Pricing Your AI Product: From Free to Enterprise
A founder's guide to AI product pricing strategy — usage-based models, cost structure, unit economics, tiering, and how to stop under-pricing your AI.
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TL;DR: AI product pricing is broken by default. Most founders either copy ChatGPT's $20/month flat rate (wrong unit of value), ignore their underlying compute costs until they're underwater at scale, or under-price because they're afraid to charge what the product is worth. This guide covers the five AI pricing models, how to build the unit economics before you set a price, how to tier across SMB to enterprise, and the specific mistakes that will kill your margins — with worked numbers throughout.
In traditional SaaS, the marginal cost of serving one more user is essentially zero. Once the software is built and the infrastructure is running, adding seat number 501 costs almost nothing. This is why per-seat pricing has been the dominant SaaS model for 20 years — the pricing unit (seat) maps to business value (user doing their job), and the cost structure supports virtually any price above a very low floor.
AI products break this model in three fundamental ways.
First, marginal costs are not zero. Every query, every generation, every inference call has a real compute cost. Call OpenAI's API 100,000 times in a month and you have a real bill. Run your own models and you have GPU costs. These costs scale directly with usage, which means your cost structure looks more like a usage-based infrastructure product than a traditional software product. If you price per seat and your users are heavy consumers of AI features, you can be profitable at 10 users and underwater at 100.
Second, value delivery is variable. A spreadsheet is a spreadsheet. It either opens or it doesn't. AI output quality varies — it varies by prompt, by model version, by how well you've tuned the system prompt, by the user's input quality. This means your customers are not buying a deterministic tool. They're buying a probabilistic outcome generator. Pricing needs to account for that variability, both in terms of the value floor (what's the worst they'll get?) and the value ceiling (what's the best case that justifies the price?).
Third, the adoption curve is different. Users need more hand-holding with AI products. They need to understand what to expect, how to prompt, when to trust the output. This means the activation cost — the work required to get a user to their first "wow" moment — is higher than most SaaS products. Pricing models that don't account for a longer activation window (free trials, generous free tiers, onboarding-heavy low pricing) will see higher churn in the first 30-60 days.
Traditional SaaS pricing is designed around zero marginal cost. AI product pricing has to be designed around non-zero marginal cost, variable value delivery, and a longer activation arc. Get any of these wrong and your pricing model will eventually fail — either in margins, in retention, or both.
There is one more difference I want to name explicitly because I see founders ignore it constantly: the comparison problem. Your customers are not comparing you only to competitors. They're comparing you to the general AI tools they already have access to — ChatGPT, Copilot, Claude. These tools are either free or $20/month. Your specialized AI product that does one thing much better than a general AI needs a clear pricing rationale that goes beyond "it's AI." This is fundamentally a positioning problem before it is a pricing problem.
Let's build that rationale.
There are five pricing structures that work for AI products. Most successful AI products use a hybrid of two or three. Here's how each works, when it fits, and its failure modes.
The traditional SaaS model applied to AI. Charge per user per month regardless of how much AI they consume.
When it works: When AI usage is relatively uniform across users and compute costs are low relative to the price point. If you're building a writing assistant at $30/seat/month and your average user costs you $2/month in inference, per-seat works fine.
When it fails: When usage variance is high. If some users generate 1,000 AI outputs per month and others generate 10, per-seat pricing creates a subsidy that makes your heaviest users unprofitable. It also creates a pricing cliff for enterprise accounts with hundreds of seats where the per-seat cost becomes harder to justify.
Real-world examples: Notion AI, Grammarly Business, many AI writing tools.
Charge for what users consume — tokens processed, API calls made, queries answered, documents analyzed, words generated.
When it works: When usage varies significantly across customers, when you can clearly communicate the pricing unit, and when you have tight visibility into your underlying compute costs. Usage-based pricing aligns your revenue directly with cost, protects margins at scale, and grows naturally with your customers' expansion.
When it fails: When the pricing unit is unfamiliar or creates anxiety (customers worry about unexpected bills), when it creates friction in the sales process (no one wants to sign a deal where the final bill is unknown), or when it discourages usage in ways that harm product stickiness.
Real-world examples: OpenAI API, Anthropic API, many B2B AI infrastructure products.
