Value-Based Pricing

Is There Ever a Time Not to Do Value-Based Pricing?

May 2, 2025

Illustration showing SaaS pricing balance between customer value and rising cloud infrastructure costs.

Imagine you’re a SaaS founder who’s diligently embraced customer value-based pricing. You’ve set prices according to the amazing ROI your product delivers, the textbook strategy for SaaS growth. Yet one day, your CFO shows you a chart of gross margins plummeting for your highest-usage customers.

It turns out each additional user or AI-driven feature is costing you real dollars in cloud computing, and some “great” customers are actually unprofitable. This scenario raises a provocative question: Is there ever a time not to do value-based pricing?

In this post, we’ll explore why value-based pricing is the default best practice in SaaS, and the edge cases where sticking to it blindly can hurt unit economics. We’ll see that the right answer is still to price based on customer value, but with a keen eye on costs and a smart pricing structure when circumstances demand.

Why Value-Based Pricing is the Gold Standard in SaaS

Value-based pricing is the gold standard in SaaS because it roots every pricing decision in customer-perceived value. Unlike cost-plus or competitor-based models, it charges for outcomes and impact, not inputs or arbitrary markups. But it’s not just a mindset, it’s a disciplined, end-to-end process.

At Monetizely, we guide SaaS companies through this with our 5-step pricing framework, detailed in our Price to Scale Vol.2. It helps teams align pricing with both business goals and customer success, so you capture the full value you deliver. Here's how each step works:

1. Goals & Segments

Start with strategic clarity.

Define your primary pricing goals: Is the company optimizing for adoption? Maximizing revenue? Improving margins? Something else?
Achieve leadership alignment on these goals early, it’s critical for coherent execution.
At the same time, segment your customers meaningfully (e.g., startup, SMB, enterprise) and map their distinct needs and value drivers.

Why it matters: Without clear segments and shared goals, pricing decisions drift. This step grounds your strategy in real market needs and sets up every decision that follows.

2. Packaging

Design tailored offers for each segment.

Packaging isn’t just feature bundling, it’s about delivering the right mix of capabilities to the right customer.
A scrappy startup might need a streamlined basic plan; a large enterprise expects a robust premium suite.
Avoid the “one-size-fits-none” trap, don’t cram every feature into one high-end plan (risking shelfware), and don’t go so granular that sales cycles get bogged down.

Impact: Smart packaging boosts conversions and makes upsells feel like a natural progression. Done right, each segment sees a plan that fits their needs and feels “worth it.”

3. Pricing Metric

Choose a unit of value that scales with customer success.

Your pricing metric (per user, per API call, per GB, etc.) should reflect how customers get value.
A good metric grows with usage, so you earn more only when the customer gets more benefit.
It also signals fairness: customers feel they’re paying for what they actually use and value.

Result: A sustainable revenue model that aligns with outcomes and strengthens customer trust.

4. Rate Setting

Calibrate your price points with precision.

With packages and metrics defined, now set rates that capture value without deterring adoption.
Don’t guess. Use market research, willingness-to-pay surveys, and A/B testing.
The goal is to find the “sweet spot” for each segment, avoiding underpricing (lost revenue) and overpricing (lost deals).

Outcome: Price levels that feel reasonable to each buyer, maximize revenue, and support long-term retention.

5. Operationalization

Turn strategy into scalable execution.

Even the best pricing model fails without implementation.
This step includes:
- Updating billing systems to support your pricing metric and tiers,
- Equipping sales with enablement tools (calculators, discount policies),
- Aligning Customer Success and Support on new offerings,
- Ensuring Revenue Operations can support recognition and compliance.

Impact: Your teams run smooth sales cycles, close structured deals, and execute pricing reliably, turning strategy into actual growth.

When you execute these five steps rigorously, you build a pricing engine that:

Ties directly to customer value and company strategy,
Enhances customer relationships (clients understand and believe in the pricing),
And creates a scalable, fair, and disciplined revenue model.

That’s why we at Monetizely champion value-based pricing. It’s not guesswork. It’s a methodical, outcome-focused approach that aligns your internal teams, your customer relationships, and your financial results around real, measurable value.

Edge Cases: When Value-Based Pricing Meets a Cost Wall

If value-based pricing is so effective, why question it? One word: unit economics.

