Pegasystems has launched Pega Infinity 26, eliminating unpredictable token-based AI pricing by moving to a flat-rate, outcome-based model for agentic workflows [1]. This shift directly addresses enterprise frustration with escalating LLM costs and positions Pega as an architect of accountable, value-linked AI in regulated sectors. With enterprise buyers increasingly demanding AI ROI proof and pricing predictability, Pega’s approach could set new standards for efficiency and transparency.
What is Covered in This Article:
- Pega Infinity 26’s architectural shift to outcome-based AI pricing
- Enterprise demand for predictable, value-driven AI investments
- Competitive implications for LLM and workflow automation vendors
- Risks and opportunities in the transition away from token-metered AI
The News: Pegasystems Inc. (Pega) has introduced Pega Infinity 26, a major update that eliminates the so-called ‘AI token tax’ by redesigning how agentic AI workflows are built and priced [1]. Instead of incurring charges for every token processed during runtime, Pega moves complex AI reasoning to the design phase, allowing runtime agents to operate efficiently with minimal token usage. Enterprises pay a flat rate per completed business case, giving them predictable costs and direct alignment with business value. Tools such as the AI Token Cost Calculator allow customers to quantify potential savings, which Pega claims can exceed 20x compared to traditional token-metered models. This move is strategically aimed at regulated and large-scale enterprises that have been wary of unpredictable AI expenses, and it is likely to accelerate adoption of agentic AI in mission-critical workflows [1].
Will Pega’s Flat-Rate AI Model Force a Rethink of Token-Based Pricing in Enterprise Automation?
Analyst Take: Pega’s move reframes the economics of enterprise AI at a moment when buyers are demanding accountable ROI and cost transparency. The shift from token-based to outcome-based pricing challenges the status quo of LLM providers and raises the bar for what constitutes enterprise-ready AI workflow automation.
Will Flat-Rate Agentic AI Break Token-Based Dominance?
Pega’s architectural overhaul directly attacks the unpredictability and scaling risk of token-based LLM pricing. By pushing reasoning into the design phase, Pega enables runtime agents to operate with minimal token consumption, letting enterprises budget confidently for AI at scale [1]. This is not a theoretical concern: according to Futurum Group’s AI Platforms Decision Maker Survey (n=820, March 2026), 78% of organizations expect to increase AI budgets in the next year, yet 63% still allocate 10% or less of their tech budgets to AI. Unpredictable token costs have been a gating factor for broader adoption, especially in regulated sectors where budget discipline is paramount. Pega’s flat-rate approach directly aligns with rising enterprise expectations for accountable, value-linked AI investments.
Outcome-Based Pricing Is the New Competitive Battleground
Pega’s move is focused on shifting the competitive narrative. Enterprise buyers are pivoting away from ‘soft’ efficiency metrics and demanding hard, measurable impact from AI investments. Futurum Research finds that embedded, pre-built, verticalized AI delivers the fastest and most predictable ROI because it provides domain context, compliance controls, and workflow fit that horizontal platforms lack. By offering outcome-based pricing, Pega is betting that predictability and domain-aligned value will trump the flexibility of open LLM APIs with opaque cost structures. This is likely to force competitors such as ServiceNow, Adobe, and Salesforce to re-examine their own pricing and architectural models for AI-powered automation.
Pega Joins an Accelerating Outcome-Based Pricing Wave
Pega’s Infinity 26 announcement enters an industry already in the midst of a pricing model transformation. As documented in Futurum Research’s May 2026 analysis, outcome-based pricing has moved from experiment to expectation, with vendors such as Zendesk, Intercom, and Decagon already committing to charging only for successful AI resolutions, and hybrid models emerging from Adobe, Automation Anywhere, Salesforce, ServiceNow, and UiPath [2].
The buyer mandate is clear: Futurum’s 1H 2026 Enterprise Software Decision Makers survey finds that consumption-based pricing (30%) and outcome-based pricing (22%) together account for more than half of enterprise buyers’ preferred AI pricing models, while traditional per-user-per-month pricing has fallen to just 20% [3].