Charge for results, not for usage. A legal AI that reviews contracts charges per contract reviewed. A recruiting AI that sources candidates charges per qualified placement. An AI that reduces customer service tickets charges a percentage of the savings.
When it works: When the outcome is clearly measurable, when it is clearly attributable to your product, and when the value created per outcome is large enough to support the price. Outcome-based pricing is the most defensible position competitively because you are literally selling results.
When it fails: When you can't measure the outcome cleanly, when outcomes have long lag times (you deliver work in January, the customer sees the result in June), or when customers are uncomfortable with variable bills tied to outcomes you influence but don't fully control.
Real-world examples: AI recruiting tools (cost per placement), some legal AI tools, AI-driven ad optimization platforms.
A combination of a base subscription plus usage overage, or a per-seat model with a usage cap, or a flat fee plus outcome-based upside. Most mature AI products settle here because it balances predictability for the customer with cost protection for you.
When it works: Always, once you know your usage distribution. The base rate covers your fixed costs and creates revenue predictability. The usage component protects against heavy consumers.
When it fails: When the hybrid is too complicated. If you need a three-paragraph explanation to tell a customer what they'll pay, you've over-engineered it. The best hybrids have one simple base rate and one simple variable component.
Real-world examples: GitHub Copilot (per seat), Salesforce Einstein (base CRM plus AI add-on), most enterprise AI deployments.
A permanently free tier (or free trial period) designed to drive activation and conversion. Not a pricing model in isolation, but it is a critical component of how many AI products go to market.
When it works: When the product has a clear product-led growth motion, when the free-to-paid conversion path is well-defined, when the marginal cost of free users is low, and when free users generate network effects or word-of-mouth that accelerate paid acquisition.
When it fails: When free users are expensive to serve (high compute costs), when the conversion trigger is unclear, when free users get enough value that they never need to upgrade, or when the free tier cannibalizes your paid conversion.
I'll cover freemium in more depth in its own section because the decisions are nuanced enough to deserve it.
Before setting any price, you need to understand your cost structure. This is the step most AI founders skip. They set a price based on what feels right or what competitors charge, discover the economics don't work when they scale, and then face the painful choice of repricing existing customers or watching margins erode.
AI products have three cost layers that compound:
Layer 1: Inference costs. These are your LLM API costs (if you're calling third-party APIs) or your GPU compute costs (if you're running your own models). This is the most variable cost and scales directly with usage.
Layer 2: Operational overhead. Storage, vector databases, retrieval infrastructure, context management. These scale with data volume and are often underestimated.
Layer 3: Human-in-the-loop costs. Quality review, output correction, customer support for AI errors. These are often invisible in the unit economics but can be significant, especially in early-stage products where output quality is still being tuned.
| Product Type | Target Gross Margin | Warning Zone | Notes |
|---|---|---|---|
| AI assistant / copilot (API-based) | 55-70% | < 50% | High API costs; margin improves with model efficiency |
| Vertical AI SaaS (proprietary model) | 65-80% | < 55% | Higher upfront; better margins at scale |
| AI infrastructure / API product | 40-60% | < 35% | Infrastructure-heavy; pricing must reflect cost |
| AI workflow automation | 60-75% | < 50% | Depends on execution frequency |
| AI analytics / insights product | 70-80% | < 60% | Lower inference; more data processing |
The benchmark for traditional SaaS is 70-80% gross margin. AI products typically run 10-20 points lower in the early stage because of inference costs. This is acceptable as long as you have a clear path to improving margins through model optimization, prompt compression, caching, and output reuse.
I've seen AI companies pitch investors with 75% gross margins that looked great — until you realized they weren't including API costs in their COGS calculation. Always run your margin analysis with inference costs fully loaded into COGS.
As an AI product matures, gross margins should improve through four mechanisms:
A well-engineered AI product at Series B should be running meaningfully better margins than at seed, purely through these optimizations — even before volume-based API discounts kick in.
Let's build the unit economics from scratch using a concrete example. Assume you're building an AI product that analyzes customer support tickets and suggests responses. The core value proposition: reduce resolution time by 40%, reduce headcount by 1-2 support agents per team.
The unit of value is the thing the customer buys. For this product, the obvious unit is "ticket analyzed" or "response generated." Let's use "ticket analyzed."
