What is the main difference between GaaS and SaaS?

SaaS provides hosted software that users operate, while GaaS provides managed AI agents that can use approved software and tools to pursue a defined outcome.

Will GaaS replace SaaS?

Not necessarily. GaaS agents often rely on SaaS applications as systems of record and action. The two models are likely to coexist and become increasingly connected.

Is SaaS with an AI assistant automatically GaaS?

No. AI features become agentic when the service can pursue goals through multi-step actions, tool use, observation, and controlled adaptation rather than only generate responses.

How is GaaS pricing different from SaaS pricing?

SaaS commonly uses subscriptions or seat-based pricing. GaaS may also charge by task, run, tool action, capacity, processing, or accepted outcome, often combined with subscriptions.

Why does GaaS require more governance than SaaS?

A GaaS agent may choose tools, access several systems, and perform external actions. Governance must therefore cover the agent, accounts, permissions, sources, destinations, approvals, retries, and activity records.

When should a company choose GaaS instead of standard SaaS?

Choose GaaS for a measurable, repeatable, multi-step outcome that benefits from adaptive execution and can be limited by clear tools, permissions, validation, and human review.

GaaS vs SaaS: What Is the Difference?

Software as a Service, or SaaS, gives customers access to hosted software.

Agentic as a Service, or GaaS, gives customers access to managed AI agents that can pursue defined outcomes through software, tools, and connected systems.

The distinction is not simply that GaaS contains AI.

Many SaaS products already include AI features.

The deeper difference is the operating role of the service.

In a traditional SaaS model, the customer or employee usually operates the application. They open pages, enter information, choose actions, and manage the process.

In a GaaS model, the user may describe the result they need. An approved agent can then organise steps, use available tools, observe results, and return the outcome or an exception for review.

SaaS provides software capabilities.

GaaS provides managed execution toward a goal.

SaaS and GaaS at a glance

Area	SaaS	GaaS
Primary offering	Hosted software application	Managed agentic capability
Main user action	Operate software features	Assign a goal and review the outcome
Execution	Mostly directed by the user	Partly directed by an AI agent
Process path	Usually defined by screens and features	May adapt within approved boundaries
Tools	Features inside the application	Tools may span several applications and data sources
Output	Work produced through software use	Completed task, draft, action, or exception
Pricing tendency	Subscription, plan, or seat-based	May include task, execution, capacity, or outcome measures
Main governance concern	User and application access	Agent, tool, account, action, and outcome control
Failure pattern	Feature or application failure	Multi-step reasoning, tool, source, or action failure
Human role	Operates the application	Sets goals, defines limits, reviews important decisions

These are general tendencies rather than rigid rules.

A modern product can combine both models.

What is SaaS?

SaaS is a cloud service model in which the provider runs an application and makes it available to customers over a network.

The provider normally manages:

application hosting;
software updates;
infrastructure;
availability;
routine maintenance; and
part of the security environment.

The customer manages how the application is configured and used.

A SaaS product may support work such as:

customer relationship management;
document collaboration;
project management;
accounting;
communication;
analytics;
file storage; or
human resources.

The application gives people an interface and a set of features.

The people remain responsible for operating those features.

What is GaaS?

GaaS is an emerging service model in which managed AI agents perform bounded work on behalf of users or systems.

A GaaS provider may manage:

AI models;
agent instructions;
task orchestration;
tool connections;
memory or task state;
monitoring;
permissions;
validation;
human approval steps;
scaling; and
updates.

The customer supplies the goal, approved data, business rules, access boundaries, and accountable reviewers.

The agent may then interact with one or more SaaS applications as part of completing the task.

The central difference: operating software vs delegating work

SaaS primarily changes how software is delivered.

Instead of installing and maintaining an application on every computer, the customer accesses a provider-managed application.

GaaS changes who performs part of the work.

The service does not only expose features. It may use those features on the customer's behalf.

Consider a customer-support process.

With SaaS, an employee might:

open the support application;
read a new request;
search the customer record;
find the relevant policy;
draft a response;
update the ticket; and
assign follow-up work.

With GaaS, the user or system might provide the goal:

Review new support requests, prepare source-based draft responses, and
send uncertain or sensitive cases to a person.

The agent could complete several approved steps while the employee focuses on review and exceptions.

GaaS often operates SaaS

GaaS should not be understood as the automatic replacement of SaaS.

Agents still need systems in which information is stored and actions are completed.

A GaaS workflow may use SaaS products to:

retrieve customer information;
read project records;
create tasks;
update statuses;
save files;
send approved messages;
review analytics; or
record completed work.

SaaS remains the system of record or business application.

GaaS becomes an execution layer above or across those systems.

Difference in user experience

SaaS interfaces are usually feature-oriented.

