How to choose a SaaS management platform
Introduction
SaaS management is how teams discover, govern, and optimize the AI and SaaS applications employees use every day. For IT teams, a SaaS Management Platform (SMP) provides a system of record for SaaS and AI inventory, access, usage, spend, and ownership so you can scale SaaS and AI usage without scaling risk or manual work.
Most organizations already use hundreds of SaaS applications and now must work to continuously manage and optimize access for AI as well. However, spreadsheets, ticket queues, and periodic audits were never designed to keep up with constant AI and SaaS app changes. As a result, IT teams struggle with incomplete visibility, inconsistent access control, and ongoing license waste.
Managing SaaS spend used to mean tracking seat licenses against active users. AI has changed the pricing models. Most AI tools don't charge per seat; they charge per token, per API call, or per compute minute. It is consumption-based billing. A team experimenting with an AI coding assistant can generate unpredictable costs that fluctuate month-to-month, and the charges often land in renewal invoices or developer credit card bills rather than in IT's license view. Any platform you evaluate today needs to handle both the seat-license world and the consumption-based AI world. Both are running in parallel inside most organizations now.
This guide outlines the requirements of modern AI and SaaS management and how to evaluate these platforms in today’s reality.
The role of AI and SaaS management in a modern organization
AI and SaaS management sits at the intersection of IT operations, security, and finance. The goal is to:
Maintain an accurate inventory of AI and SaaS applications
Ensure users have the right access at the right time
Reduce unnecessary spending and costs
Support audits with defensible access evidence
Unfortunately, the new reality is that shadow IT and AI isn’t a fringe behavior anymore, and organizations can’t afford to wait or look the other way when employees and business units sign up for AI and SaaS apps on their own. These unmanaged applications quickly become part of how work gets done, often handling sensitive data, storing credentials, or integrating with other tools. IT is faced with a fragmented ecosystem where they’re expected to manage access, control costs, and meet compliance requirements of tools they cannot consistently see or govern.
These unmanaged apps create blind spots in access control, complicate offboarding, inflate spend, and make audits harder to defend. The challenge isn’t preventing this, but getting visibility and applying governance without breaking workflows. Effective AI and SaaS management keeps teams moving while IT maintains control of access and spend.
Shadow AI carries a steep data exposure risk
Shadow AI follows the same adoption pattern as shadow SaaS: an employee finds a useful tool, expenses it or signs up on a free tier, and IT finds out later. The risk profile is different in two ways.
First, the cost exposure is harder to predict. A free-tier API that a developer uses for internal tooling can generate a six-figure invoice by the time it's discovered, because consumption-based pricing scales with usage in ways a monthly seat fee does not.
Second, the data exposure is more direct. AI tools ingest and process content; employees paste internal documents, customer records, and source code into AI interfaces as a matter of routine. The National Cybersecurity Alliance's 2025 survey of 6,500 workers found that 43% share sensitive workplace information with AI tools without their employer's knowledge, including internal documents (50%), client data (44%), and financial data (42%). IBM's 2025 Cost of a Data Breach Report found that organizations with high levels of unmanaged AI tools incur an additional $670,000 in average breach costs.
Why IdPs, SSOs, identity governance, or finance tools can’t meet SaaS management needs
Most organizations already have tools that cover part of the SaaS story. But none of them were built to reconcile it into a single, continuously accurate system of record.
Identity Provider / Single Sign On (IdP / SSO) only reflect apps that are integrated with SSO. While many vendors don’t support SSO or require enterprise-tier licensing, the challenge for larger organizations is scale rather than budget or technical capability. Even with resources and in-house expertise, the number of applications that require integration far exceeds what IT can implement, especially as new SaaS tools are adopted at an accelerating pace. The backlog of apps to integrate grows faster than teams can address it.
IGA tools can enforce processes, but they typically don’t solve AI and SaaS discovery, ongoing inventory, and license management. They often depend on clean application integrations and structured entitlement models, which are hard to maintain across hundreds of fast-changing AI and SaaS apps. They also do not inherently link usage context to decisions like deprovisioning or license right-sizing.