Most classic SaaS benefits from high gross margins, once you’ve built the product, the cost of serving one more customer is negligible. It’s like the movie business: big upfront investment, then infinite low-cost distribution. In these cases, pricing based on customer value (not cost) makes perfect sense.

But that’s changing.

As Dave put it, “We’re moving from the movie business model to something more like manufacturing, where we need to care about COGS.” That shift is being driven by two factors:

Cloud infrastructure costs (e.g., compute, storage, bandwidth)
AI-heavy products, especially Generative AI

In traditional SaaS, cost-plus pricing (marking up based on costs) is usually discouraged. Since, software has high R&D costs but very low marginal costs per customer, so pricing based on production costs makes little sense. That’s why pricing should focus on customer-perceived value.

But there’s an important exception:

When your SaaS product has real, recurring variable costs per unit of usage, you can’t ignore cost.

This applies to infrastructure-adjacent products and modern AI-heavy SaaS. For example:

If each customer query burns GPU cycles or hits third-party APIs with usage-based billing,
Or if large-scale data processing drives up compute and storage usage per tenant,

Then your cost-per-user is no longer trivial.

What does this mean in practice?

Traditional SaaS sees 80-90% gross margins
AI-heavy SaaS products often have 50-60% gross margins

This narrower margin leaves less room for pricing errors. If you price purely on value and choose the wrong value metric, you could end up with:

Unprofitable customers
Negative unit economics (you lose money on each user)

As Dave Kellogg observes: “AI is forcing software companies to think about cost, something we’ve historically been able to ignore.”

So, should you abandon value-based pricing?

Not exactly. Pure cost-plus pricing ignores the actual value you create, and often leaves money on the table. But in these edge cases, a hybrid approach works best:

Start with customer value, but layer in cost-awareness to protect your margins.

You still aim to capture customer-perceived value, but:

You avoid value metrics that scale faster than your costs (e.g., unbounded API calls)
You build pricing tiers that guard against runaway compute consumption
You validate that each segment is profitable at a unit level

Value-Based Pricing Examples in High-Cost Scenarios (GenAI Case Studies)

1. OpenAI’s ChatGPT

What happened: OpenAI introduced ChatGPT Plus at $20/month, later launching ChatGPT Pro at $200/month for power users. On the surface, this looked like great value for unlimited access to cutting-edge AI.

The issue: Power users were submitting 20,000+ queries/month, each costing ~$0.004–$0.01. For some users, cloud costs exceeded the $200 price, resulting in negative gross margins.
The fix: OpenAI:

Limited GPT-4 usage even for paying customers,
Introduced enterprise plans at higher price points.

The lesson: Even high-value tiers can backfire if a subset of users over-consumes. Flat-rate pricing without usage caps is risky in high-cost environments.

2. GitHub Copilot

What happened: Initially priced at $10/user/month, Copilot was a hit with developers for its coding productivity boost.

The issue: Each prompt triggered Codex model queries (via Azure), leading to $30–$80/month compute costs per active user. Microsoft was losing $20–$70 per user.

The fix:

Launched Copilot for Business at $19/user/month.
Later introduced Microsoft 365 Copilot at $30/user/month.

The lesson: Strategic subsidization may work short-term, but long-term viability requires aligning price with actual COGS, even when customer-perceived value is high.

3. Twilio and Usage-Based SaaS

What happened: Twilio avoided flat-rate pricing from day one, instead charging per SMS, per call, per minute, etc.

Why it worked:

Costs (carrier fees, infrastructure) scale with usage.
Usage-based pricing ensured revenue scaled with cost.

As Tomasz Tunguz observed, “If your costs are material and scale with usage like Twilio, then usage-based pricing aligns your costs with your customers’ spend. This prevents very large customers from being your worst customers... costing you money because the account is gross-margin negative.”. In other words, Twilio’s usage pricing ensures even the biggest user remains profitable (and in fact, big users are great customers, not loss leaders). This is a prime example of sticking with value-based pricing (customers pay in proportion to the value they get from messaging) while smartly baking in cost coverage.

The lesson: Usage-based pricing is value-based and cost-aware, a durable approach when variable costs are non-trivial.

4. Cloud Infrastructure SaaS (various)

What happens:

Snowflake charges by credits tied to compute/storage usage, not flat fees.
AWS uses reserved vs. on-demand pricing to balance predictability and cost-efficiency.

Common trend: Many vendors that started with “unlimited” plans introduced:

Fair use caps
Overage charges
Tiered usage slabs

The lesson: These pricing model shifts are not retreats from value, they’re corrections to ensure value and cost stay in sync.

