GaaS vs AI as a Service: What Is the Difference?
AI as a Service, often shortened to AIaaS, gives organisations managed access to AI tools, models, and platforms.
Agentic as a Service, or GaaS, adds a managed execution layer that can use those AI capabilities to pursue a defined outcome.
AIaaS helps a customer use artificial intelligence without building every model or infrastructure component internally.
GaaS goes further by combining AI with planning, tools, state, permissions, validation, monitoring, and human review.
A simple way to understand the difference is:
- AIaaS provides intelligence as a capability.
- GaaS provides managed agentic work as a service.
GaaS often depends on AIaaS, but the two are not the same.
AIaaS and GaaS at a glance
| Area | AI as a Service | Agentic as a Service |
|---|---|---|
| Primary offering | AI models, APIs, tools, or platforms | Managed agents that pursue bounded outcomes |
| Customer input | Prompt, data, or API request | Goal, context, limits, and expected result |
| Typical output | Prediction, generation, classification, embedding, or analysis | Completed task, draft, action, exception, or reviewed outcome |
| Planning | Usually managed by the customer application | May be handled by the agentic service |
| Tool use | Customer builds or configures integrations | Provider may manage tool selection and execution |
| State | Often request-based or application-managed | Task state is usually part of the service |
| Workflow responsibility | Mostly belongs to the customer | Shared between provider and customer |
| Evaluation | Model or API quality | End-to-end task and action quality |
| Governance | Model, data, API, and platform controls | Adds agent purpose, tool, destination, action, and approval controls |
| Human role | Uses AI output inside a process | Defines goals and reviews important actions or exceptions |
These categories can overlap.
Some AIaaS platforms now include agent-building features, while some GaaS providers expose model APIs and development tools.
The distinction depends on what the provider actually manages.
What is AI as a Service?
AI as a Service is the delivery of AI capabilities through a managed platform.
Customers can use AI without building all of the required models, software, and infrastructure themselves.
AIaaS may provide:
- language models;
- image generation;
- speech recognition;
- translation;
- document extraction;
- computer vision;
- recommendations;
- forecasting;
- classification;
- embeddings;
- model hosting;
- model training;
- evaluation tools; or
- development platforms.
The provider manages much of the underlying AI environment.
The customer normally decides how the capability fits into an application or workflow.
What is Agentic as a Service?
Agentic as a Service is an emerging model for delivering managed AI agents that can work toward a goal.
A GaaS provider may manage:
- agent instructions;
- model selection;
- planning;
- task state;
- tool connections;
- workflow orchestration;
- validation;
- retries;
- approval stages;
- monitoring;
- exception handling; and
- continuous evaluation.
Instead of only calling an AI capability, the customer delegates a bounded task.
For example:
Review approved project records, identify unresolved delivery risks,
prepare a weekly report, and send uncertain items to a manager.
The service may retrieve records, compare dates, group issues, prepare the report, and request review.
The central difference: capability vs execution
AIaaS primarily provides a capability that another system uses.
For example, an application may send text to a classification API and receive one of several labels.
The application still controls:
- when the API is called;
- what data is sent;
- what happens after the label returns;
- whether another service is called;
- how errors are handled; and
- when the process is complete.
GaaS can manage more of that sequence.
The agentic service may decide which approved capability to use, inspect the result, choose the next action, and stop when the outcome is complete.
AIaaS supplies a building block.
GaaS can operate the process around several building blocks.
AIaaS is often part of the GaaS stack
GaaS does not remove the need for models and AI platforms.
A managed agent may rely on AIaaS for:
- language understanding;
- reasoning;
- document analysis;
- embeddings;
- speech processing;
- image interpretation;
- model hosting; or
- safety classification.
The GaaS layer adds:
- goal management;
- tool use;
- task memory;
- execution control;
- validation;
- approvals;
- monitoring; and
- exception handling.
This means an organisation may buy GaaS from one provider while the service uses AIaaS from another provider underneath.
Customers should know which providers are involved.