Users navigate:

dashboards;
menus;
forms;
lists;
reports;
settings; and
application-specific workflows.

GaaS interfaces are more likely to be goal-oriented.

A user may interact through:

a conversational request;
a task form;
a scheduled instruction;
an approval queue;
an exception inbox;
an outcome dashboard; or
an API.

The interface may show less of the routine execution and more of:

requested goals;
task status;
evidence;
approvals;
exceptions;
completed actions; and
performance measures.

Difference in workflow design

SaaS workflows are commonly built around application states.

Examples include:

a form is submitted;
a status changes;
a user selects an action;
an approval is completed; or
a record moves to another stage.

GaaS workflows are built around goals and decisions.

An agent may determine that it needs to:

retrieve more context;
choose one of several tools;
ask a follow-up question;
compare sources;
repeat a safe read;
prepare a draft;
stop because evidence is insufficient; or
request human approval.

The workflow still needs boundaries.

The difference is that some steps are selected during execution rather than fully specified in advance.

Difference in autonomy

SaaS does not normally decide what business outcome to pursue.

It performs the feature the user selects.

GaaS introduces bounded autonomy.

An agent may decide:

which approved source to check first;
which tool fits the task;
whether more information is needed;
how to organise intermediate work;
when a validation has failed;
whether to escalate; and
when the completion conditions have been met.

This autonomy should be limited by rules, permissions, and review.

Greater autonomy is not automatically better.

The right level is the minimum needed to complete the task effectively.

Difference in responsibility

In SaaS, operational responsibility is often divided like this:

Provider:

runs the application;
maintains infrastructure;
releases software updates;
protects the hosted service; and
provides product support.

Customer:

configures users;
manages access;
enters and governs data;
designs business processes; and
operates the software.

GaaS adds new responsibilities.

Provider:

operates the agent runtime;
manages model and tool integrations;
monitors executions;
controls service updates;
handles failures and scaling; and
exposes evidence about agent actions.

Customer:

defines approved goals;
controls source and destination access;
assigns accountable owners;
reviews sensitive actions;
evaluates outcomes;
sets retention requirements; and
decides where agents must not act.

Difference in pricing logic

SaaS has traditionally used pricing such as:

per user;
per seat;
per month;
per feature tier;
per storage amount; or
per usage unit.

GaaS may use some of the same structures, but agent execution creates additional pricing possibilities.

These may include:

per completed task;
per agent run;
per workflow;
per tool action;
per processing minute;
per model usage;
per managed agent;
per automation capacity; or
a combination of subscription and usage.

Outcome-based pricing is often discussed in relation to agentic services, but it needs a precise definition.

A provider and customer must agree on:

what counts as an outcome;
when a task is considered complete;
how exceptions are treated;
whether human correction is included;
whether failed runs are charged;
how duplicate actions are handled; and
how quality is measured.

Per-seat pricing may still remain

GaaS does not make user-based pricing impossible.

Organisations may still pay for:

administration access;
reviewer seats;
operator seats;
team workspaces;
governance features;
monitoring dashboards; or
support levels.

A practical GaaS product may combine seat-based access with execution-based pricing.

Difference in measurement

SaaS metrics often focus on software adoption.

Examples include:

active users;
login frequency;
feature use;
retention;
storage;
uptime; and
subscription renewal.

GaaS needs outcome and execution measures.

Examples include:

completed tasks;
validated outcomes;
correct tool selection;
human review rate;
exception rate;
correction rate;
duplicate-action rate;
average completion time;
cost per accepted outcome; and
business impact.

High agent activity does not prove that useful work was completed.

Difference in failure modes

SaaS failures are often visible as:

unavailable application;
broken feature;
slow page;
failed save;
missing record;
permission denial; or
integration error.

GaaS can inherit all of those failures and add agent-specific ones.

These include:

incorrect task interpretation;
unsuitable plan;
wrong tool selection;
unsupported conclusion;
repeated execution;
missing stopping condition;
unsafe fallback;
invalid external action;
false completion claim; or
failure to escalate uncertainty.

GaaS therefore needs visibility into the full task path.

Difference in governance

SaaS governance focuses heavily on:

user identities;
roles;
application permissions;
data access;
configuration;
integrations;
retention; and
account activity.

GaaS governance must also cover:

agent identity;
agent purpose;
model selection;
available tools;
tool permissions;
source quality;
destination restrictions;
task limits;
approval boundaries;
memory;
retries;
activity evidence;
model and prompt updates; and
incident response.

An agent should not inherit a person's full application access without a separate need assessment.

Difference in security boundaries

A SaaS user typically receives access to application features based on their role.

A GaaS agent may use several accounts, tools, and systems during one task.

This creates a larger potential action surface.