Procurement and finance systems show what you pay for, not what’s actually used or who still needs access. They can be strong sources for renewals and vendor spend, but they don’t link spend to actual adoption, who is accessing them, or whether licenses need to be reclaimed when people change roles or leave.
Ticketing systems document intent, not outcomes. A closed ticket does not prove that access was removed everywhere, that orphaned accounts were eliminated, or that licenses were recovered across apps outside of SSO.
Without a dedicated AI and SaaS management platform that correlates identity, finance, usage, and ownership signals, teams are left to manually reconcile across systems. In practice, that means:
Many SaaS apps and shadow AI remain invisible
Onboarding and offboarding remain manual and incomplete, especially outside of SSO
License and consumption-based pricing optimization happens too late to influence renewals
Access reviews and audits require manual reconciliation and evidence gathering
Modern AI and SaaS environments demand a purpose-built approach that delivers continuous inventory accuracy, lifecycle control beyond SSO, usage-informed spend governance, and audit-ready access logs.
Key requirements for SaaS management platforms
The needs of AI and SaaS management have evolved. As organizations adopt more SaaS, IT teams must continuously manage access, spend, and risk, not just quarterly or annually. Modern AI and SaaS management must:
Account for apps adopted outside of IT control
Support frequent access requests and offboarding apps outside of SSO
Optimize spend by removing redundant applications and unused/orphaned licenses, and improving contract renewals
Provide ongoing visibility, not snapshots in time
Provide access and usage reports for compliance purposes
Automate manual IT workflows
Fit into existing IT workflows without slowing teams down
Overview: AI and SaaS management requirements
Each requirement can be broken down into specific capabilities to evaluate when evaluating solutions.
Continuous discovery and app library
Support multiple discovery sources including IdP, SSO, finance, browser extensions, device agents, and credential vaults with ongoing sync and reconciliation
Provide usage context and access methods per app including SSO, local accounts, and shared credentials
Support clear ownership assignment for every app, including reassignment workflows
Classifies discovered AI apps by model, API keys, and team
Surfaces shadow AI accessed through personal accounts at the domain level
Supports direct API integrations with major AI providers to pull actual usage data
Onboarding / offboarding
Centralized user access visibility across AI and SaaS tools, including apps outside SSO
Automated provisioning and deprovisioning workflows with audit logging
Integration with identity providers without depending exclusively on them
Access requests
Centralized access request intake for SSO and non SSO apps
Policy based approval workflows using role, department, risk, or app owner
Auditable request and approval history
License utilization and optimization
Granular visibility into license usage with active versus inactive user definitions
Automated workflows to reclaim orphaned accounts or downgrade licenses
Actionable recommendations instead of static reporting
Contract management and renewals
Centralized tracking for contracts, renewals, owners, and key terms
Usage informed renewal decisions using adoption and cost data
Shared visibility for IT, finance, and procurement
Consumption-Based Pricing Management
Tracks AI consumption at the token and model level, not just the subscription level
Allows per-team or per-provider budget setting with configurable alert thresholds
Shows real-time burn rate and projected budget exhaustion per provider
Includes AI consumption management in the base platform, not a separate SKU or add-on
Access reviews
Centralized access review data, including non SSO apps where possible
Structured workflows with reviewer assignment and attestations
Exportable audit evidence with timestamps and decisions
Workflow orchestration
No code workflows with templates and app specific actions
Employee communication through Slack, Teams, and email
Fast time to value with minimal configuration
Trigger workflows from HR systems, identity providers, app discovery events, or usage signals
Automate AI and SaaS spend optimization, renewal reminders, and app discovery
The following sections define each requirement and list vendor questions for evaluating SaaS management platforms.
SaaS Management Platforms: Evaluation criteria
The evaluation criteria below are designed to help IT leaders meet the operational, security, and financial requirements of a modern SaaS environment. When used in the buying process, these criteria help ensure you evaluate the most important capabilities required for an effective AI and SaaS management platform.