Difference in what the customer builds
With AIaaS, the customer often builds the surrounding application.
This may include:
- prompt design;
- user interface;
- data retrieval;
- tool integrations;
- business rules;
- workflow logic;
- retries;
- state management;
- monitoring;
- approval screens; and
- incident handling.
With GaaS, more of this operating layer may be included.
The customer may configure goals, policies, sources, and destinations rather than building every execution component.
The amount of included work varies by provider.
Some products offer a complete managed service.
Others offer an agent platform that still requires substantial customer development.
Difference in the unit of value
AIaaS value is often measured by the quality of an AI operation.
Examples include:
- accurate classification;
- useful generated text;
- correct extraction;
- low model latency;
- strong benchmark performance;
- suitable embeddings; or
- reliable transcription.
GaaS value is measured by the completed task.
Examples include:
- a report prepared correctly;
- a request routed to the right team;
- a record updated after approval;
- an issue escalated with the right evidence;
- a research brief completed with valid sources; or
- a recurring process finished without duplication.
A strong model result can still produce a poor business outcome if the wrong tool, source, or destination is used.
Difference in user interaction
AIaaS is often used through:
- APIs;
- software development kits;
- model playgrounds;
- platform dashboards;
- embedded AI features; or
- developer tools.
GaaS is more likely to expose:
- goal-based requests;
- task forms;
- outcome dashboards;
- approval queues;
- exception inboxes;
- schedule controls;
- activity records; or
- managed service interfaces.
The user focuses less on individual model calls and more on the state of the task.
Difference in orchestration
AIaaS does not necessarily coordinate several steps.
A customer can use an AIaaS platform to build orchestration, but the service category itself does not require it.
GaaS depends on orchestration.
The service may coordinate:
- one or more models;
- specialist agents;
- fixed workflow steps;
- tool calls;
- validation rules;
- approval stages;
- parallel work;
- retries;
- fallbacks; and
- final delivery.
Orchestration is what turns separate capabilities into a managed task.
Difference in state and memory
Many AIaaS calls are request-based.
The service receives input and returns output.
Any longer process state is often stored by the customer application.
GaaS usually needs task state.
It may need to remember:
- the original goal;
- completed steps;
- tool results;
- unresolved questions;
- current plan;
- approvals;
- errors;
- retries; and
- remaining work.
Some services also support longer-term memory.
Customers should confirm what is stored, why it is retained, and how it can be reviewed or deleted.
Difference in integration depth
AIaaS integrations often connect an application to an AI capability.
For example:
Document
→ Extraction API
→ Structured Fields
GaaS integrations connect an agent to the systems needed for a task.
For example:
New Document
→ Agent Reviews Content
→ Retrieves Related Account
→ Validates Required Fields
→ Prepares Update
→ Human Approval
→ Writes to Business System
The GaaS service may cross several application boundaries.
This increases both its usefulness and its governance requirements.
Difference in tool responsibility
In AIaaS, the customer often creates the tools or integrations that surround the model.
The customer decides:
- how the tool is described;
- what parameters it accepts;
- which account it uses;
- what the output means;
- when it can be called; and
- how failures are handled.
In GaaS, the provider may manage part or all of this tool layer.
Customers should still review:
- tool purpose;
- read or write behaviour;
- account scope;
- source access;
- destination access;
- retry behaviour;
- error handling; and
- audit evidence.
Difference in human oversight
AIaaS output is commonly reviewed inside the customer's application or process.
The provider may not know whether the output is used to draft a message, approve a claim, update a record, or make a business decision.
GaaS is closer to the operational action.
Human review therefore needs to be designed into the service.
Approval is especially important before:
- financial actions;
- customer-facing communication;
- record changes;
- deletion;
- access changes;
- legal or policy decisions;
- security actions; or
- other difficult-to-reverse outcomes.
Difference in failure handling
AIaaS failures may include:
- unavailable model;
- invalid request;
- poor output;
- model timeout;
- rate limit;
- unsupported file;
- unsafe content response; or
- service outage.