The Pattern: Hybrid Value + Cost Models

None of these companies abandoned value-based pricing:

OpenAI didn’t switch to pure cost-plus per GPU-hour
GitHub didn’t stop pricing based on developer productivity
Twilio and Snowflake never dropped usage-based (value-aligned) pricing

Instead, they made key adjustments:

Metered usage components (limits, overages)
Higher price tiers for heavier users
Multi-part tariffs that reflect both value and cost

The takeaway: “Price on perceived customer value, but protect your margins through smart, cost-informed pricing architecture.”

Striking the Balance: Value-Based Pricing with Cost-Aware Structure

The upshot is that even when you encounter edge cases, the solution is still rooted in customer value. You usually don’t throw value-based pricing out the window, you adapt it. Here are some best-practice strategies to balance customer value pricing with cost awareness:

1. Know Your Unit Economics Cold

Start with data. Monitor your COGS and gross margins, not just by product, but by feature, even customer.

Flag segments or usage patterns where costs exceed revenue.
For example, if an AI feature runs at 55% gross margin vs. the rest of the platform at 80%, it may need a separate pricing model.
Pricing based on value still requires delivering that value profitably.

According to OpenView reports, nearly half of SaaS companies now use more complex models (e.g. hybrid subscription + usage) to ensure profitable growth. Keep an eye on:

LTV/CAC
Payback period

Underpricing stretches both and hurts efficiency.

2. Choose the Right Value Metric (With Cost in Mind)

Your pricing metric is what customers pay for, so choose one that reflects both perceived value and actual costs.

If your AI product reduces human workload, per-user pricing is a mismatch.
Instead, charge per execution, resolution, token processed, or other compute-tied metrics.
That way, each unit of customer value is also a unit of incurred cost, a fair exchange.

Example: A high-volume data SaaS might price by data volume or processing hours, not users, aligning cost with usage.

The right metric ensures:

Customers pay proportionally to value.
You’re not eating variable costs silently as usage scales.

3. Use Hybrid or Tiered Pricing Models

You don’t have to choose between 100% subscription or 100% usage, blended models often work best.

Common structure: Base fee + usage overages
Base covers fixed costs; variable fees scale with heavy users

Example: Charge $X/month for a usage quota, then $Y/unit beyond that.

This model:

Prevents resource drain from super-users
Ensures revenue scales with usage
Encourages logical plan upgrades

This hybrid approach captures the best of both worlds – a stable relationship via the platform fee and scalability via usage pricing.

Used effectively by companies like Segment and AWS, this setup offers:

Predictability for customers
Protection for margins

Just avoid volume discounts that tip into negative margin territory.

4. Segment Your Customers and Tailor Packages

Cost-heavy “edge cases” are often tied to specific segments, e.g. enterprise clients with massive usage.

Don’t use one-size-fits-all pricing. Instead:

Offer specialized enterprise plans with higher base fees and usage-based add-ons
Bundle features like SLAs or premium support for high-value segments

Example: Maybe 80% of users fit a per-user plan, but the top 20% need consumption-based pricing. That’s okay. Design for both.

When packaging is aligned:

Customers feel the pricing is fair and relevant
You avoid shelfware and discounting traps

5. Watch Outcomes: ACV, Retention, TTV

How do you know if your pricing is working? The ultimate proof is in metrics like:

Annual Contract Value (ACV)
Retention / Net Revenue Retention (NRR)
Time to Value (TTV)
Gross margins
LTV/CAC

If you moved to a more cost-aware pricing model and suddenly retention plummets, maybe you went too far and customers no longer perceive it as a fair deal. If your ACV or NRR (net revenue retention) climbs, it could indicate that customers are expanding happily and you’re capturing more value. Particularly watch gross margins and lifetime value. The goal of value-based pricing tempered by cost is to maximize LTV and have solid gross margins, leading to a healthy LTV/CAC. Also monitor competitive feedback; if competitors aren’t subject to the same cost pressures and undercut you, you may need to communicate your value more clearly. In short, track these closely. Use them as a feedback loop, not a post-mortem.

6. Don’t Over-Rely on Expensive Pricing Research

Big studies like conjoint analysis or Van Westendorp can help, but they’re slow, costly, and often miss the real-world nuance of SaaS.