For each ticket analyzed, you need to:
Assume an average ticket is 200 words = ~270 input tokens. The suggested response is 150 words = ~200 output tokens. Running on GPT-4o at current pricing: approximately $0.0027 per ticket in inference costs. Add storage and operational overhead: $0.0003. Total cost per ticket: approximately $0.003.
At 65% gross margin, you need to charge at least: $0.003 / (1 - 0.65) = $0.0086 per ticket. Round to $0.01 per ticket as a floor.
A support team handles 5,000 tickets per month. At $0.01/ticket, that's $50/month. The product claims to reduce resolution time by 40% — if a support agent handles 200 tickets/day at $50/hour, that's $25 in salary cost per 100 tickets, or $0.25 per ticket. A 40% reduction saves $0.10 per ticket. At $0.01, you're capturing 10% of value created. That's pricing power you've left on the table. The AI leverage ratio metric is what you use to quantify and document this value gap.
If you're capturing 10% of value at $0.01 and your cost allows 65% margin at that price, you have room to reprice. A more defensible number: $0.05-0.08 per ticket, capturing 20-32% of the value you create. At $0.05/ticket and 5,000 tickets/month, that's $250/month — still cheap relative to the value delivered, and now your margin is 94% instead of 65%.
| Price Per Ticket | Monthly Revenue (5k tickets) | Monthly Cost | Gross Margin |
|---|---|---|---|
| $0.01 | $50 | $15 | 70% |
| $0.03 | $150 | $15 | 90% |
| $0.05 | $250 | $15 | 94% |
| $0.10 | $500 | $15 | 97% |
The lesson here is that AI costs are so low relative to the value created for many use cases that cost-plus pricing leaves enormous value uncaptured. Price for value. The cost gives you a floor, not a target.
Once you have a unit price, you design tiers as volume bundles:
Notice the per-unit price decreases at higher volumes. This is intentional — it incentivizes customers to move up tiers, gives large customers a reason to commit to annual contracts, and reflects real volume discounts you receive on API costs.
I've watched founders make the freemium decision wrong in both directions. Some refuse to offer free at all, citing compute costs, and wonder why their growth is slow. Others offer generous free tiers, acquire thousands of free users, and discover they're paying $40,000/month in inference costs with a conversion rate of 1.2%.
The decision framework is not "should we do freemium?" It's "can we build a free tier that is cheap to serve, valuable enough to create habit, and designed to convert?"
Test 1: Cost per free user. Calculate your inference cost for a free user at median usage. If your free tier allows users to generate 50 AI outputs per month and each output costs $0.02 in inference, your cost per free user per month is $1. If you convert 5% of free users to paid at $50/month, you need to carry 100 free users to convert 5 paying customers at $50 = $250 revenue, at a cost of $100 in inference. That math works. If your conversion rate is 1% and your cost per free user is $5, you're paying $500 in inference to generate $50 in revenue. That math does not work.
Test 2: The activation gap. The free tier must deliver enough value to create habit, but not so much value that it removes the conversion trigger. The common mistake is building a free tier that is too good — users get exactly what they need for free and never encounter a reason to pay. Design your free tier around a specific ceiling that matters: output volume, feature access, collaboration, export, API access, or data retention.
Test 3: The aha moment timing. Free users need to reach their aha moment before they hit the free tier limit. If users discover the core value of your product only after 200 interactions, and your free tier limits them to 20, most free users will churn before they've seen enough to convert. Get them to the aha moment first, then let them hit the ceiling.
| Element | Recommendation | Anti-pattern |
|---|---|---|
| Output volume cap | Set at ~50-60% of average paid user consumption | Unlimited (destroys conversion) or too low (prevents aha moment) |
| Feature restrictions | Gate collaboration, integrations, custom fine-tuning | Gating core AI functionality that prevents users from seeing value |
| Time limits | 14-day full trial OR permanent limited-volume free | 7-day trial (too short for habit formation) |
| Data retention | 30 days on free, unlimited on paid | No retention — users build no history to lose |
| API access | Paid only | Free API access subsidizes developers who will never convert |
Freemium works for AI products when: compute costs are low (under $2/user/month for the free tier), the product has a clear self-serve activation path, and the conversion trigger is a natural feature ceiling rather than an artificial lock. It fails when compute costs are high, when the sales motion is enterprise-focused (free individuals don't turn into enterprise deals), or when the product requires significant setup that makes a free trial feel like a consulting engagement.