Controls should answer:

Which agent is acting?
For which customer or project?
Which account is it using?
Which tool is allowed?
What information may be sent?
Which destination may change?
Does the action require approval?
How is the action recorded?
How can access be revoked?

Least-privilege access is essential.

Difference in data handling

In SaaS, customer data generally enters and remains within the application and its approved integrations.

In GaaS, task data may move through additional components:

agent runtime;
model provider;
orchestration service;
tool server;
connected SaaS application;
memory store;
monitoring system; and
final destination.

Customers need a full data-flow explanation.

They should not assume that a task remains within one application merely because the agent started there.

Difference in product updates

A SaaS update may change:

interface;
feature;
integration;
report;
permission; or
application behaviour.

A GaaS update may also change:

model;
system instructions;
planning behaviour;
tool selection;
memory;
validation;
retry policy;
approval threshold; or
completion criteria.

These changes can affect results without changing the visible interface.

Material agent changes should be versioned and evaluated.

Difference in customer onboarding

SaaS onboarding commonly covers:

account setup;
users;
roles;
data import;
configuration;
integrations; and
feature training.

GaaS onboarding should additionally cover:

approved goals;
unsupported tasks;
source boundaries;
destination boundaries;
tool access;
evaluation cases;
human approvals;
stopping conditions;
exception owners;
monitoring;
incident procedures; and
success measures.

The organisation is not only configuring software.

It is authorising a service to act.

Difference in procurement

A SaaS evaluation may ask:

Does the application provide the required features?
Does it integrate with current systems?
Is it secure and reliable?
Can users learn it?
What is the subscription cost?
Can data be exported?

A GaaS evaluation must also ask:

Which tasks can the agent complete?
How is completion measured?
Which models and tools are used?
Can actions be inspected?
How are uncertain cases escalated?
Which writes require approval?
How are loops and retries limited?
What happens when a source changes?
How are model updates tested?
Can agent definitions and activity records be exported?
Who is accountable for a wrong action?

Can an existing SaaS product become GaaS?

Yes, but adding a chatbot is not enough.

A SaaS provider moves toward GaaS when it offers agents that can:

receive goals;
operate product features;
use connected systems;
maintain task state;
adapt to results;
complete several steps;
produce evidence;
stop safely; and
send exceptions to people.

The provider also needs operational controls for permissions, monitoring, evaluation, and incident response.

SaaS with AI is not always GaaS

A SaaS product may include:

text generation;
summarisation;
search;
recommendations;
prediction;
classification; or
a conversational assistant.

SaaS may be the better choice when:

the process is simple;
users need direct control;
actions are infrequent;
a clear interface already solves the problem;
the work does not require adaptive planning;
mistakes would be difficult to detect;
agent monitoring is not available;
the organisation is not ready to govern agent actions; or
deterministic features are more reliable than an agent.

Not every application needs an autonomous execution layer.

When GaaS may add value

GaaS may add value when:

the goal is repeatable;
work spans several systems;
the exact path varies;
users spend time coordinating routine steps;
source information can be verified;
tools have narrow permissions;
outcomes can be measured;
exceptions can be routed to people;
actions are reversible or reviewed; and
the provider offers strong observability.

Start with a bounded task rather than an entire department.

How SaaS and GaaS can coexist in Feluda

Feluda can connect structured AI work with software and external tools.

Studio can define fixed workflow boundaries.

AI models can handle interpretation and bounded decisions.

MCP and Gene tools can connect approved systems and data sources.

Workbench can support interactive, tool-assisted tasks.

RunFlows can expose execution output, warnings, and errors.

Schedule Manager can trigger recurring work.

In this model, SaaS products may remain the systems where records and actions live, while Feluda coordinates agentic work across them.

This is a practical example of coexistence rather than replacement.

How to decide between SaaS and GaaS

Ask these questions:

Does the user need software features or a completed outcome?
Is the process fixed or variable?
Must work cross several systems?
Can the result be measured?
Can sources and destinations be limited?
Are important actions reviewable?
Can the provider show what the agent did?
Is the organisation ready to manage agent permissions?
Would a deterministic workflow be more dependable?
What happens when the agent cannot complete the task?

The answer may be SaaS, GaaS, or a hybrid.

The practical conclusion

SaaS and GaaS solve different parts of the operating model.

SaaS gives people applications they can use.

GaaS gives people managed agents that can use applications and tools toward defined outcomes.

SaaS remains valuable for systems of record, collaboration, interfaces, and business features.

GaaS adds a layer of delegated execution.

The strongest designs combine them carefully:

SaaS stores and exposes business capabilities;
GaaS coordinates approved work;
deterministic rules protect important boundaries; and
people remain accountable for sensitive decisions.

The question is not whether GaaS will eliminate SaaS.

The better question is which work should remain directly operated by people and which bounded outcomes can be delegated safely to managed agents.