Your AI Product's Real Price Tag is Hidden

Here’s a question every builder shipping an AI-powered product should be able to answer: of the price your customer pays, how much is for your product and how much is for the model inference running underneath it?

If you can’t answer that cleanly, you have a pricing architecture problem. And it’s one that will get worse, not better, with time.

Why Bundling Became the Default

The instinct to bundle makes sense. Traditional SaaS trained us this way. You absorb infrastructure costs into the subscription, present one clean price, and let unit economics work themselves out at scale. Nobody charged customers separately for their AWS bill.

Applied to AI products, this translates into a familiar pattern. CodeRabbit, the AI code review tool (raised $60M Series B in 2025, ~$550M valuation, reviews across 2M+ repositories), charges $12 to $24 per seat per month with unlimited pull request reviews. Within that per-seat price, the product is burning tokens: sending diffs to model providers, running multi-pass analysis, generating suggestions, opening Jira tickets. All of it folded into one number.

Emergent, the AI app builder, takes a slightly different approach with credits. Their Standard plan ($20/month) gives you 100 credits, Pro ($200/month) gives 750. Every AI action consumes credits based on task complexity. It makes the cost more visible than a flat subscription, but the credits are still an internal currency. The customer can’t see which model is running underneath, can’t choose a cheaper provider for less critical tasks, and can’t tell what percentage of their $20 is product value versus inference pass-through.

Both approaches share the same underlying assumption: AI inference is just another infrastructure cost to be absorbed. In 2023, that assumption was defensible. In 2026, it’s becoming a liability. Here’s why.

The Cost Base Has Changed Fundamentally

Traditional infrastructure costs are stable and predictable. Your compute, storage, and bandwidth costs don’t swing 10x in a year. You can model them, absorb them, and move on.

AI model costs behave nothing like this.

According to Epoch AI’s research, LLM inference prices have been declining at a median rate of 50x per year, with the fastest drops (post January 2024) accelerating to 200x per year. Stanford’s AI Index quantified this more concretely: achieving GPT-3.5 level performance became 280x cheaper between November 2022 and October 2024. The cost of processing a million tokens dropped from roughly $12 to under $2 in that same window, and continues to fall. DeepSeek’s latest models now process a million tokens of input and output combined for about $0.70.

When your cost base can drop by an order of magnitude in a single year, folding it into a fixed subscription creates a structural misalignment. If you priced your product assuming $5 per million tokens and that drops to $0.50, you’re either pocketing a windfall your customers will eventually notice, or a competitor who architected for transparency will undercut you while maintaining healthy margins on their actual product value.

This is not how traditional infra costs behaved. AWS prices declined gradually and predictably. AI model pricing is in a competitive freefall driven by new entrants, open-source alternatives, and hardware improvements happening simultaneously.

Three Problems That Compound

Beyond the macro price decline, bundled pricing creates three specific problems that get worse at scale.

Usage variance is enormous, and per-seat pricing ignores it. On a tool like CodeRabbit, one developer pushing 20 PRs a day on a monorepo and another pushing 2 PRs a week on a microservice pay the same per-seat price while generating wildly different inference costs. In traditional SaaS, the marginal cost of serving one more user is near zero. In AI-powered products, every interaction has a real, variable cost. Per-seat pricing forces you to either over-charge light users or eat losses on heavy ones.

Model superiority is not permanent, and bundling locks you in. Anthropic might be the best provider for code analysis today. Tomorrow, a new entrant or a fine-tuned open-source model might deliver better results at a fraction of the cost. The number of LLM models available through inference providers grew from about 60 in early 2024 to over 650 by late 2025. When pricing is bundled, switching providers doesn’t translate into savings for customers. More importantly, you have no clean mechanism to let customers choose: the best model for production code reviews, a cheaper model for draft PRs, a local model for sensitive code. The bundled price hides all of this.

The “wrapper tax” becomes visible as costs drop. When inference was expensive, a product charging $24/seat while spending $15/seat on tokens could justify the margin through genuine product value. When that $15 drops to $2, the same $24 looks less like a product price and more like a markup. Customers are increasingly aware of this. The products that survive will be the ones where the product value is clearly distinguishable from the inference cost, not hidden behind the same number.