The biggest pricing mistake I see in AI companies is trying to serve all segments with the same pricing structure. Your SMB customers need simplicity, monthly billing, and self-serve onboarding. Your enterprise customers need contract flexibility, security review, and custom terms. These are not the same product, even if the underlying AI is identical. Understanding SaaS metrics benchmarks by stage helps set realistic expectations for ACV and NRR at each segment.
SMB buyers make decisions quickly, often individually, and respond to simple pricing. They want to know: "What do I get for $X/month?" Complexity kills conversion here.
Key SMB pricing principles:
Typical ACV range for AI products serving SMB: $600 - $6,000.
Mid-market is where AI products often struggle to price correctly. These buyers have more budget and more requirements — security reviews, SSO, audit logs, multi-team collaboration — but they still want a predictable price, not an open-ended negotiation.
Key mid-market pricing principles:
Typical ACV range for AI products serving mid-market: $18,000 - $75,000.
Enterprise AI deals look nothing like the pricing tiers on your website. They are negotiated, customized, and often structured around committed usage volumes or outcome-based components. The website price is a starting point, not a ceiling or a floor.
Key enterprise pricing principles:
| Segment | Typical ACV | Deal Cycle | Pricing Model | Key Buyer |
|---|---|---|---|---|
| SMB | $600 - $6,000 | < 1 week | Monthly subscription | Individual user / team lead |
| Mid-market | $18,000 - $75,000 | 4-8 weeks | Annual subscription + usage | VP / Director |
| Enterprise | $100,000 - $500,000+ | 3-9 months | Negotiated platform deal | VP + Legal + IT + Procurement |
Every AI founder I know has faced this: a prospect says, "ChatGPT does something similar for $20/month. Why would I pay $300/month for your product?"
This is the price anchoring problem, and it is almost never a price problem. It is a value communication problem.
ChatGPT at $20/month is a general-purpose AI assistant. It does many things reasonably well. Your vertical AI product should do one specific thing dramatically better than a general AI — and that specific thing should be the thing your customer needs most.
The customer comparing you to ChatGPT has not yet understood this. They see "AI product" and anchor to the reference price they know. Your job is to reframe the comparison.
Strategy 1: ROI anchoring. Shift the conversation from price to value. "ChatGPT at $20/month is a great general tool. Our product is specifically designed for [your use case]. Customers who deploy it see [specific measurable outcome — e.g., 40% faster, 3x more, $X saved per month]. If it saves you $5,000 per month, the $300/month is not a cost — it's a 16x ROI on a software investment."
Strategy 2: Workflow integration anchoring. General AI requires the user to know how to use it for their specific case. Vertical AI is pre-configured, pre-trained on domain data, and integrated into the workflow. "You can use ChatGPT, but you'd need to paste in your data each time, write your own prompts, manually format the output, and copy it into your system. Our product does all of that automatically. You're not paying for AI — you're paying for the workflow that makes AI useful."
Strategy 3: Accuracy/trust anchoring. General AI hallucinates. Vertical AI trained on domain-specific data with guardrails is more accurate for that domain. "For a general task, ChatGPT is great. For [your domain], the error rate on general models is too high for [customer's use case]. Our model is fine-tuned on [domain] data and validated against [domain benchmark]. That accuracy difference matters when [consequence of an error — legal exposure, compliance failure, customer trust]."
The goal is not to win a feature comparison. The goal is to establish a reference frame where the comparison to ChatGPT $20/month is irrelevant because you're solving a different, more specific, and more valuable problem.
Value-based pricing is the discipline of setting your price based on the economic value you create for the customer, rather than based on your costs or what competitors charge. It is the right approach for AI products and it requires real work to execute.
Step 1: Identify the value driver. What is the primary measurable outcome your AI product creates? Time saved per user per week? Error rate reduced? Revenue increased? Tickets resolved? Candidates sourced? Be specific. "Better outcomes" is not a value driver. "45 minutes of analyst time saved per report" is.
Step 2: Quantify the value per unit. Assign a dollar value to the value driver. If the value driver is time saved: how much is the user's time worth? A marketing analyst at $80k/year costs roughly $40/hour loaded. If your product saves them 45 minutes per report and they write 8 reports per week, that's 6 hours/week = $240/week in time value created per user.