Continuous AI and SaaS discovery and inventory
Maintaining accurate visibility into AI and SaaS usage is foundational to SaaS management. Without it, IT teams are forced to operate reactively, responding to incidents, renewals, and audits after the fact. The challenge is that AI and SaaS is introduced into the organization through multiple pathways, often outside IT oversight. Modern AI and SaaS management requires continuous, multi-source discovery that closes the gaps left by traditional tools and reflects true SaaS adoption and usage.
Multiple discovery sources (IdP, SSO, finance, device agents, credential vaults, browser extensions): Relying on a single discovery signal, such as SSO, inevitably results in blind spots. Not all apps are connected to identity providers, and many are adopted with corporate cards before IT is aware of them. A SaaS management platform must correlate discovery data from identity systems, financial records, and directories to build a more complete and accurate SaaS inventory.
Always up-to-date inventory of apps used across the organization: Connect your IdP, SSO, finance systems, device agents, and use the SaaS management platform’s browser extension to continuously uncover and manage apps.
App catalog with app category, security certifications, etc: Give employees a central library of all company-approved SaaS tools. Employees can browse available apps, see which are pre-approved for their team, and request access in just a few clicks. Behind the scenes, IT defines access policies and approval chains so access requests are routed automatically.
Control access across the lifecycle
User access constantly changes as employees join, change roles, and leave. Manual lifecycle processes don’t scale and often fail at the most critical moments, especially during offboarding. Effective SaaS management requires centralized, automated lifecycle control.
Onboarding/offboarding: Manual processes introduce delays and inconsistencies. Automation ensures that users receive appropriate access when needed and that access is removed promptly when roles change or employment ends. This reduces both security risk and operational overhead. Critically, these workflows must include offboarding for shadow IT.
Integrations with HRIS, identity providers, and 350+ business applications: Lifecycle automation must align with identity systems. Integration with identity providers allows SaaS management platforms to act on authoritative user data and reduce duplication of effort across tools.
Access Requests: Manage every stage of the access request process, from the initial request through approval and provisioning, all while integrating with your existing ITSM workflows.
Optimize SaaS licenses and AI costs
SaaS spend grows quietly. Unused licenses, duplicate tools, and missed renewal windows add up over time. Without usage context, optimization efforts are reactive and often too late to influence renewals.
Reclaim unused licenses: Knowing how many licenses you own is not the same as knowing how they’re used. SaaS management platforms must surface actual usage data so IT teams can identify underutilized or unused licenses with confidence.
Manage contract renewals: Renewals should never come as a surprise. Clear renewal dates and assigned owners allow IT to review usage, validate business needs, and engage stakeholders before contracts auto-renew.
Consolidate redundant applications: Spot duplicate or overlapping tools and consolidate contracts to reduce costs.
Token-level and model-level visibility. You need to see which teams are consuming the most tokens, which models are generating the costs, and what the daily burn rate looks like across providers.
Budget tracking against pre-committed spend: Many enterprise AI agreements involve pre-committed annual spend with a use-it-or-lose-it structure. Configurable alerts at defined thresholds are the difference between managing the budget and discovering the problem after the quarter closes.
Runway calculation: Know whether the platform shows how long your current AI budget will last at today's burn rate, per provider. This can be an early warning mechanism for finance teams.
Support audits and access reviews
Access reviews and audits are recurring realities for many organizations. When access data is scattered across systems, reviews become time-consuming and error-prone. SaaS management should simplify, not complicate, this process.
Simplify access reviews: Ensure they are repeatable and auditable. Structured workflows help ensure reviews are completed consistently and on time, reducing last-minute audit pressure.
Remove access directly in access reviews: Access reviews should do more than validate access, they should be the point of action. IT teams should remove access or licenses directly within the review workflow so risks are addressed immediately, follow-up work is eliminated, and every decision is captured in a clear, auditable trail.