GaaS inherits those problems and adds:
- wrong plan;
- wrong tool;
- incorrect source;
- missing task state;
- repeated action;
- partial completion;
- wrong destination;
- failed approval;
- unsafe fallback;
- false completion; or
- failure to escalate.
GaaS needs end-to-end recovery rules.
Difference in retries
AIaaS retries are often technical.
An application may resend a failed model request after a temporary error.
GaaS retries can have operational consequences.
Repeating a read may be harmless.
Repeating a create, send, or update action may produce duplicates.
A reliable GaaS service should check:
- whether the first action completed;
- whether a unique request identifier exists;
- whether the destination can be inspected;
- whether the action is reversible;
- which error allows a retry; and
- when a person must take over.
Difference in observability
AIaaS monitoring often focuses on:
- requests;
- tokens;
- latency;
- model version;
- availability;
- error rate;
- safety results; and
- cost.
GaaS observability should additionally show:
- task goal;
- workflow version;
- plan or execution path;
- selected tools;
- tool inputs;
- tool outputs;
- state changes;
- approvals;
- retries;
- external actions;
- final status; and
- exceptions.
Customers need to understand what the agent did, not only which model was called.
Difference in evaluation
AIaaS evaluation commonly measures the AI capability.
This may include:
- accuracy;
- relevance;
- groundedness;
- toxicity;
- extraction quality;
- latency;
- robustness; or
- benchmark performance.
GaaS evaluation must measure the complete task.
Useful measures include:
- completion rate;
- correct tool selection;
- correct source use;
- successful validation;
- correct destination;
- approval rate;
- exception rate;
- duplicate-action rate;
- human correction;
- total runtime;
- cost per accepted result; and
- business impact.
Model quality is necessary but not sufficient.
Difference in pricing
AIaaS pricing often uses:
- model tokens;
- API calls;
- images;
- audio duration;
- compute time;
- storage;
- training jobs; or
- platform capacity.
GaaS pricing may include:
- agent runs;
- completed tasks;
- workflow capacity;
- tool actions;
- managed agents;
- processing time;
- outcome volume;
- support level; or
- a combined subscription and usage model.
Customers should ask how failed, partial, repeated, and human-corrected runs are treated.
Difference in provider responsibility
An AIaaS provider is responsible for operating the AI service it offers.
The customer remains responsible for the application and process around it.
A GaaS provider may accept responsibility for more of the task lifecycle.
That may include:
- execution logic;
- integrations;
- state;
- monitoring;
- tool reliability;
- workflow updates;
- exception routing; and
- operational support.
Contracts and service descriptions should state the exact boundary.
The phrase "managed agent" is not enough.
Difference in customer responsibility
GaaS does not remove customer accountability.
The customer still needs to define:
- approved use cases;
- data boundaries;
- source quality;
- account ownership;
- permission scope;
- human reviewers;
- acceptable outcomes;
- retention;
- incident contacts;
- prohibited actions; and
- when the service must stop.
The provider manages execution.
The customer governs its organisational use.
Difference in governance
AIaaS governance includes:
- approved models;
- data use;
- privacy;
- model risk;
- security;
- provider access;
- retention;
- evaluation; and
- human use of outputs.
GaaS adds:
- agent identity;
- agent purpose;
- tool access;
- action limits;
- source allowlists;
- destination allowlists;
- state and memory;
- approval rules;
- retry rules;
- stopping conditions;
- exception ownership;
- action evidence; and
- incident response.
The governance unit changes from a model call to an operating agent.
Difference in procurement
When buying AIaaS, ask:
- Which models and capabilities are available?
- Where is data processed?
- How is usage measured?
- What evaluation tools are provided?
- How are models updated?
- What are the availability terms?
- Can data and models be exported?
- How are credentials protected?
When buying GaaS, also ask:
- Which outcomes can the agent complete?
- Which tools and accounts does it use?
- How is task state managed?
- Which actions require approval?