Instead, Monetizely recommends lean, continuous pricing research:

Run pricing pilots
Use product telemetry to simulate new models
Offer add-ons to small cohorts and track uptake

For example:

Thinking of charging per API call? See if 10% of customers drive 50% of cost first.
Use pricing experiments to uncover willingness to pay, not survey guesses.

Formal research should inform, not dictate. Context, cost structure, and customer value perception matter more than a theoretical price point.

By following these strategies, you rarely have to abandon value-based pricing, you simply refine it. Even in extreme cases like our AI examples, the answer was to adjust the model (metering, higher tiers, etc.) in line with value delivered, rather than switching to, say, pure cost-plus pricing. The art of pricing is finding that sweet spot where customers feel the price is fair for the value they receive, and the business achieves the margins and revenue it needs.

Value Pricing in Marketing vs. Pricing Operations

Before we wrap, it’s important to distinguish between value-based pricing as a marketing narrative and as an operational strategy; they're related, but not always aligned unless managed intentionally.

In marketing and sales, value pricing is a message: “We price based on the value you receive, not by hours or costs.”

This framing positions your software as delivering ROI and justifies the price in the customer’s mind. Marketing teams use:

ROI calculators and case studies (e.g., “Customer X saved $1M using our tool”)
Talk tracks focused on business outcomes
Positioning statements, like: “For $5,000/month, you automate $50,000 of work, a 10x return.”

It’s about showing fairness and upside, making the customer feel the price is worth it.

On the backend, pricing operations is about making that promise real. It’s not just messaging, it’s decisions like:

What pricing metric reflects value? (Users? Workflows? Transactions?)
Should pricing be outcome-based, or a proxy?
How do we handle discounting, tiering, or performance guarantees?
What if the customer doesn’t see the value we assume?

Pricing strategy must also navigate:

Billing systems and customer usage tracking
Margin protection, especially for high-cost features
Cross-functional input from product, finance, and sales
Testing and iteration through financial modeling and customer feedback

The Cost vs. Value Balance

Marketing will never say: “We price based on our costs.”
But operations might still charge extra for costly features to protect margins—so long as the customer sees the value.

From the outside, the customer sees value (“this add-on saves me time”). Inside, the pricing team sees sustainability (“this protects our margin”). The two must be in sync.

Alignment is Everything

Marketing shouldn’t promise “unlimited value” if operations can’t deliver profitably.
Ops shouldn’t design pricing that sales can’t credibly sell as a great deal.

The best SaaS companies align both sides: externally appealing, internally sound.

At Monetizely, we bridge both: When sales says, “This plan is priced so you get 5x ROI,” Finance agrees, because the numbers work out on the backend, too.

Conclusion: Default to Value: With a Cost-Conscious Twist

So, is there ever a time not to do value-based pricing?
In our view, value-based pricing should remain your North Star in nearly every scenario.

What’s often misunderstood is this: Value-based pricing ≠ ignoring costs.

The best SaaS pricing leaders anchor prices to customer-perceived value and remain highly conscious of their cost structure and target margins.

Take the edge case of generative AI services with steep cloud bills. Even there, the answer wasn’t to abandon customer-value pricing. Instead, successful companies:

Evolved their models with usage-based pricing or tiered plans that scaled with both value and cost
Introduced hybrid models that mixed predictability with flexibility
Communicated the value-for-cost story clearly to customers

Think of it as “value-based pricing plus,” a strategy grounded in value, tempered by cost-awareness to ensure profitability.

If you find yourself saying, “value-based pricing isn’t working for us,” don’t throw it out. It’s usually a signal to fine-tune:

Choose a better value metric
Revisit your customer segments and packaging
Add guardrails for extreme usage scenarios

These refinements typically solve the issue. Yes, undisciplined value-based pricing can squeeze margins (as some AI firms discovered the hard way). But reverting to cost-plus or competitor-based pricing is worse, it leaves money on the table and disconnects you from your customers. The goal isn’t choosing value or cost. It’s aligning both, value first, cost-informed second.

At Monetizely, we specialize in that alignment. We’ve helped SaaS companies:

Restructure pricing around customer outcomes and willingness-to-pay
Strengthen unit economics to support scalable profitability
Unlock growth and retention through smarter pricing models

If you're underpricing a high-value product, or bleeding margin on a popular one, we can help. Get a free pricing assessment done from our experts, we’ll make sure you’re capturing value and delivering it profitably.