
Ayush Parchure
Content Writing Intern, Flexprice

7. Figma

Pricing model: Role-based per-seat with three seat types per plan tier, plus per-seat AI credit allocations
Figma does something most per-seat SaaS does not: it charges different prices for different roles inside the same company. Three seat types (Full, Dev, Collab) across three paid tiers, plus a generous free plan.
Every seat also includes a monthly allotment of AI credits:
Starter(Free): 150 AI credits per day, up to 500 per month, unlimited drafts. Individual designers and students live here. Enough to build portfolios and experiment with Figma AI without opening a wallet.
Professional (monthly or annual billing) prices three seat types:
Full seat: $16/month + 3,000 AI credits/month. For designers doing the actual design work.
Dev seat: $12/month + 500 AI credits/month. For engineers who inspect designs, pull specs, and export assets.
Collab seat: $3/month + 500 AI credits/month. For PMs, leaders, and reviewers who comment and approve.
Organization (annual billing only) bumps the prices and the AI allowance:
Full seat: $55/month + 3,500 AI credits/month.
Dev seat: $25/month + 500 AI credits/month.
Collab seat: $5/month + 500 AI credits/month.
Enterprise (annual billing only) is the top tier:
Full seat: $90/month + 4,250 AI credits/month.
Dev seat: $35/month + 500 AI credits/month.
Collab seat: $5/month + 500 AI credits/month.
Figma is what happens when per-seat pricing grows up. Three seat types at three prices, then multiplied by plan tier, creates a matrix that lets Figma monetize the reality that everyone in a company needs Figma access, but only some should pay full price.
A 20-person product team might have 5 Full seats, 10 Dev seats, and 5 Collab seats, each priced for the work they actually do.
When Figma rolled out Dev Mode seat pricing back in January 2024, community reaction was strong. Users pushed back on the $35/month Dev Mode seat, arguing it felt disproportionate for simply inspecting designs.

Two years on, the matrix is still in place, which tells you how Figma read the trade-off between short-term user anger and long-term expansion revenue.
How does this help you
If multiple roles in your customer's company use your product but extract different value from it, Figma shows you how to monetize that spread. You stop leaving money on the table by pricing the designer the same as the PM, who only comments. You stop losing deals because a flat seat price feels too expensive for the reviewer tier.
Role-based pricing also unlocks a growth lever most founders miss: you can sell to more people at the same company without forcing a plan upgrade.
Take a 50-person company. With role-based seats, they can put all 50 people on Figma,10 Full, 10 Dev, 30 Collab, because each role is priced for what it actually does. Under flat per-seat pricing, they would buy 5 designer seats and leave the rest out.
If your product has this kind of role spread today, you are sitting on expansion revenue you have not charged for.
8. Vapi

Pricing model: Usage-based per-minute with passthrough on third-party providers, enterprise committed-spend options
Vapi, one of our customers at Flexprice, prices AI voice the honest way: you pay for every minute of call time, and the bill is built from four separate cost components. No seats. No tiers for humans. Value scales one-for-one with usage.
Here is what makes up a Vapi minute:
Platform orchestration(~$0.05 per minute): Vapi's own fee for running the call, handling turn-taking, routing, and the state machine that makes a voice agent work.
LLM passthrough(~$0.02–$0.20 per minute): The cost of whichever language model runs the conversation (GPT, Claude, Gemini, and others). Vapi bills this at cost. Small models sit near the bottom of the range; GPT-4 class reasoning models sit at the top.
Voice/TTS passthrough(~$0.04 per minute on average): The cost of the voice engine that speaks back to the caller (ElevenLabs, PlayHT, and others). Also billed at cost. Premium voices cost more than standard.
Transcription(STT)(~$0.01 per minute): The speech-to-text layer that converts the caller's audio into text for the LLM (typically via Deepgram).
Telephony: variable per minute, plus monthly phone number fees. The raw cost of carrying the call over phone lines.
Blended customer cost: typically $0.07–$0.25 per minute, with heavier setups reaching $0.30–$0.33. A customer using a small LLM and a standard voice lands near the low end. One using GPT-4 class and a premium voice lands near the high end.
Enterprise: Custom volume discounts and committed-use contracts. Larger customers negotiate bulk minute pricing, committed monthly spend, and dedicated support.
The page looks simple ("$0.05 per minute"). Underneath, Vapi is tracking four cost dimensions in real time so the customer sees one blended per-minute rate on their invoice.
That transparency is why Vapi wins deals: the buyer knows exactly what each component of a call costs and how to optimize.
How does this help you?
If you are building on top of third-party AI providers (LLMs, voice engines, telephony, image models), Vapi's passthrough model solves the margin and the trust problem at once. You show your customer the real cost of each component. Your fee is your orchestration value, not a hidden markup on someone else's API.
The buyer trusts the breakdown because they can verify it against the provider's own pricing page. Provider prices change a few times a year. When they do, you pass the new price straight through to the customer. You don't eat the cost yourself, and you don't raise your list price to cover it. Your pricing stays honest, and your margin stays protected.
If you are pricing an AI product today and cannot decide whether to mark up passthrough costs or pass them through cleanly, Vapi shows you the answer. The transparent path is also the easier one to defend.
9. Segwise

