You may be evaluating Pay-i, already using it, or looking for a broader view of AI ROI. AI usage and token visibility is useful, especially for engineering and product teams. But finance, HR, and executive teams need to know where AI is being used, what it costs, whether people are using it well, and what the business is getting back. For these use cases, Larridin is the best alternative.
This guide compares Pay-i with four alternatives: Larridin, Mavvrik, Worklytics, and Faros AI.
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Larridin — Enterprise AI Measurement Platform |
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Larridin is the strongest Pay-i alternative when the AI ROI question goes beyond engineering. Pay-i gives technical teams deep visibility into GenAI costs and use-case performance. Larridin is built for the enterprise-wide view: who’s using AI, what it costs, how well people and agents are using it, and what value the business gets back. |
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✅ Best For CIOs, CFOs, CHROs, and AI transformation leaders who need enterprise-wide AI ROI across teams, tools, agents, workflows, and outcomes. |
⚠️ Key Limitation Larridin is broader than Pay-i, but it’s not a replacement for Pay-i’s specialized model testing and technical cost-optimization workflows. |
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Mavvrik — AI & Cloud Cost Governance |
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Mavvrik is a strong Pay-i alternative when the main problem is AI and cloud cost governance. It helps finance, FinOps, and platform teams track infrastructure costs across cloud, GPUs, SaaS, Kubernetes, GenAI, and agentic workloads. |
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✅ Best For CFOs, FinOps teams, and platform leaders managing AI, cloud, GPU, and agent infrastructure costs at scale. |
⚠️ Key Limitation Mavvrik is built for AI/cloud cost governance and FinOps, not Pay-i-style prompt-level telemetry, model comparison, or use-case ROI optimization for technical GenAI workflows. |
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Worklytics — People Analytics + AI Adoption Benchmarks |
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Worklytics is a strong Pay-i alternative when the main question is workforce adoption, not token cost. It helps HR and people analytics teams understand how AI adoption varies across teams, roles, and peer benchmarks. |
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✅ Best For HR and people analytics teams that want to benchmark AI adoption across teams, roles, and peers. |
⚠️ Key Limitation Worklytics provides adoption benchmarks, not Pay-i-style token spend tracking, model comparison, or use-case-level ROI. |
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Faros AI — Engineering Productivity & DORA Metrics |
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Faros AI is a strong Pay-i alternative for engineering teams that care more about software delivery intelligence than token-level cost telemetry. It focuses on DORA metrics, SDLC analytics, engineering workflows, and AI adoption inside software teams. |
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✅ Best For Engineering and platform teams that need DORA metrics, SDLC analytics, AI adoption tracking, and compliance-ready reporting. |
⚠️ Key Limitation Faros AI is focused on software delivery intelligence, not Pay-i-style token telemetry, model comparison, or use-case ROI tracking. |
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Feature |
Larridin |
Pay-i |
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Token spend tracking |
✅ Yes — workforce and enterprise layer |
✅ Yes — infra/token layer |
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Enterprise-wide coverage |
✅ Yes — all departments |
⚠️ Technical/product workflows |
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Non-engineering workflow tracking |
✅ Browser and desktop coverage |
❌ Not the core use case |
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AI proficiency measurement |
✅ Yes |
⚠️ Limited |
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Shadow AI discovery |
✅ Yes |
⚠️ Limited |
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Use-case A/B testing (model vs. model) |
⚠️ Limited / not specialized |
✅ Yes — specialized |
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GPU/provisioned capacity management |
❌ No |
✅ Yes — specialized |
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CFO / board-level ROI reporting |
✅ Yes |
⚠️ Eng/product focused |
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Privacy-first, zero data retention |
✅ Yes |
⚠️ Vendor dependent |
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In Our Tests... In our enterprise AI audits, we found that precise token-level data doesn’t always give leaders the full AI ROI picture. Technical teams may understand GenAI costs inside products, applications, or engineering workflows, while missing AI usage in sales, marketing, HR, finance, and operations. That is where Larridin pulls ahead. It surfaces AI activity across departments, tools, users, agents, workflows, spend, proficiency, and outcomes. |
Pay-i is best for engineering and product teams that need token-level GenAI cost visibility, use-case ROI tracking, model comparison, and capacity planning. It is especially useful when AI activity is already instrumented inside products, applications, agents, or technical workflows.
Choose Larridin when the AI ROI question extends beyond engineering and product teams. Larridin helps leaders track AI usage, spend, proficiency, governance, and business impact across departments, tools, agents, workflows, and outcomes.
Yes. Pay-i can give technical teams deeper token-level visibility for specific GenAI use cases. Larridin gives executives a broader enterprise view of how AI is being used, what it costs, and what value it produces across the organization.
Larridin is the best fit when leaders need enterprise-wide AI ROI tracking across technical and non-technical teams. Mavvrik is stronger for AI and cloud cost governance, Worklytics is stronger for adoption benchmarks, and Faros AI is stronger for engineering productivity.
Pay-i is a strong choice when engineering and product teams need deep GenAI cost visibility, token telemetry, and use-case ROI tracking.
Choose Larridin when the bigger problem is enterprise AI accountability: what AI costs, who is using it, how well they are using it, and what the business is getting back across every department.
