General-Purpose AI Models: Practical Guide for SaaS Teams

General-purpose AI models matter to SaaS teams when the product, internal workflow, or vendor stack depends on a model that can perform a wide range of tasks across different contexts. The practical question is not only whether a model is powerful. It is whether the company provides the model, integrates it into an AI system, relies on it as a downstream provider, or uses it in a way that creates evidence, customer, procurement, security, copyright, or AI Act questions.

Under the EU AI Act, general-purpose AI model obligations sit mainly in Chapter V. Article 53 sets baseline duties for providers of general-purpose AI models, including technical documentation, information for downstream providers, a copyright policy, and a public summary of training content. Article 55 adds duties for models with systemic risk, including model evaluation, systemic-risk assessment and mitigation, serious-incident reporting, and cybersecurity. Article 51 explains when a general-purpose AI model is classified as systemic risk, including the presumption for models trained with more than 10^25 floating point operations.

For most SaaS companies, the first operating task is role clarity. A team that uses a foundation model through an API is usually not the model provider. But it may be a provider of an AI system that integrates the model, a deployer of an AI system, a reseller or distributor in some arrangements, or a customer-facing vendor that must answer buyer questions about model documentation, limitations, security, privacy, copyright, and change management.

This connects directly to AI governance expectations for SaaS vendors, the broader EU AI Act overview for SaaS providers, internal AI-tool review, and buyer control questions for AI-enabled products.

Why this matters in practice

General-purpose AI model work becomes messy when teams treat model choice as a purely engineering decision. The model affects product behavior, customer disclosures, vendor risk, security review, data protection, copyright posture, audit evidence, and sales answers.

The AI Act also creates a handoff problem. Model providers need to make certain information available to providers of AI systems who intend to integrate the model. Downstream SaaS teams need enough information to understand capabilities and limitations, implement safeguards, document their own system, explain customer-facing behavior, and decide whether additional obligations are triggered by the intended use.

This does not mean every SaaS team must build a regulatory program designed for frontier model labs. It means teams should know whether they are providing a model, integrating a model, deploying a system that uses a model, or selling a product whose behavior depends on a model provider. The evidence standard should match that role.

Start with role mapping

The first workflow should be a role map. List every general-purpose model the company uses or offers. Include hosted APIs, open-source models, fine-tuned models, embedded vendor features, internal productivity tools, product copilots, support assistants, analytics features, and customer-configurable AI workflows.

For each entry, capture the model name, provider, version or family, hosting arrangement, use case, product owner, data owner, customer exposure, geography, whether the model is modified or fine-tuned, and whether the company exposes the model directly or only as part of a narrower application.

Then assign a role hypothesis. Is the company a provider of a general-purpose AI model? Is it a downstream provider of an AI system that integrates a model? Is it a deployer using an AI system internally? Is it a customer of a vendor AI feature? Is it enabling customers to configure their own AI use through the platform?

Role mapping should not be a one-time legal memo. It should live in product intake, vendor review, architecture review, security review, and release approval. A model upgrade, new fine-tuning step, new customer-facing output, new sensitive use case, or new market can change the answer.

Know the baseline provider duties

If the company provides a general-purpose AI model, Article 53 is the starting point. Providers must keep technical documentation about the model, including training and testing process and evaluation results, for authorities. They must also make information and documentation available to providers of AI systems who intend to integrate the model.

The downstream-facing information should help integrators understand model capabilities and limitations and comply with their own AI Act obligations. For SaaS teams, this means the documentation question is not abstract. If customers or partners integrate a model or model-based capability, they will ask for capability boundaries, limitations, supported use, evaluation evidence, safety notes, versioning, and restrictions.

Article 53 also requires a policy to comply with EU copyright law and a sufficiently detailed public summary of training content according to an AI Office template. Open-source general-purpose AI models can be exempt from some Article 53 documentation duties when the model is released under a free and open-source licence and required model information is publicly available, but that exception does not apply to models with systemic risk.

Most SaaS teams are not training general-purpose models from scratch. Still, these duties shape vendor diligence. A team using a third-party model should ask whether the provider maintains technical documentation, downstream integration documentation, copyright policy, training-content summary, usage restrictions, safety documentation, and update notices.

Watch for systemic-risk signals

Article 51 classifies a general-purpose AI model as having systemic risk if it has high-impact capabilities or if the Commission designates it based on equivalent capabilities or impact. The AI Act presumes high-impact capabilities when training compute is greater than 10^25 floating point operations.

Article 55 adds extra obligations for providers of general-purpose AI models with systemic risk. These include model evaluation using standardised protocols and tools, adversarial testing, assessment and mitigation of systemic risks at Union level, serious-incident tracking and reporting, and cybersecurity for the model and physical infrastructure.