Pricing model: Credit-based with separate credit buckets per product line
For Segwise, there is no pricing page available publicly. You need to contact the Segwise sales team for current tier pricing and credit allowances.
Segwise is one of our Flexprice customers and one of the cleanest examples of credit-based subscription pricing for an AI product. Customers buy a monthly credit allowance. Every AI call decrements credits from the relevant bucket. When a bucket empties, overage rates kick in.
Segwise runs separate credit buckets per product line, which means the credits you spend on ad analysis are tracked independently from the credits you spend on ad generation.
What you can take from the model itself is the structural idea. It is the same one you now see at OpenAI, Anthropic, Replicate, ElevenLabs, and most new AI products in 2026:
Prepaid credits are the default way to price AI products now.
Credits work because they hide the messy token math from the buyer. Your customer knows they bought $500 worth of credits. They do not need to know whether one call used 1,200 tokens on a small model or 14,000 tokens on a bigger one. That simplicity is the whole point.
How does this help you?
If your AI product uses multiple models or providers on a single customer call, credits fix your pricing explanation problem. You stop saying "we charge $0.00012 per token across three model families. Now the response becomes one credit per ad analysis, and $500 in credits gets you this much work." You get paid upfront.
Credits also protect you when models change. New AI models come out every few months, and their prices move around a lot. When that happens, you quietly adjust how many credits a call costs on your backend.
Credits sit between your customer and the model prices underneath, and they absorb the churn for you. If you are launching an AI product this year and want pricing that will not break every quarter, start with credits.
Here in this case study, you can also see how Flexprice helped Segwise to get started within 2 weeks of time

10. Simplismart

Pricing model: Hybrid platform access plus per-resource usage meters plus entitlements
Simplismart, another Flexprice customer, operates on a hybrid pricing model. Customers pay for platform access, and four distinct usage meters run underneath. One subscription, multiple value drivers, one invoice.
Here is how each meter is priced:
Model APIs (LLM/SLM): $0.06 to $3.90 per 1M tokens. The per-token rate depends on the model. Smaller, cheaper models sit at the bottom (Llama 3.1 8B at $0.13 per 1M tokens). Frontier reasoning models sit at the top (DeepSeek-R1 at $3.90 per 1M tokens). Customers pay only for the tokens they actually use.
Diffusion/image generation: $0.03 to $0.28 per 1024×1024 image. Priced per image generated. Lightweight models like Flux Dev run as low as $0.03 per image. Heavier models like SDXL run up to $0.28. A customer generating 10,000 marketing images on Flux Dev pays $300; the same volume on SDXL is $2,800.
Speech-to-text: $0.0018 to $0.003 per audio minute. Whisper v3 Turbo is the cheapest at $0.0018 per minute. Whisper Large v3 runs $0.003 per minute for
higher-accuracy transcription. At scale, 100,000 audio minutes on Turbo costs $180.
Dedicated GPU deployments (hourly rates):
T4: $1.20/hour. Entry-level inference GPU.
L4: $1.50/hour. Newer mid-tier GPU, better throughput than T4.
A10G: $2.00/hour. Mid-range inference workhorse.
A100: $3.00/hour. High-end training and inference GPU.
H100: $4.00/hour. Frontier-class GPU for large models.
H200: $5.20/hour. Newer generation, for heavier workloads.
B200: Contact sales. Simplismart's top-end GPU, priced on request.
Enterprise: Custom pricing for large-scale GPU reservations at committed rates below on-demand pricing. Enterprises book capacity in advance and pay less per hour for the certainty.
The reason Simplismart splits its pricing is simple. Text work is priced per token. Image work is priced per image. Audio work is priced per minute. A dedicated GPU is priced per hour. Forcing all four into one price (or one flat subscription) either leaves money on the table or overcharges half your customers.
How does this help you?
If you sell per-seat or flat-rate pricing today and you are adding AI, GPU, or heavy usage features, Simplismart shows you where this ends up. You keep a baseline platform fee that every customer pays. You charge for each type of work separately, because each one has different costs on your end. You put it all on one invoice so your customer sees one total, not four.
If you think you are running "just" per-seat today, but you have already added one usage-based feature or an AI add-on, you are already running a hybrid model. The real question is whether you design it on purpose now or fix it later when things start breaking.