The Customer is Already Asking for Separation

This isn’t a theoretical argument about pricing elegance. Customers, especially enterprise ones, are actively demanding it.

The clearest signal is the rise of BYOK, Bring Your Own Key. JetBrains shipped BYOK support for their AI Assistant and Junie agent in December 2025, letting developers plug in their own Anthropic, OpenAI, or compatible API keys directly into the IDE, no JetBrains AI subscription required. VS Code followed with its own BYOK framework through the Language Model Chat Provider API. Vercel’s AI Gateway and Cloudflare’s AI Gateway both offer first-class BYOK with per-request key injection. There’s now a dedicated directory at byoklist.com just cataloging AI tools that support the pattern.

The customer logic is straightforward. A team already paying for Anthropic’s API or sitting on negotiated Google Cloud credits doesn’t want to pay retail rates for the same tokens, marked up and laundered through every tool in their stack. They want their centralized AI spend to flow through their own accounts, with their own rate cards and their own usage dashboards.

JetBrains understood something important when they launched BYOK: it’s a trust signal. It tells the customer that the IDE’s value stands on its own, independent of which model runs underneath. That’s a powerful competitive position as the market matures.

Compliance Will Force the Issue

Even if market dynamics don’t force pricing separation, regulation will.

The EU AI Act reached partial enforcement in February 2025, with full enforcement for high-risk AI systems coming in August 2026 and fines of up to €35 million or 7% of global turnover. In the US, California’s AB 2013 (effective January 2026) mandates training data disclosure for generative AI, the Colorado AI Act takes effect in 2026, and over 1,000 AI-related bills were introduced across US states in 2025. Federal agencies introduced 59 AI-related regulations in 2024, more than double the previous year.

The compliance requirements converge on one theme: transparency and traceability of AI systems. Enterprises will need to demonstrate which AI providers processed their data, maintain audit trails, and enforce provider-level policies based on data classification. The EU AI Act explicitly requires full data lineage tracking for high-risk systems.

Here’s the problem: when model usage is bundled into an opaque subscription, the customer has no visibility into any of this. They can’t tell their compliance team which provider touched their code. They can’t enforce a policy that says “no sending financial data to Provider X.” They can’t produce an audit trail of AI consumption for regulators.

Despite 90% of enterprises now using AI in daily operations, only 18% have fully implemented governance frameworks. That gap is closing fast, and it will close faster once fines start landing. Products that separate the model layer, and especially those supporting BYOK, give customers the control they need: choose your provider, track your costs, maintain your compliance. It turns a black box into something governable.

What Builders Should Actually Do

Acknowledging the problem is easy. Here’s what acting on it looks like.

Separate the invoice, not just the architecture. Your customer should be able to see two things: what they’re paying for your product (the workflow, the UX, the integrations, the domain logic) and what they’re paying for AI inference. These can be two line items on the same invoice, or a product fee plus a metered consumption component. The point is visibility, not complexity.

Support BYOK from day one if you can. Let customers plug in their own API keys. Yes, this means your revenue per customer drops because you lose the inference margin. But it also means your product has to stand on genuine value, your sales cycle gets shorter (no procurement fights about hidden AI costs), and enterprise customers who already have negotiated rates with providers will prefer you over competitors who force their own markup.

Build for provider flexibility. If your architecture is hardwired to a single model provider, you can’t offer customers choice and you can’t optimize costs as the market shifts. Abstract your model layer so you can swap providers, blend them, or let customers decide. This is good engineering regardless of pricing strategy, but it becomes essential when your pricing makes the model layer visible.

Let the credit model be transitional, not final. If you’re already using credits (like Emergent does), that’s a reasonable starting point. But treat it as a step toward full transparency, not the end state. The next move is making the credit-to-token relationship visible, then letting customers bring their own keys, then separating product pricing from consumption entirely.

