Weaverse's New Pricing Plans for 2026
Introducing Growth and Scale: Weaverse's New Pricing for 2026
As a founder, I've learned that pricing should tell a story—and that story should be about you, not us. Today, I'm excited to announce our new pricing structure: Pay-As-You-Go, Growth, and Scale. They're the result of listening closely to how our customers are building, what they need as they expand, and where we need to invest to support their ambitions.
For two years, we've watched merchants build incredible headless storefronts on Weaverse. We've seen stores grow from thousands of monthly visitors to hundreds of thousands. We've seen merchants push our platform further than we expected—and we wanted our pricing to reflect that reality.
Why We're Evolving Our Pricing
Growth is messy. Your store might handle 50,000 monthly views today and 500,000 next year. We wanted a pricing structure without hard walls—one that lets you start lean and scale smoothly without friction.
We also listened to what matters most: flexibility and support. Merchants told us that as they scale, they need more than features—they need reliability, faster response times, and room to grow without worrying about hitting limits. Our new plans directly address that feedback.
By introducing PAYG, Growth, and Scale, we're creating a natural progression that matches your business stage. You can start where you need to be, grow without friction, and access the support tier that makes sense for your operation.
Importantly, we're being transparent about why the plans cost what they do. Growth and Scale include substantially more infrastructure investment per customer—more capacity, faster processing, priority support—and we're pricing them to reflect that reality while staying competitive in the market. We're not the cheapest option, but we're designed to be the right option for merchants building seriously.
Our New Pricing Plans
Pay-As-You-Go (PAYG) — Usage-Based
PAYG is built for merchants who want full flexibility without commitment. You're getting:
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Usage-based billing — only pay for what you use, no wasted capacity
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Full Weaverse features — access to all design and optimization tools
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Standard support — reach our team when you need them
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No monthly minimum — perfect for testing, seasonal stores, or variable traffic
Who's it for? New stores launching, seasonal businesses, or merchants who want to test Weaverse without committing to a fixed plan.
Growth Plan — $59/month
Growth is built for merchants doing serious volume. You're getting:
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500,000 monthly view credits — enough for stores handling meaningful traffic without compromise
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Priority support — reach our team faster when you need them
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All Growth features — full access to Weaverse's design and optimization tools
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Predictable pricing — know exactly what you'll pay each month
Who's it for? Growing stores that are scaling beyond initial launch. You're handling 200K–500K monthly views and need breathing room to expand without hitting limits.
Scale Plan — $299/month
Scale is for established merchants running high-volume operations. You get:
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3,000,000 monthly view credits — handle enterprise-level traffic without constraints
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Premium support — dedicated support priority and faster response times
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All Scale features — white-glove support, custom integrations, and advanced optimization
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Built for enterprise — infrastructure designed for the biggest operations
Who's it for? Established merchants with high-traffic storefronts generating 500K+ monthly views. You need reliability, speed, and a team that understands your operation.
Quick Comparison
| Plan | Monthly Cost | View Credits | Support Level | Best For |
|---|---|---|---|---|
| PAYG | Usage-based | Pay per use | Standard | Testing, seasonal, variable traffic |
| Growth | $59/month | 500,000 | Priority | Growing stores, 200K–500K views/month |
| Scale | $299/month | 3,000,000 | Premium | Enterprise, 500K+ views/month |
Choosing the Right Plan for Your Store
The honest answer: look at your traffic and your growth trajectory.
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Launching or testing? Start with PAYG. Only pay for what you use, no commitment.
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Getting 200K–500K views monthly? Growth is your spot. Better capacity, priority support, and room to expand.
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Running 500K+ views or managing high-volume operations? Scale gives you the infrastructure and support to handle anything.
Still not sure? Think about this: What's the cost of slowdowns or support delays if your store goes down during peak sales? What's the value of priority support when you need it? If that cost is more than the difference between plans, upgrade.
FAQ
Can I switch between plans later? Yes. Upgrade or downgrade anytime, and billing adjusts on your next cycle. No penalties.
What are view credits? View credits represent the monthly capacity of your storefront—how many visitor interactions your site can handle. One view credit = one page view. If you exceed your monthly allotment, we don't cut you off; we just notify you and work with you on next steps. It's transparent, not punitive.
How does PAYG billing work? You're charged based on actual usage at the end of each billing cycle. No monthly minimum, no wasted capacity. Great for stores with variable or unpredictable traffic.