Exportable audit evidence: Auditors expect evidence. SaaS management platforms should provide clear, exportable records that demonstrate who had access, who reviewed it, and when decisions were made.
Alignment between IT, Security and Finance
SaaS management is a cross-functional problem. It requires shared visibility into apps, access, and spend, plus workflows that make it easy for every team to take action and stay aligned over time.
Broad integration coverage: Prebuilt integrations bring identity, HR, ITSM, and finance signals together so every team is working from the same inventory and the same access context.
Automated workflows for IT Ops and Fin Ops: Guided workflows with clear owners and audit history help teams complete access and spend actions consistently, without custom scripting or constant upkeep.
Fast time to value: Early, credible insights build trust across teams and create momentum for shared processes like access reviews, offboarding, and budgeting decisions.
Questions to ask vendors
Based on the defined evaluation criteria, a variety of questions are critical to answer when evaluating SaaS management platforms. Each vendor included in the evaluation process should answer these questions and provide a detailed response explaining how their platform addresses each use case.
Continuous AI and SaaS discovery and inventory
How do you build a complete, continuously updated inventory of all SaaS apps across our company?
What sources do you use to discover SaaS acquired outside IT, such as corporate cards, browser activity, or vaulted credentials?
Can your platform show how each app is accessed, including SSO, local accounts, and shared credentials?
How quickly does your inventory reflect a new app, user, or meaningful usage change?
How do you assign and track owners, and what happens when ownership changes?
Which AI providers do you integrate with directly via API? How do you handle providers that don't yet have billing APIs?
How does the platform handle AI tools that employees access through personal accounts using a work email address? Does your discovery layer cover that scenario?
Is AI spend and consumption management included in your standard platform, or is it a separate product tier with its own pricing?
Lifecycle control and access requests
How do you onboard and offboard users across SSO and non SSO apps, and how do you verify completion?
What parts of onboarding and offboarding are fully automated versus manual?
How do access requests move from intake through approvals, provisioning, verification, and audit logging?
Optimize licenses and AI spend
How do you measure active versus inactive license usage?
What optimization actions can the platform automate for reclaiming licenses or removing orphaned accounts?
How do you track renewal dates, contract owners, entitlements, and key terms?
How do you identify redundant tools and support consolidation decisions?
How do IT, finance, and procurement collaborate using the same data?
How does the platform track AI consumption: at the subscription level, or at the token and model level? Can you show me what the per-user and per-team view looks like?
Can I set per-team spending budgets and receive alerts before a threshold is crossed, not after an invoice arrives?
How does the platform reconcile API-reported usage against invoiced amounts? Can it flag discrepancies?
AI and SaaS Management: How 1Password helps
1Password SaaS Manager is built to help IT teams discover, govern, and optimize AI and SaaS usage without relying on manual processes. With 1Password SaaS Manager, teams gain:
Discovery & inventory: Maintain a continuously updated inventory and make AI and SaaS use visible, including apps outside SSO
Automated employee lifecycle management: Automate onboarding/offboarding workflows, including shadow IT and shadow AI
Optimization & renewals: Identify unused licenses, token and consumption-based usage, and upcoming renewals using usage data to support right-sizing and renewal decisions
Access reviews & audits: Run structured access reviews using live data
Scale & speed: Integrate with existing IT workflows and scale as your organization grows
By focusing on operational clarity and ease of use, 1Password SaaS Manager helps IT, finance, and security teams regain control of AI and SaaS without slowing the business down.
Conclusion
Managing AI and SaaS has become a defining challenge for modern IT and security teams. As AI and SaaS adoption accelerate, access, spend, and risk change continuously. While traditional tools like identity providers, finance systems, and ticketing workflows still play an important role, they weren’t designed to manage SaaS end-to-end. A purpose-built SaaS Management Platform, helps teams maintain control with continuous visibility, consistent access control, and ongoing license management.
Want to see how 1Password can support your SaaS management needs? Contact your account rep or request a demo today.