The Real Question

By 2022, 61% of SaaS companies had adopted some form of usage-based pricing. Gartner projected that over 30% of enterprise SaaS would incorporate outcome-based components by 2025. The broader market has already moved past flat bundled subscriptions. AI products, where the variable cost of serving each customer is higher and more volatile than any previous SaaS category, should be leading this transition, not clinging to the old model.

The builders who internalize this distinction early, who architect their products, their pricing, and their value propositions around a clean separation between core product and model consumption, will have a structural advantage as model costs continue their downward trajectory and regulation tightens.

The question that separates durable AI businesses from transient ones is simple: if the model layer became free tomorrow, would customers still pay for your product?

If yes, decouple the burn and prove it. If not, that’s the real problem to solve.

References

Epoch AI — “LLM inference prices have fallen rapidly but unequally across tasks” (March 2025). Median decline of 50x/year, up to 200x post-Jan 2024. epoch.ai/data-insights/llm-inference-price-trends
Stanford HAI — AI Index Report. GPT-3.5 level performance became 280x cheaper between Nov 2022 and Oct 2024. aiindex.stanford.edu/report
Fradkin et al. — “The Emerging Market for Intelligence: Pricing, Supply, and Demand for LLMs” (December 2025). LLM models grew from ~60 in early 2024 to 650+ by late 2025. andreyfradkin.com/assets/LLM_Demand_12_12_2025.pdf
IntuitionLabs — “LLM API Pricing Comparison 2025.” DeepSeek pricing data, cross-provider cost analysis. intuitionlabs.ai/articles/llm-api-pricing-comparison-2025
SumatoSoft — “What Affects AI Development Cost in 2026.” Token costs from ~$12 to under $2 per million (2022-2024). sumatosoft.com/blog/ai-development-costs
Sacra — “CodeRabbit valuation, funding & news.” $60M Series B, ~$550M valuation, seat-based pricing model. sacra.com/c/coderabbit
Emergent — Official pricing page. Credit-based plans: Standard ($20/100 credits), Pro ($200/750 credits). emergent.sh/pricing
JetBrains — “Bring Your Own Key (BYOK) Is Now Live in JetBrains IDEs” (December 2025). blog.jetbrains.com/ai/2025/12/bring-your-own-key-byok-is-now-live-in-jetbrains-ides
VS Code — “Expanding Model Choice in VS Code with Bring Your Own Key” (October 2025). code.visualstudio.com/blogs/2025/10/22/bring-your-own-key
Vercel — AI Gateway BYOK documentation. vercel.com/docs/ai-gateway/authentication-and-byok/byok
Cloudflare — AI Gateway BYOK documentation. developers.cloudflare.com/ai-gateway/configuration/bring-your-own-keys
BYOKList — Directory of AI tools supporting Bring Your Own Key. byoklist.com
Secure Privacy — “AI Risk & Compliance 2026: Enterprise Governance Overview.” EU AI Act enforcement timeline, California AB 2013, fines. secureprivacy.ai/blog/ai-risk-compliance-2026
Secure Privacy — “AI Governance Framework Tools.” 90% enterprise AI adoption vs. 18% governance implementation. secureprivacy.ai/blog/ai-governance-framework-tools
DPO Centre — “Data protection & AI governance 2025-2026.” Colorado AI Act, 1,000+ US state AI bills. dpocentre.com/data-protection-ai-governance-2025-2026
Credo AI — “Latest AI Regulations Update: What Enterprises Need to Know in 2026.” 59 US federal AI regulations in 2024. credo.ai/blog/latest-ai-regulations-update-what-enterprises-need-to-know
Sombra Inc. — “An Ultimate Guide to AI Regulations and Governance in 2026.” EU AI Act data lineage requirements. sombrainc.com/blog/ai-regulations-2026-eu-ai-act
Monetizely — “The 2026 Guide to SaaS, AI, and Agentic Pricing Models.” 61% usage-based adoption, Gartner outcome-based projections. getmonetizely.com/blogs/the-2026-guide-to-saas-ai-and-agentic-pricing-models