Step 3: Determine your capture rate. Value-based pricing theory says you should capture somewhere between 10% and 30% of the value you create. Below 10%, you're chronically under-priced and risk being taken for granted. Above 30%, customers start to feel like you're capturing more value than you deserve and look for alternatives.
At $240/week in value per user, 20% capture = $48/week = ~$192/month per user. That's your value-based price ceiling. Anything below that is defensible. Anything above requires exceptional ROI evidence or unique lock-in.
Step 4: Validate against willingness to pay. Value calculations are theoretical. Actual willingness to pay is empirical. You find it through direct customer conversations ("what would you pay for this?"), price sensitivity surveys, and A/B tests on pricing pages. The value calculation is the compass; willingness-to-pay testing is the GPS.
Never set your price based solely on value calculations without empirical willingness-to-pay data. Value is what you create; price is what you capture; the gap between them is where negotiation, competition, and psychology live.
Getting the tier structure right is as important as getting the price right. The wrong packaging will cause customers to land in the wrong tier, create friction in upgrades, and destroy your expansion revenue.
| Tier | Target User | Price Range | What's Included |
|---|---|---|---|
| Free | Individual explorers, developers testing the API | $0 | Core AI feature with volume cap, 7-30 day history, no integrations |
| Starter/Pro | Individual power users, small teams | $25 - $99/month | Higher volume, more features, some integrations, email support |
| Team/Business | Teams of 5-50, mid-market buyers | $100 - $500/month | Full feature set, collaboration, SSO, priority support, onboarding |
| Enterprise | Large orgs, 50+ users, high-security requirements | Custom ($1,000+/month) | Custom contracts, dedicated CSM, SLA, compliance, fine-tuning |
The most common mistake: restricting access to the AI feature itself in lower tiers, rather than restricting volume or advanced features. If a user on the Free tier cannot experience the core AI value proposition, they will never convert. Gate the ceiling, not the floor.
Do NOT gate in Free/Starter:
DO gate in Free/Starter:
Every tier structure should be designed with expansion in mind. The question is: what natural trigger moves a customer from Tier 1 to Tier 2, from Tier 2 to Tier 3?
The best expansion triggers are organic signals of success: a customer hitting their monthly output cap means the product is working. That moment — when they're about to be rate-limited — is the highest-intent upgrade moment you will ever have. Make the upgrade path frictionless. One click. Instant access. No manual sales process required until you get to enterprise.
I've made some of these personally and watched others make the rest.
Mistake 1: Pricing to cover current API costs without modeling future usage growth. Your API costs per user today are not the same as they'll be when users are deeply embedded in their workflows and generating 10x the output volume. Price for where your heavy users will be in 6 months, not where they are today.
Mistake 2: Using the wrong unit of value. A document AI priced "per document" makes sense until enterprise customers realize they can batch large projects into single documents. Price for the actual value delivered, not for a proxy that customers can game.
Mistake 3: Ignoring cost of abuse. Free tiers with unlimited generation are magnets for abuse — prompt injection testing, bot usage, automated scraping. Every minute of fraudulent usage is real compute cost. Rate limits and usage caps in free tiers are not just pricing policy; they are fraud prevention.
Mistake 4: Setting prices without a gross margin model. Build a spreadsheet. Model 100 customers with your median usage. Calculate your monthly COGS including inference, storage, and support. Calculate your gross margin. If it's below 50%, reprice before you scale the problem.
Mistake 5: Annual plans without usage caps. An annual plan with unlimited usage is a blank check at the price. Always include a fair use policy or explicit usage limits on annual plans, or build in an overage mechanism.
Mistake 6: Letting enterprise customers negotiate your unit economics into unprofitability. Enterprise buyers are skilled negotiators. They will push for pricing that makes no sense at your scale. Know your floor before you walk into any enterprise negotiation. The floor is the price below which the deal is margin-negative.
Mistake 7: Copying competitor pricing without understanding their cost structure. Your competitor may have secured a preferential API deal, may be running their own fine-tuned models, or may have raised enough capital to subsidize their pricing. Do not anchor your pricing to theirs without understanding what they're actually paying.