When should I switch from PAYG to Growth? When your monthly usage consistently approaches or exceeds the equivalent of Growth plan cost ($59), it makes sense to lock in the fixed rate for predictability and priority support.
How do I know which plan I actually need? Check your Weaverse analytics dashboard for your typical monthly views, then match that to a plan with headroom for growth. Our support team is also here to help—just ask.
Ready to Explore?
If you're already with us, you're set—just keep building. If you're new to Weaverse, now's a great time to start. These plans are designed around real customer needs, and that means you can trust that what you're paying for actually matters.
View Our Pricing Plans — Find the tier that fits your store.
Questions? Contact Our Team — We're here to help you choose and get started.
—Paul, Founder & CEO, Weaverse
P.S. Thank you to every merchant building with Weaverse. Your feedback shaped this announcement. Keep building fast, beautiful, and scalable storefronts.
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Agentic Commerce on Shopify: How to Make Your Hydrogen Store AI-Agent-Ready in 2026
Agentic Commerce on Shopify: How to Make Your Hydrogen Store AI-Agent-Ready in 2026 AI agents are no longer just helping customers research products. They’re starting to shop for them. That changes what it means to optimize a Shopify storefront in 2026. If a customer asks ChatGPT, Gemini, Copilot, or Perplexity to find the best product for a need, the winning store may not be the one with the prettiest homepage. It may be the one with the cleanest product data, the clearest schema, and the most machine-readable storefront. Shopify sees where this is going. Agentic Storefronts are now live. Universal Commerce Protocol (UCP), co-developed with Google, gives merchants a new path to become discoverable and transactable in AI-driven buying flows. For Hydrogen teams, this shift is not a threat. It is an advantage. Because headless storefronts already separate presentation from data, they are in a better position to serve both humans and machines—if the implementation is done right. The shift: from browse-first to ask-first commerce Traditional ecommerce assumes a human visits your site, clicks around, compares options, reads reviews, and decides. Agentic commerce compresses that flow. Now the customer says: Find me leather boots under $300 Compare the best protein powders without artificial sweeteners Reorder the best moisturizer I bought last time An AI agent handles discovery, comparison, filtering, and, increasingly, transaction steps. In that world, your storefront still matters for human trust and conversion. But your discoverability layer changes completely. Instead of competing only on: branding design merchandising ad creative you also compete on: structured product attributes variant completeness stable identifiers machine-readable policy and offer data feed quality schema quality storefront and API reliability If AI systems cannot parse your catalog confidently, they will simply recommend someone else. Why Shopify merchants should care now This is not theoretical anymore. Shopify has already started building for agentic commerce through: Agentic Storefronts Shopify Catalog UCP stronger machine-readable commerce surfaces improved developer tooling around modern Hydrogen builds The strategic message is clear: commerce interfaces are expanding beyond the browser. Your customer may still buy from a human-facing storefront. But the path to that purchase may begin inside an AI interface that never sees your hero banner, campaign landing page, or carefully tuned homepage flow. That means the old optimization stack is incomplete. A store can look premium and still be invisible to AI-driven discovery. The real bottleneck: bad product data Most merchants do not have an AI-readiness problem. They have a product data discipline problem. This is where many catalogs break down: vague product titles inconsistent variant naming missing GTINs incomplete metafields missing dimensions, materials, or care specs untyped custom data weak or missing Product schema broken canonical relationships across variants For humans, you can sometimes get away with that. For AI systems, you usually cannot. Agents work better when they can rely on structured, typed, normalized inputs. That includes: brand product type size color material dimensions availability price condition fulfillment details review signals return policies If those fields are incomplete, the agent has less confidence. Less confidence means less visibility. Why Hydrogen stores have an architectural advantage Hydrogen teams are better positioned than legacy storefront teams for one reason: the architecture already separates content and data from presentation. That matters because AI readiness is mostly about the quality of the data layer. A well-built Hydrogen store can: output clean JSON-LD from server-rendered routes expose typed metafield data consistently support structured product and collection pages generate machine-readable manifests and feed layers keep storefront UX flexible without compromising