See how Flexprice helped Simplismart. Full story on how they moved to hybrid pricing in days, cut pricing changes from days to minutes, and saved over $145K a year. Read the case study.
Brands that started with one model. Here's what they run now.
Some of these brands started differently from where they are now. That's the part every founder skips.
Slack launched with simple per-seat pricing. Today, it offers tiered seats, fair-billing credits, annual discounts, and a separate enterprise Grid contract.
Dropbox started with one consumer plan. Today it runs personal, family, business, and enterprise. Four subscription pricing shapes on one back end.
Notion started per-seat only. Adding AI forced them into per-seat plus usage metering. The AI add-on then got folded into a higher tier in May 2025, which is itself a pricing model change, not a marketing one.
The pattern every founder needs to see is that every successful SaaS becomes hybrid. Not because hybrid pricing is trendy. Because growth forces you to price different value drivers differently. Your seats don't capture your AI cost. Your AI cost doesn't capture the collaborative value of the core product. So you layer.
Here's the warning. If your billing stack can only run the subscription pricing model you picked on day one, you hit a wall the day you try to upgrade.
Pricing isn't a decision. It's a system you'll change four times before you hit $10M ARR.
How to pick a subscription pricing model for your current situation
You don't need a 40-page framework. You need just three questions, and each one maps to a brand on this list.
1. What's your primary value driver today?
If you look like Dropbox, it's storage.
If you look like Slack, it's team seats.
If you look like Vapi, it's minutes.
If you look like Segwise, it's AI calls.
If you sell outcomes, you're already in the hybrid bucket.
Pick the brand you most resemble and start with that subscription pricing model.
2. How will the value compound for your customer in 18 months?
Notion started per-seat. Adding AI pushed them into per-seat plus usage.
ElevenLabs started with flat credit tiers. Now they run tier-specific overage rates.
Your subscription pricing will shift the same way. So pick a model that fits today, but plan your billing stack for the model you'll need next.
3. Can your billing stack run the model you want and the model you'll need next?
Most teams answer "yes" because the pricing page is easy. Then they discover that running Simplismart's hybrid pricing model (platform fee plus usage meter plus entitlements) on a Stripe-only stack is 3 to 6 months of engineering.
If your stack was built for one model, you ship one or the other, not both.
Moving from Slack's per-seat to Simplismart's hybrid costs most teams a quarter of their engineering work on billing instead of product building.
Pick the subscription pricing model that fits you today. Make sure your stack can run the model.
How Flexprice runs any of these subscription pricing models
Here's what we keep hearing from customers. You don't need to rebuild your whole stack when you change your pricing. You just change a few pieces underneath.
That's the whole idea. Every brand in this post looks different on the pricing page, but underneath, they all come down to the same five things Flexprice gives you out of the box:
Plans and prices: Flat-rate, tiered, per-seat, usage-based, hybrid. All live on one object. When sales cuts a custom deal, you add a per-customer override and move on.
Meters: Real-time tracking for API calls, minutes, tokens, active users, visits, or anything else you count.
Features and entitlements: Feature toggles, usage limits, plan-specific gates. This is how you turn AI on for Business and off for Plus without writing new code.
Credit grants: Prepaid balances, auto top-ups, expirations, promo credits. The engine behind "buy $500 in credits" and enterprise prepaid commitments.
Subscription schedules: Phased or ramped pricing over time. The pilot-to-scale shape enterprise contracts need, with no customer migration.
Mix those five, and you can build every shape on this list. Two quick examples to make it real.
Want Slack's fair-billing per-seat? Price on seats. Meter on active users. Credit back any seat that has been inactive for 14 or more days. Three pieces, one subscription.
Want Segwise's credit-based model with per-bucket overage? Tiered prices, credit grants for the allowance, a meter per product line, overage prices starting at zero, and entitlements to keep the buckets separate. Uses all five pieces.
You came here for 10 subscription pricing examples. You leave with a stack that runs all of them without a rewrite.
Pick your favorite brand from this list and tell us which one. We'll show you how Flexprice runs its subscription pricing in 15 minutes.
Wrapping up
Ten brands. Six subscription pricing models. One pattern.
Flat-rate looks simple until you count the multi-seat cancel paths.
Tiered looks fair until the auto-upgrade email lands in a customer's inbox. Per-seat looks clean until you need role-based entitlements.
Usage-based pricing looks modern until the first invoice dispute lands in support. Credit-based looks buyer-friendly until you're tracking balances, rollovers, and expirations across 500 customers. Hybrid is where every mature SaaS eventually lands, because different value drivers deserve different metrics.
The common thread across all 10 SaaS pricing examples: the pricing page is the easy part. The billing stack is where the work lives.
Pick the model that fits your business today. Build (or buy) a billing stack that runs the model you'll need in 18 months. Every brand in this post learned that the hard way. You don't have to.
If you're stuck on which subscription pricing model to copy, pick the brand on this list that looks most like you, model after it for six months, then re-read this post. You'll see your next move.
What are the main subscription pricing models used by SaaS brands in 2026?
What is the difference between usage-based pricing and credit-based pricing?
Why do SaaS companies eventually switch to hybrid pricing models?
How does Slack's per-seat pricing work, and why do enterprises trust it?
How do I choose the right subscription pricing model for my SaaS product?


































