If your company builds AI products, you probably have two AI cost problems.
Your employees are using Copilot, ChatGPT and other AI tools to do their jobs.
At the same time, customers are using AI features inside your own product, creating token and infrastructure costs on your bill.
Those are different measurement problems.
One is about whether employees are getting value from the AI tools you've bought.
The other is about whether the AI features you sell have healthy unit economics.
Most tools specialize in one side.
This guide compares five options for AI-native and SaaS companies that need to understand both.
The first problem is internal.
That's an AI adoption, productivity and workforce-spend question.
The second problem sits inside your product.
Every call your customers make to an LLM creates a variable cost.
As usage grows, token, model and infrastructure costs can grow with it.
That's a FinOps and unit-economics question.
A few signs usually tell you that you have both problems.
The same company may need two kinds of measurement.
That distinction matters when comparing vendors.
|
Tool |
Best for |
Current G2 rating |
Public pricing |
|---|---|---|---|
|
Larridin |
Company-wide AI usage, spend and business-impact measurement |
No public G2 profile verified |
Not published |
|
Pay-i |
GenAI unit economics and use-case ROI |
No rating independently verified |
Not published |
|
Mavvrik |
AI, infrastructure and cloud cost governance |
No rating independently verified |
Flat-rate, quote-based |
|
DX |
Engineering productivity and AI coding-assistant impact |
4.6/5, 342 reviews |
Not published |
|
LinearB |
Engineering analytics with AI usage and cost visibility |
4.6/5, 80 reviews |
Free, Business $49, Enterprise custom |
Pricing and ratings change, so verify them again when you enter a buying process.
Larridin measures how AI is being used across an organization and connects that activity with spend, proficiency and business outcomes.
Its current product set includes several layers.
That makes Larridin broader than an engineering analytics platform or token-monitoring tool.
Its current site lists Klaviyo, SurveyMonkey, Gainsight and Vertiv among enterprises using or trusting the platform.
Larridin was founded by Russ Fradin, Jim Larrison and Ameya Kanitkar. Kanitkar previously held engineering and data leadership roles at LinkedIn, Coinbase and Groupon.
The company raised $17 million in seed funding led by Andreessen Horowitz with participation from Bloomberg Beta, Gradient and other investors.
Larridin doesn't publish list pricing.
There is also an important limitation for this buyer.
Larridin measures organizational AI usage, spend and business impact.
It isn't primarily a product-metering system for calculating the token cost of every customer interaction inside a SaaS product.
If customer-level AI unit economics are your biggest concern, the next two tools are closer to that problem.
Pay-i is built around GenAI economics.
Its ROI Optimization Platform connects AI costs with the value produced by individual use cases.
Its Use Case Leaderboard compares GenAI initiatives, agents and features in one view.
Value Measurement connects those use cases with business KPIs.
True Unit Economics tracks costs across full agent workflows, including multiple models and tool calls.
Its A/B Impact Testing feature compares models, prompts and architectures using both technical and business measures.
That makes Pay-i especially relevant for software companies operating several AI features at the same time.
Instead of only asking how many tokens were consumed, teams can ask what a specific AI use case costs and what value it creates.
Pay-i does not publish pricing on its site.
I also couldn't independently verify a current G2 rating.
Those are questions to take into a sales conversation.
For a deeper comparison, see our Pay-i alternatives guide.
Mavvrik takes a broader cost-governance approach.
It tracks spending across AI models, agents, GPUs, cloud infrastructure and data platforms.
That includes LLM APIs, Kubernetes, Snowflake, Databricks and GPU environments.
Teams can break costs down by dimensions such as team, product, feature and AI agent.
Mavvrik also provides budget thresholds, alerts, cost allocation and chargeback capabilities.
Its public site says most customers move from kickoff to live dashboards in under two weeks.
Pricing is flat-rate and based on customer requirements rather than a percentage of spend.
This makes Mavvrik useful for companies whose AI cost problem extends beyond tokens.
An AI platform company may have model API costs, rented GPUs, Kubernetes infrastructure and cloud spend tied to the same product.
Seeing those together can make unit economics easier to understand.
For a broader comparison, see our Mavvrik alternatives guide.
DX focuses on engineering productivity and developer experience.
G2 currently rates DX 4.6 out of 5 across 342 reviews.
The platform combines development-system data with developer-reported information and frameworks such as Core 4 and DXI.
For teams using AI coding assistants, that means AI adoption can be viewed alongside engineering delivery and experience metrics.
That's more useful than adoption alone.
Knowing that 80% of developers use an AI coding tool doesn't tell you whether cycle time, output or quality changed.
DX is strongest when the question is specifically about engineering.
It doesn't solve the customer-facing token economics of an AI product.
If your main AI ROI question is whether engineers are getting more productive from coding assistants, that's a reasonable fit.
If finance also needs the cost of serving your own AI customers, you'll probably need another layer.
LinearB also focuses on engineering performance.
G2 currently shows a 4.6 out of 5 rating across 80 reviews.
Its core platform tracks engineering delivery metrics using data from code repositories and project-management systems.
LinearB is also one of the vendors in this group with publicly visible pricing.
Current G2 pricing lists:
That makes it easier to estimate the cost of a pilot than with vendors that require a sales conversation before sharing pricing.
LinearB is a good fit for teams that already want engineering delivery analytics and also need visibility into AI adoption and engineering cost.
Its limitation is similar to DX.
It measures what is happening inside the engineering organization.
It isn't designed primarily to calculate what each customer-facing AI feature costs to operate.
Start with the question leadership can't answer today.
If you can't see which teams are using AI or whether that usage produces measurable business value, you need an organization-wide measurement layer.
That's where Larridin fits.
If your product team can't tell whether an AI feature has healthy unit economics, look at tools built around GenAI cost attribution.
Pay-i and Mavvrik both address that problem from different directions.
If your biggest question is whether AI coding assistants improve engineering performance, DX and LinearB are closer to the data you need.
And if your AI footprint includes model APIs, GPUs, cloud infrastructure and data platforms, Mavvrik's broader cost-governance scope may matter more.
An AI-native company often has two different executives asking two different questions.
The CFO wants to know what an AI-powered product costs to operate.
The VP of Engineering wants to know whether AI tools make developers more productive.
Those numbers may eventually appear on the same board slide.
They don't necessarily come from the same system.
Start with the unanswered question creating the most pressure today.
Then add the second layer when you need it.
Trying to force one platform to solve a problem it wasn't designed for usually creates another dashboard nobody trusts.
For more on the broader measurement problem, see our guides to AI ROI measurement for CFOs, AI monitoring for CIOs and developer productivity measurement.
Need the organization-wide view of AI usage, spend and business impact? See Larridin in action.