data integrity In other words, Hydrogen makes it easier to build a storefront that works for humans on the surface and machines underneath. That is exactly the direction commerce is heading. Where Weaverse fits This is also why Weaverse has a natural position in the shift to agentic commerce. The real opportunity is not choosing between beautiful storefronts for humans and structured storefronts for machines. The opportunity is building both from the same source of truth. With the right Weaverse implementation, teams can: keep merchant-friendly visual editing preserve a structured section architecture flow metafield data into storefront rendering support stronger schema outputs reduce the gap between merchandisers and developers That matters because AI readiness cannot depend on engineers manually patching every product page forever. The system has to be maintainable by the actual team running the store. The 2026 AI-agent-readiness checklist for Shopify + Hydrogen teams If you want your storefront to stay visible in AI-driven shopping flows, start here: 1. Tighten product titles Every title should clearly communicate: brand product type key differentiator Avoid vague naming. Keep titles precise and scannable. 2. Complete variant-level data Every variant should have: accurate size, color, and material data availability price SKU GTIN where applicable 3. Populate critical metafields At minimum, make sure structured data exists for: material dimensions weight care instructions certifications compatibility or use case shipping or fulfillment constraints where relevant 4. Implement JSON-LD properly Support: Product Offer ProductGroup where relevant review and aggregate rating where valid 5. Clean up internal product data logic Make sure data is consistent across: PDP collection cards search results feeds structured data outputs 6. Enable Shopify’s discovery surfaces Where relevant, prepare for: Shopify Catalog Agentic Storefront pathways UCP-compatible discovery patterns as they mature 7. Validate what machines actually see Do not just inspect the page visually. Test structured outputs and rich result eligibility. The mistake merchants will make A lot of brands will hear “agentic commerce” and respond with content theater. They will publish hot takes, add “AI-ready” to landing pages, and bolt on a chatbot. But that is not the hard part. The hard part is cleaning the data model. Because AI visibility is not a branding claim. It is an operational outcome. The winners will be the teams that treat: product data schema identifiers merchandising structure storefront architecture as revenue infrastructure. Final takeaway The future of commerce is not humans versus AI. It is structured backend for machines and compelling frontend for humans. That is the middle ground Shopify is moving toward. And it is exactly where Hydrogen and Weaverse can win. If your storefront cannot pass the AI-agent parse test, you will lose demand long before a customer ever reaches your site. Want to make your Hydrogen store AI-agent-ready without sacrificing visual control? Build it with Weaverse. Start free at https://weaverse.io.
ChatGPT Quit Agentic Commerce. That Doesn't Mean You Should.
ChatGPT Quit Agentic Commerce. That Doesn't Mean You Should. Two posts this week confirm the same signal: Juozas Kaziukėnas: ChatGPT is abandoning agentic commerce after 5 months. Users researched products but didn't buy through the chatbot. Kelly Goetsch: OpenAI is losing ground to Anthropic and deprioritizing commerce because enterprise commerce is harder than they expected. The headlines say "AI commerce is failing." The real story is more nuanced. Why They Retreated Agentic commerce — AI agents that discover, compare, and complete purchases on behalf of users — hit three hard walls: 1. Catalog Normalization Is Brutal Product data (pricing, inventory, variants, availability) needs to be: Standardized across every retailer Constantly updated in real-time Accurate enough for AI to trust Only Google Shopping has done this at scale. ChatGPT couldn't. 2. Trust Gap: Research ≠ Purchase Users were happy to research products inside ChatGPT. But when it came time to buy, they didn't trust the chatbot with payment. This isn't a ChatGPT problem — it's a user behavior problem. Facebook Shops and Google's "Buy with Google" hit the same wall. 3. Fraud and Error Safeguards Commerce and payment firms need real safeguards against: AI initiating fraudulent transactions AI making erroneous purchases Liability when something goes wrong These safeguards don't exist yet at scale. Without them, platforms retreat to just driving traffic to retailer sites. What They're Not Saying This is a pause, not a permanent retreat. Kelly Goetsch explicitly predicts: "they'll re-prioritize commerce in a few months once they figure out the infrastructure layer." Juozas Kaziukėnas frames it as lack of conviction: Chinese AI platforms like Alibaba's Qwen are spending hundreds of millions to force the behavior change. ChatGPT gave up too early. Either way, they'll be back. The question is: will your store be ready? The Infrastructure Layer Wins Long-Term