Pega’s approach goes further than many peers by eliminating token variability entirely through design-phase reasoning, rather than simply capping or discounting runtime token costs. This positions Pega not as a follower but as an amplifier of the outcome-based trend, offering the cost predictability buyers demand while also addressing the architectural inefficiency that makes token-metered models expensive in the first place. Vendors that combine pricing flexibility with provable outcomes will capture the next wave of enterprise spend [2]. Pega’s challenge is demonstrating that a rigid flat-rate model can accommodate the diversity of enterprise workflows without sacrificing the flexibility that hybrid models offer.
Execution Risks: Can Pega Deliver on Reliability and Governance?
While Pega’s approach addresses clear enterprise pain points, execution is not guaranteed. According to Futurum Group’s AI Platforms Decision Maker Survey (n=820, March 2026), the top GenAI adoption challenge remains reliability and hallucination management (55%), with data privacy and security close behind (53%). Pega’s design-phase reasoning could improve reliability by reducing runtime variability, but it also concentrates risk in upfront configuration and governance. Enterprises will scrutinize whether Pega can deliver consistent agent performance across complex, regulated workflows without introducing new bottlenecks or governance gaps. If Pega succeeds, it could set a new industry standard, but if reliability or transparency falters, buyers may retreat to more traditional, if costlier, models.
What to Watch:
- Pricing Model Arms Race: Will major workflow automation vendors respond with their own outcome-based or flat-rate AI pricing within 12 months?
- Adoption Inflection: Do regulated industries accelerate deployment of agentic AI now that cost predictability is achievable?
- Reliability Proof Points: Can Pega demonstrate consistent, auditable agent performance in high-stakes, multi-agent workflows?
- Competitive Retaliation: Will LLM providers or platform vendors introduce hybrid models that blend flexibility with cost controls to counter Pega’s advantage?
Read the announcement about the Pega Infinity 26 on the company website.
Sources
1. Are Outcome-Based and Hybrid AI Pricing Models Rewriting the Vendor Playbook?
As Agentic AI Decouples Value from Seats, Vendors that Combine Pricing Flexibility with Provable Outcomes Will Capture the Next Wave of Enterprise Spend
Analyst(s): Keith Kirkpatrick |
Publication Date: May 11, 2026 |
Document #: AIOKK202605
2. Will Outcomes-Based Pricing Become the Preferred Pricing Model for AI Agents?
Will Outcomes-Based Pricing Become the Preferred Pricing Model for AI Agents?
Linking Pricing to Results May Finally Be the Pricing Model That Benefits Vendors and Customers
3. Will Major SaaS Vendors Continue to Evolve Their Pricing Models?
As AI Becomes a Core Functionality Within SaaS Software, Vendors Must Balance Accessibility, Features, and Pricing Against Their Own Cost Models and Revenue Targets
Analyst(s): Keith Kirkpatrick |
Publication Date: November 19, 2025 |
Document #: AINKK202511
Declaration of generative AI and AI-assisted technologies in the writing process: This content has been generated with the support of artificial intelligence technologies. Due to the fast pace of content creation and the continuous evolution of data and information, The Futurum Group and its analysts strive to ensure the accuracy and factual integrity of the information presented. However, the opinions and interpretations expressed in this content reflect those of the individual author/analyst. The Futurum Group makes no guarantees regarding the completeness, accuracy, or reliability of any information contained herein. Readers are encouraged to verify facts independently and consult relevant sources for further clarification.
Disclosure: Futurum is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of Futurum as a whole.
Read the full Futurum Group Disclosure.
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Author Information
Keith Kirkpatrick is VP & Research Director, Enterprise Software & Digital Workflows for The Futurum Group. Keith has over 25 years of experience in research, marketing, and consulting-based fields.
He has authored in-depth reports and market forecast studies covering artificial intelligence, biometrics, data analytics, robotics, high performance computing, and quantum computing, with a specific focus on the use of these technologies within large enterprise organizations and SMBs. He has also established strong working relationships with the international technology vendor community and is a frequent speaker at industry conferences and events.
In his career as a financial and technology journalist he has written for national and trade publications, including BusinessWeek, CNBC.com, Investment Dealers’ Digest, The Red Herring, The Communications of the ACM, and Mobile Computing & Communications, among others.
He is a member of the Association of Independent Information Professionals (AIIP).
Keith holds dual Bachelor of Arts degrees in Magazine Journalism and Sociology from Syracuse University.