- How are retries and duplicates controlled?
- What activity can customers inspect?
- How are exceptions routed?
- How are workflow changes evaluated?
- Who is responsible for a wrong action?
- How can the agent be paused or removed?
Can an AIaaS platform become GaaS?
Yes.
An AIaaS platform can move toward GaaS by adding:
- agent runtimes;
- workflow orchestration;
- tool integrations;
- state management;
- memory;
- guardrails;
- approval controls;
- observability;
- evaluation;
- deployment;
- monitoring; and
- operational support.
However, an agent builder is not automatically a managed agentic service.
Customers may still need to design, deploy, monitor, and maintain the agent themselves.
The service becomes closer to GaaS as the provider takes responsibility for more of the operational outcome.
Can GaaS use several AIaaS providers?
Yes.
One agentic service may use:
- one model for reasoning;
- another for extraction;
- another for speech;
- a separate embedding service;
- a local model for sensitive data; and
- a specialist model for verification.
This can improve task fit.
It also creates more provider, privacy, reliability, and cost dependencies.
Customers should know when routing changes between providers.
When AIaaS is the better choice
AIaaS may be the better choice when:
- the organisation has development expertise;
- the workflow is highly customised;
- the customer wants direct control;
- only one AI capability is needed;
- the surrounding process already exists;
- agentic execution would add unnecessary complexity;
- data must remain inside a customer-managed application;
- the use case is experimental; or
- the organisation wants to build its own agent platform.
AIaaS offers flexible building blocks.
When GaaS is the better choice
GaaS may be a stronger fit when:
- the desired outcome is clear;
- the task repeats;
- execution spans several steps;
- several systems must be coordinated;
- the exact path can vary;
- the organisation does not want to operate the full agent stack;
- suitable permissions can be defined;
- exceptions can be routed to people;
- outcomes can be measured; and
- the provider offers strong activity evidence.
Begin with narrow, reviewable work.
When a hybrid approach makes sense
Many organisations will combine the two.
They may use AIaaS to:
- access models;
- build internal AI features;
- create specialist capabilities; or
- keep sensitive logic under direct control.
They may use GaaS to:
- operate recurring research;
- coordinate support processes;
- manage document workflows;
- prepare business updates; or
- complete bounded cross-system tasks.
A custom application can also call a managed agentic service.
The categories are complementary.
How Feluda relates to both models
Feluda lets users configure AI providers, including cloud and local models.
This reflects the AIaaS or model-capability layer.
Feluda also supports visual workflows, tool use, MCP connections, RunFlows, scheduling, and permissions.
These capabilities can support an agentic operating layer around the models.
A Feluda user may:
- connect an AI provider;
- select approved tools;
- build a structured workflow;
- run it interactively or on a schedule;
- inspect results and errors; and
- keep parts of the environment local.
Feluda therefore helps users combine AI capabilities with controlled execution.
Whether the final offering is called GaaS depends on how it is packaged, managed, and delivered to its users.
How to choose between AIaaS and GaaS
Ask:
- Do you need an AI capability or a completed task?
- Who will build the surrounding workflow?
- Who will manage tools and integrations?
- Who will store task state?
- Who will handle retries and failures?
- Who will review external actions?
- Can the outcome be measured?
- Do you need direct control over models?
- How much provider dependence is acceptable?
- Can the service show the complete execution path?
Choose AIaaS when you need flexible AI building blocks.
Choose GaaS when you need a managed agentic process.
Use both when internal control and managed execution each serve a different part of the task.
The practical conclusion
AI as a Service makes artificial intelligence easier to access.
Agentic as a Service makes goal-directed AI work easier to operate.
AIaaS provides models, APIs, and AI tools.
GaaS combines those capabilities with planning, tools, state, controls, monitoring, approvals, and outcome management.
The difference is not that one uses AI and the other does not.
The difference is how much of the task lifecycle the service manages.
Organisations should choose based on the responsibility they want to retain, the control they need, and the outcomes they can evaluate.