solution
In AI project development, the client defines the problem and the outcome. Folder IT then defines the technical solution and delivers it. The scope covers discovery, architecture, build, evaluation, and production deployment.
This model suits companies that know what they want but not how to build it. For example, a team may need a document-processing system or an internal agent. The partner picks the models, the data design, and the guardrails.
Custom AI development services of this kind need clear acceptance criteria. Because of that, the first step is usually discovery. The custom AI development services market shift explains why buyers now expect this level of ownership.
AI Solutions Builder: When the Need Is Not Yet Clear
Some companies know AI matters, but not where to start. In the AI solutions builder model, Folder IT defines both the what and the how. The work begins with use-case discovery. Then it moves to a prototype and a path to production.
This model fits early-stage programs. It also fits companies with many ideas, since they need a ranked plan before any build starts.
How to Choose Between the Three Models
The choice follows from three questions. First, how clear is the need? Second, how much AI expertise does the internal team already have? Third, how much control does the company want over the technical design?
- A clear roadmap and a team that can steer point to an AI pod.
- A clear problem with an open solution points to AI project development.
- An unclear need points to an AI solutions builder engagement.
- When the answer shifts over time, companies often start with one model and then move to another.
Build-versus-partner data supports this. A widely reported MIT study found that AI tools bought from specialized vendors and built through partnerships succeed about 67% of the time. Internal builds succeeded about a third as often. The same report put the failure rate of enterprise AI pilots at 95%.
Why Many Companies Hire LATAM Developers for AI Work
Nearshore teams in Latin America share time zones with US companies. As a result, standups, reviews, and incident response happen in the same working day.
Companies that want to hire LATAM developers should look at more than rates. They should check the seniority of the engineers, the delivery record, and how the team integrates with US product leaders. The guide to hiring AI developers in Latin America covers these checks in detail.
What to Ask Any AI Development Services Provider
A provider should show production work, not slides. These questions separate real delivery from marketing:
- Which AI systems are running in production today?
- Who built them, and will the same engineers work on this project?
- How does the team evaluate quality before launch?
- Who owns the code, the data, and the models?
- How does the team handle security and access to sensitive data?
Because proof matters, Folder IT publishes case studies and open-source builds as proof. The custom AI development company checklist goes deeper on vendor selection. For a ranked list of providers, see the 8 best custom AI development companies.
Frequently Asked Questions
What are AI development services?
AI development services are paid engineering services that design, build, and run AI systems for a client. They include LLM applications, RAG, AI agents, computer vision, data engineering, integrations, and evaluation. They can be delivered as an embedded team, a project, or a solution design engagement.
What is an AI pod?
An AI pod is a small, cross-functional team of three to five senior engineers, led by a forward deployed engineer. The pod works inside the client’s operation and follows the client’s roadmap. It suits companies that know what to build but lack senior AI talent.
What is the difference between an AI pod and an AI project?
In an AI pod, the client defines both the need and the technical approach, and the pod executes. In an AI project, the client defines the need, and the provider defines the technical solution and delivers it. The pod gives more client control. The project gives more provider ownership.
What do custom AI development services include?
Custom AI development services include discovery, architecture, model selection, data engineering, application development, integration with existing systems, evaluation, and production deployment. The exact scope depends on the use case and the engagement model.
How much do AI development services cost?
Cost depends on scope, team seniority, duration, and the engagement model. A pod is usually priced by team and time. A project is priced by scope and milestones. Providers should give a clear estimate after discovery, not before it.
Why do companies hire LATAM developers for AI projects?
Latin American teams work in US time zones, so collaboration happens in real time. The key checks are seniority, delivery record, and how well the team integrates with the client’s product leaders.
How do you choose an AI development services provider?
Start with who defines the what and the how. Then ask for production cases, named engineers, an evaluation method, and clear ownership of code and data. Providers that show running systems are a safer bet than those that show only demos.
Choosing the Right Model
AI development services work best when the model matches the need. An AI pod gives senior talent inside the client’s roadmap. AI project development gives a partner who owns the solution. An AI solutions builder engagement gives direction when the need is still unclear.
Folder IT delivers all three models with nearshore teams that work in US hours. The company has production case studies and open-source builds that show how its engineers work. Get in touch to scope which model fits a specific AI roadmap.
Written by the Folder IT engineering team, a nearshore software development company working in-hours with US-based engineering and product teams.
