Build or buy AI? Ready-made service, in-house development, or AI platform?

There are three common paths to an AI solution: buying a ready-made service, developing a custom solution, or using a platform as the foundation for multiple tailored use cases. No single option is best for every situation.

The right choice is the simplest solution that meets the organization's actual requirements over time. A standard service can be superior when the need is common and the data is non-sensitive. Custom development may be justified when the workflow is unique. A platform becomes particularly relevant when multiple solutions need to share identity, data access, models, and governance.

Three Common Paths

Ready-Made Point Solution

A point solution addresses a specific, limited need, such as meeting notes, writing assistance, or coding support. It can be implemented quickly and requires minimal internal development. The limitation is that the organization must adapt to the product's data flow, integrations, and features.

Custom-Built Solution

A custom solution is developed for a specific process and can provide precise control over interfaces, logic, and integration. It requires expertise and carries responsibility for the entire lifecycle: architecture, testing, security, operations, model changes, and maintenance.

AI Platform

A platform offers shared building blocks for multiple solutions, such as identity, permissions, model connections, RAG, logging, and administration. On top of the platform, the organization can create assistants, agents, and data-driven workflows with unified governance.

The platform does not eliminate the need for business adaptation, but by reusing the same foundation, the focus can remain on configuring and developing what is unique for each area of use.

The right choice is the simplest solution that meets the organization's actual requirements over time.

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When is a Ready-Made Service Enough?

Choose a ready-made service when the task is standardized, the data can be handled according to the service's terms, and the integration need is minor. Examples include grammar checking of public text, brainstorming, or support in a well-known productivity workflow.

Still, verify enterprise terms, central administration, identity, data storage, and the ability to disable inappropriate features. A consumer service that each employee signs up for individually rarely provides the same level of control as a central enterprise agreement.

A point solution is less suitable when the same data needs to be used in many solutions, when permissions must follow document levels, or when the organization wants to be able to switch models and providers without replacing the entire application.

When is Custom Development Justified?

Custom development is suitable when the technical solution is closely linked to a unique workflow or a differentiated product. It may also be justified when performance, operations close to the data source, or integrations require an architecture that standard products do not offer.

Expect full production responsibility. An initial demo represents only a fraction of the cost. Identity, technical monitoring, incident management, test data, secure development, user support, and future model updates need owners and budgets.

Avoid building generic features that the market already solves well unless they create specific value for the organization. In-house development should be concentrated where the organization's process, data, or product is actually unique.

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What Does a Platform Add?

A platform can provide a common control point for:

• login, groups, and roles

• connecting to documents and data sources

• choosing between external and local models

• reusable assistants and agent tools

• logging, tracking, and cost control

• testing, version control, and maintenance

This is particularly valuable when multiple departments otherwise risk purchasing separate tools with different terms and duplicate integrations. The platform can establish a unified security and maintenance model even as the use cases vary.

However, a platform is the wrong choice if the organization only has a small, standardized need that a ready-made service solves just as well at a lower price. The value of a platform arises through reuse, control, and agility.

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Competence and Maintenance

Regardless of which option you choose, training, user support, and a clear responsibility after launch are required. For a ready-made service, maintenance mainly involves vendor management and user administration. A custom-developed solution requires its own product team or a long-term development partner. A platform needs a platform owner and a business owner for each solution.

It is therefore important to determine early on who is responsible for ongoing maintenance. Models change, data sources are updated, and new edge cases arise as the solution is used. Without clear responsibility for testing, costs, quality, and incidents, the solution risks degrading over time regardless of which option you have chosen.

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Integration and Security

A ready-made service is limited to the integrations offered by the vendor. A custom-developed solution provides greater control but also entails a greater responsibility for security and maintenance. A platform can provide a common structure for connections and permissions, but how data is separated and how each integration is handled needs to be reviewed.

NIST's AI Risk Management Framework emphasizes that risks must be identified, assessed, and managed based on the specific organization and use case.[1] A certification is therefore not sufficient to assess security. The review should also cover architecture, contracts, logging, incident management, and the ability to verify that security measures work in practice.

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Total Cost and Lock-in

Compare total cost of ownership (TCO), not just the purchase price and license costs. Include:

• implementation and integration

• cloud, models, and storage

• security reviews and legal compliance

• testing and quality monitoring

• support, training, and change management

• maintenance and future development

• cost of export or switching

The risk of lock-in can exist in everything from data formats and workflows to prompts, integrations, model APIs, and internal expertise. Therefore, ensure that business data, configurations, and relevant logs can be exported.

A model-agnostic platform reduces dependency on individual models, but still implies a certain dependency on the platform's own components.

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Decision Matrix

Choose a ready-made service if:

The need is common, integration requirements are minor, data handling requirements fit the agreement, and quick value is more important than customization.

Choose custom development if:

The workflow or product is unique, integrations are complex, and you can support long-term product responsibility.

Choose a platform if:

Multiple use cases need to share data access, models, and security controls, or if the organization wants to develop and maintain solutions under a unified governance structure.

Combine the options when:

A standard service can handle general tasks, a platform is needed for working with internal data, and custom development is used for the organization's unique requirements.

Questions to Answer Before a Decision

• How standardized is the need?

• Which information classes and personal data are affected?

• Which systems and data sources must be integrated?

• Do multiple solutions need to share the same foundation?

• What expertise and maintenance capacity exist internally?

• How often do the process, model, and requirements change?

• What control do we need over region, logs, and permissions?

• How are quality and business value measured?

• How do we export data and configuration when switching?

• What does the alternative cost over three years, including maintenance?

Digg recommends that public sector entities make a conscious choice based on needs, information, control, and alternatives before procuring generative technology.[2] The same discipline is relevant in the private sector as well.

Sources

1. NIST, AI Risk Management Framework:
   https://www.nist.gov/itl/ai-risk-management-framework

2. Digg, Make conscious decisions on acquiring generative AI:
   https://www.digg.se/ai-for-offentlig-forvaltning/riktlinjer-for-generativ-ai/ta-medvetna-beslut-om-att-skaffa-generativ-ai