Pricing is not a one-time decision. It is an ongoing experiment. Here's how I approach price testing for AI products.
Stage 1: Qualitative research (before launch). Talk to 20-30 potential customers before you set your initial price. Use the Van Westendorp Price Sensitivity Meter: ask what price would be "too cheap" (low quality concern), "cheap but acceptable," "expensive but I'd pay it," and "too expensive." This is also covered in the beta playbook as the week-5 willingness-to-pay discovery exercise. The overlap between "cheap but acceptable" and "expensive but I'd pay it" is your acceptable price range.
Stage 2: Cohort-based price testing (months 1-6). Offer different prices to different acquisition cohorts. Do not run simultaneous A/B tests on the same audience — it creates pricing inconsistency. Instead, test $X for new signups in month 1, $Y in month 2, and compare conversion rates, activation rates, and 90-day retention across cohorts.
Stage 3: Price tier optimization (months 6-18). Once you have paying customers, look at which tier they land in, which tier they upgrade from, and which tier they churn from most. A high concentration of customers in your top self-serve tier often signals that the tier below it is underpriced. High concentration in your lowest tier suggests either a value delivery problem or a pricing structure problem.
Stage 4: Enterprise price discovery (ongoing). Enterprise pricing is discovered through deals, not set through tiers. Log every enterprise deal — the opening ask, the final number, what drove the negotiation. After 10-15 deals, patterns emerge in what enterprise buyers are willing to pay and what moves the number.
| Testing Stage | Method | What You're Learning | Timeline |
|---|---|---|---|
| Pre-launch | Van Westendorp + 1:1 interviews | Acceptable price range, key value drivers | Weeks 1-4 |
| Early post-launch | Cohort price testing | Conversion rate at different price points | Months 1-6 |
| Growth stage | Tier analysis + upgrade patterns | Where tier boundaries should be | Months 6-18 |
| Enterprise | Deal logging + win/loss analysis | Enterprise willingness to pay | Ongoing |
Pricing is never done. The right price for month 1 is almost never the right price for month 18. Build the muscle of pricing iteration early so you can move quickly when the data tells you something needs to change.
Should I put my enterprise pricing on my website?
No. Enterprise pricing belongs in a sales conversation, not on a public pricing page. Putting a number — even a "starting at $X" — anchors the negotiation before you've had a chance to understand the customer's budget, use case, or scale. Your enterprise page should have a "Contact us" CTA, not a price.
How do I price if I don't yet know my usage distribution?
Estimate conservatively. Talk to 10 potential customers about their expected usage. Model the high, median, and low case. Price to be profitable at the median and solvent at the high case. Offer early customers a "founding pricing" rate in exchange for detailed usage reporting — this gives you real data quickly.
When should I raise prices?
Three signals: (1) Your close rate on new customers is above 50% — high close rates indicate you're underpriced. (2) Your gross margins are above 80% on a fully loaded basis — you have room between your cost and your value. (3) Customers are telling you the product is "a no-brainer at this price" — this is a warning that you're leaving money on the table, not a compliment.
Can I grandfather existing customers when I raise prices?
You can, but you should not do it indefinitely. A standard approach: new prices apply to new customers immediately, existing customers are grandfathered for 12 months, then migrate at a 20-30% discount from new pricing. This respects the relationship without locking you into old economics forever.
How do I handle customers who want to pay per outcome rather than per usage?
Outcome-based pricing is attractive but requires careful contract design. You need: a clear definition of the outcome, an agreed measurement methodology, a minimum payment floor that covers your costs regardless of outcomes, and a cap on your upside so the deal doesn't become unpredictable. If you can negotiate all four, outcome-based deals can be highly valuable — they align incentives and often command premium pricing.
What's the right gross margin target for my AI product?
The venture-bankable target is 60%+ at Series A, improving to 70%+ by Series B. Below 60% consistently triggers investor concern about unit economics. Above 75% with a clear path to 80%+ is a strong margin profile that supports aggressive growth investment. If you're below 60%, the priority is model optimization and pricing adjustment before you scale headcount.
How much should I discount for annual contracts?
The standard is 15-20% off monthly pricing for annual commits. Some founders go to 25% for their first year to build up their annual ARR base. Do not go above 25% — it trains customers to expect deep discounts and sets a low anchor for renewal negotiations.
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