Pricing and token economy of AI features – the product aspect

Most of the product managers are excited about integrating GenAI capabilities into their products.
Very few, however, ask the hard question early enough:

What will this actually cost at scale - and will the economics still make sense?

Usually, that question only comes up once the feature starts getting real adoption at scale… and someone in the organization receives the bill.

At our upcoming meetup at Workato, we’ll tackle this extremely important, highly relevant, and still under-discussed topic with three must-see talks designed to save you both money and a lot of operational headaches:

1. The Hidden tax on enterprise AI - How Smart Orchestration Turns Token Waste
   Dan Cohen from Workato will break down the five major cost drivers behind LLM calls at scale, along with practical approaches that can reduce costs by 20%–80%.
2. Designing AI Agents with Token Efficiency in Mind
   Yaron Moshe (Product) and Nadav Porat (Engineering) from Teads (formerly Outbrain) will share real-world lessons from building high-scale AI agents, including clever optimization techniques and non-obvious token reduction strategies that significantly lower costs while maintaining a strong user experience.
3. Consumption based pricing in Gong
   Anna Feldman from Gong will share the full product journey, key considerations, and lessons learned from evolving their pricing model from pure seat-based licensing to a hybrid usage-based approach and explain why they ultimately chose not to charge based on tokens.

If you’re building AI features, shipping GenAI products, or planning to scale AI in production, this meetup will give you practical frameworks, hard-earned lessons, and actionable strategies from teams already operating at scale.

We’re looking forward to meeting you there.

ProductX Team

Info: Every CTO building an AI-powered product in 2026 is having the same uncomfortable conversation with their CFO: why is our AI infrastructure bill growing faster than our revenue?
The answer isn't the LLM. It's the architecture around it.
In this talk, we'll break down the five hidden cost drivers silently compounding in every enterprise agentic deployment — from tool schema overload (50–100 KB of overhead before a single line of business logic runs) to the uncomfortable truth that most enterprise AI tasks don't need an LLM at all. Then we'll look at how a well-designed orchestration layer fixes all five, with token savings of 19–79% documented in production systems today.
This is a talk for Product Managers, VP Products, and CTOs who are building or buying AI features and want to understand the architectural decisions that separate a scalable AI product from a runaway cost center.

Info: Yaron and Nadav will share practical insights from building and
scaling customer-facing AI agents. The session will explore key
architectural and product decisions, lessons learned, and effective
strategies for reducing token consumption while maintaining
high-quality user experiences and operational efficiency.

Info: Most SaaS companies rely on seat-based pricing, but AI is challenging that model by introducing highly variable usage costs across customers. In this session, I’ll share how Gong evolved its pricing approach by combining traditional seat-based pricing with a scalable credit model for AI features. I’ll walk through the real product and business decisions behind the implementation - including how we determined what to charge for, how we priced credits, how we integrated the model into the product experience, and the key challenges and tradeoffs we faced along the way.
This is a practical, behind-the-scenes look at building and launching a hybrid pricing model in a real SaaS environment.

Event Venue & Nearby Stays

WeWork ToHa וויוורק תוה״א, 114 Yigal Alon Street, Tel Aviv-Yafo, Israel

Concerts, fests, parties, meetups - all the happenings, one place.

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