Downstream SaaS teams usually will not know training compute, evaluation methodology, or systemic-risk classification from public marketing pages. That is why vendor review should ask direct questions. Does the provider classify the model as systemic risk? Has it notified the AI Office where required? What evaluation, incident, security, abuse, and model-update evidence can downstream customers access? What changes would trigger customer notice?

This is not only a compliance concern. If a key model is systemic-risk, changes in availability, policy, safety behavior, rate limits, model retirement, data handling, or regulator-facing obligations can affect product commitments and customer trust.

Build a practical evidence packet

A SaaS evidence packet for general-purpose AI models should be short enough to maintain and strong enough to answer security, compliance, and customer questions.

Keep the model inventory, role map, use case description, provider identity, model version, hosting region, data categories, customer exposure, intended and prohibited uses, vendor documentation, copyright and training-content summary references, security evidence, privacy review, logging design, monitoring plan, human oversight where relevant, model-change process, fallback plan, and customer disclosure position.

If the company fine-tunes or adapts a model, add the training or fine-tuning dataset description, data provenance, privacy basis, evaluation results, limitation notes, red-team or abuse testing, release approval, and rollback plan. If the adapted model is offered broadly, get legal review on whether the company has moved closer to model-provider obligations rather than only system-provider obligations.

For customer-facing AI features, add product documentation, help-center wording, trust-center summary, approved sales answers, known limitation language, escalation triggers, and support runbooks. Buyers increasingly want to know not only which model is used, but how it is governed.

Put vendor review on rails

Vendor review should ask concrete model questions instead of vague AI risk questions. Ask what model is used, whether it is a general-purpose AI model, whether the provider claims systemic-risk status, what Article 53 or Article 55 documentation exists, what customer-facing documentation is available, how model updates are announced, how incidents are handled, how abuse and safety are monitored, and what contractual limits apply.

Security should review access controls, data retention, logging, encryption, tenant isolation, abuse monitoring, incident notification, subprocessors, and model-hosting architecture. Privacy should review input and output data, personal data exposure, training or retention settings, data transfer, data subject rights impact, and whether a DPIA or similar assessment is needed.

Product should own intended use, limitations, customer controls, feature configuration, user notices, fallback behavior, and release decisions. Legal and compliance should own role analysis, AI Act interpretation, copyright posture, customer commitments, and evidence standard.

The workflow should produce a decision, not a pile of notes. Approved, approved with conditions, blocked, or more facts needed. Each condition should have an owner and deadline.

Common mistakes

The first mistake is using "we do not train the model" as the whole answer. That may be true and important, but it does not answer whether the company integrates, deploys, configures, or sells an AI system that depends on the model.

The second mistake is treating open source as automatically low effort. Open-source models can reduce dependence on a hosted provider, but they increase responsibility for hosting, security, evaluation, versioning, data governance, monitoring, and misuse controls. The Article 53 open-source exception is also limited and does not cover systemic-risk models.

The third mistake is skipping copyright evidence. Article 53 explicitly includes a copyright policy and a training-content summary for providers. Downstream teams should still ask vendors what copyright, data provenance, training summary, and indemnity information is available before putting model outputs into customer-facing workflows.

The fourth mistake is leaving model changes to engineering release notes. Model upgrades can change outputs, refusal behavior, latency, accuracy, cost, privacy settings, and customer commitments. Significant changes should trigger a review path.

The fifth mistake is answering customer questionnaires without a controlled source of truth. Sales teams need approved answers on model providers, data use, training, retention, security, subprocessors, limitations, and customer controls. Otherwise, every enterprise review becomes fresh archaeology.

FAQ

What is the practical purpose of general-purpose AI model governance?

The practical purpose is to know which models the company depends on, what role the company plays, what evidence exists, what limitations apply, and what must happen when the model, use case, or legal context changes.

When does this apply to SaaS teams?

It applies when a SaaS team provides, integrates, deploys, configures, fine-tunes, or relies on a general-purpose AI model in a product, internal workflow, customer-facing feature, or vendor relationship.

What should teams document first?

Start with a model inventory and role map. Then collect provider documentation, use case facts, customer exposure, data categories, model version, security and privacy review, copyright posture, limitations, monitoring, and change triggers.

Sources

• Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence.
• European Commission AI Act Service Desk, Article 51: Classification of general-purpose AI models as general-purpose AI models with systemic risk.
• European Commission AI Act Service Desk, Article 53: Obligations for providers of general-purpose AI models.
• European Commission AI Act Service Desk, Article 55: Obligations of providers of general-purpose AI models with systemic risk.
• European Commission AI Act Service Desk, Article 56: Codes of practice.
• European Commission AI Act Service Desk, Article 113: Entry into force and application.