When AI platforms return to agentic commerce, the stores that win won't be the ones with the prettiest themes or the best brand storytelling. They'll be the ones with: Structured product data that agents can read and reason about Clean APIs that don't break under automation Checkout flows with real safeguards and clear liability boundaries Machine-readable manifests (like UCP's ucp.json) that broadcast capabilities to agents This is exactly what Hydrogen + Storefront API architectures are built for. Why Hydrogen Teams Have a Structural Advantage Monolithic Liquid stores have product data trapped in rendered HTML. AI agents have a hard time reasoning over server-rendered markup. Hydrogen stores built on the Storefront API already expose: Structured, queryable product data Stable cart and checkout mutations Clean authentication via Customer Account API Market-aware URLs for localized experiences Adding UCP support or similar agent-ready layers is an extension of the architecture, not a rewrite. What You Should Do While Big Platforms Regroup 1. Audit your data layer Can an AI agent query your product catalog via clean API? Or is your data trapped in theme templates? 2. Check your checkout safeguards Do you have clear boundaries for what automated systems can and can't do? Fraud detection? Liability clarity? 3. Make your capabilities machine-readable If you're on Hydrogen, you're most of the way there. The next step is declaring your capabilities in a format agents can discover. 4. Don't wait for platforms to figure it out The stores that win agentic commerce won't be the ones who waited for ChatGPT to solve catalog normalization. They'll be the ones who made their data clean, their APIs stable, and their checkout safe — before the next platform tries again. The Honest Take Agentic commerce is harder than the hype. The platforms with the biggest AI models just hit the wall. But the infrastructure layer still wins. If your store is machine-readable, API-first, and checkout-safe, you become the integration backbone for whichever platform eventually cracks it. The platforms quit early. That doesn't mean you should. Sources Juozas Kaziukėnas on ChatGPT abandoning agentic commerce: https://www.linkedin.com/posts/juozas_chatgpt-is-abandoning-agentic-commerce-its-activity-7435308306329473025-Ncy0 Kelly Goetsch on OpenAI deprioritizing commerce: https://www.linkedin.com/posts/kellygoetsch_feels-like-openai-is-quickly-losing-ground-activity-7435323329483460608-GgKK Shopify headless architecture: https://shopify.dev/docs/storefronts/headless
How to Set Up Color Swatches in Your Shopify Hydrogen Store
How to Set Up Color Swatches in Your Shopify Hydrogen Store If you're building a Hydrogen storefront with variant-heavy catalogs, swatches are one of the highest-impact UX improvements you can ship quickly. This guide walks through the full implementation path from Shopify Admin setup to production-ready React components. Color swatches transform the shopping experience by allowing customers to visualize product variants at a glance. Instead of reading through dropdown menus, customers see the actual colors—making purchase decisions faster and more intuitive. In this comprehensive tutorial, we'll walk through implementing color swatches in a Shopify Hydrogen store, from configuring the data in Shopify Admin to rendering the swatches in your React components using the modern optionValues API (released in 2024-07). Part 1: Setting Up Swatches in Shopify Admin Step 1: Access Product Variant Options Log in to your Shopify Admin Navigate to Products → Select a product with color variants Scroll to the Variants section Click Edit Options next to the "Color" option Step 2: Configure Swatch Data For each color value, you can set: Swatch TypeWhen to UseExample Solid Color (Hex)Single, uniform colors#FF0000 for Red Image UploadPatterns, textures, gradientsPlaid, Leopard print, Tie-dye Named ColorStandard CSS colors"Navy", "Tomato", "Teal" Best Practices for Swatch Configuration ✅ Use Hex Codes for Accuracy: #1E3A8A is more reliable than color names ✅ Optimize Swatch Images: Keep them under 200x200px ✅ Consistent Naming: Use "Navy Blue" across all products, not sometimes "Navy" ✅ Fallback Ready: If no hex is set, the code will attempt to parse the option name as a color Part 2: Querying Swatch Data with GraphQL The optionValues API Shopify's Storefront API provides the optionValues field—a dedicated, efficient way to fetch product option data including swatches. GraphQL Fragment Add this fragment to your app/graphql/fragments.ts: fragment ProductOption on ProductOption { name optionValues { name firstSelectableVariant { id availableForSale price { amount currencyCode } image { url altText } } swatch { color image { previewImage { # Optimize: Resize swatch images server-side url(transform: { width: 100, height: 100, crop: CENTER }) altText } } } } } Key fields: swatch.color: Hex code (e.g., #FF5733) swatch.image.previewImage: Optimized image for patterns/textures firstSelectableVariant: First available variant with this option—critical for navigation Using the Fragment in Product Queries query Product($handle: String!) { product(handle: $handle) { id title options { ...ProductOption } variants(first: 100) { nodes { id availableForSale selectedOptions { name value } } } } } Part 3: Building the React Components 1. Color Utility Functions Create helpers to handle edge cases like light colors on white backgrounds. // app/utils/colors.ts import { colord } from "colord"; /** * Validates if a string is a parseable color * Supports hex, RGB, HSL, and named colors */ export function isValidColor(color: string): boolean { return colord(color).isValid(); } /** * Detects if a color is "light" (brightness > threshold) * Used to add borders to white/cream/yellow swatches */ export function isLightColor(color: string, threshold = 0.8): boolean { const c = colord(color); return c.isValid() && c.brightness() > threshold; } Install the dependency: npm install colord 2. Swatch Component Create a reusable swatch component with local type definitions: // app/components/product/ProductOptionSwatches.tsx import { Image } from "@shopify/hydrogen"; import { cn } from "~/utils/cn"; import { isLightColor, isValidColor } from "~/utils/colors"; // Define types locally for better portability export type SwatchOptionValue = { name: string; selected: boolean; // Computed prop available: boolean; // Computed prop swatch?: { color?: string | null; image?: { previewImage?: { url: string; altText?: string | null; } | null; } | null; } | null; }; type SwatchProps = { optionValues: SwatchOptionValue[]; onSelect: (value: SwatchOptionValue) => void; }; export function ProductOptionSwatches({ optionValues, onSelect }: SwatchProps) { return ( <div className="flex flex-wrap gap-3"> {optionValues.map((value) => ( <SwatchButton key={value.name} value={value} onClick={() => onSelect(value)} /> ))} </div> ); } function SwatchButton({ value, onClick }: { value: SwatchOptionValue; onClick: () => void; }) { const { name, selected, available, swatch } = value; const hexColor = swatch?.color || name; // Fallback to name const image = swatch?.image?.previewImage; return ( <button type="button" disabled={!available} onClick={onClick} title={name} className={cn( "size-8 overflow-hidden rounded-full transition-all", "outline-1 outline-offset-2", selected ? "outline outline-gray-900" : "outline-transparent hover:outline hover:outline-gray-400", !available && "opacity-50 cursor-not-allowed diagonal-strike" )} > {image ? ( <Image data={image} className="h-full w-full object-cover" width={32} height={32} sizes="32px" /> ) : ( <span className={cn( "block h-full w-full", (!isValidColor(hexColor) || isLightColor(hexColor)) && "border border-gray-200" )} style={{ backgroundColor: hexColor }} > <span className="sr-only">{name}</span> </span> )} </button> ); } 3. Diagonal Strike-Through for Unavailable Variants Add this CSS utility to your app/styles/app.css: .diagonal-strike { position: relative; } .diagonal-strike::after { content: ""; position: absolute; inset: 0; background: linear-gradient( to bottom right, transparent calc(50% - 1px), #999 calc(50% - 1px), #999 calc(50% + 1px), transparent calc(50% + 1px) ); pointer-events: none; } 4. Using the Component In your route file, map the raw GraphQL data to the SwatchOptionValue type by calculating selected and available status. // app/routes/products.$handle.tsx import { useNavigate, useLoaderData } from "@remix-run/react"; import { ProductOptionSwatches, type SwatchOptionValue } from "~/components/product/ProductOptionSwatches"; export default function ProductPage() { const { product, selectedVariant } = useLoaderData<typeof loader>(); const navigate = useNavigate(); // 1. Find the color option const colorOption = product.options.find( (opt) => ["Color", "Colors", "Colour", "Colours"].includes(opt.name) ); // 2. Map raw data to component props // We need to calculate 'selected' and 'available' based on current context const swatches: SwatchOptionValue[] | undefined = colorOption?.optionValues.map((value) => { // Check if this is the currently selected value const isSelected = selectedVariant?.selectedOptions.some( (opt) => opt.name === colorOption.name && opt.value === value.name ); return { ...value, selected: !!isSelected, available: !!value.firstSelectableVariant?.availableForSale, }; }); const handleSwatchSelect = (value: SwatchOptionValue) => { // Navigate to the corresponding variant URL if (value.firstSelectableVariant) { navigate(`?variant=${value.firstSelectableVariant.id.split('/').pop()}`, { preventScrollReset: true, replace: true }); } }; return ( <div> {colorOption && swatches && ( <div className="space-y-2"> <h3 className="font-medium">Color: {selectedVariant?.selectedOptions.find(o => o.name === colorOption.name)?.value}</h3> <ProductOptionSwatches optionValues={swatches} onSelect={handleSwatchSelect} /> </div> )} </div> ); } Final checklist Before shipping, verify: Swatch data is configured for every color option in Shopify Admin Variant availability is reflected visually (disabled + strike-through) Light colors have visible borders Swatch images are optimized and transformed server-side URL/state updates correctly when users change swatches With this setup, shoppers can scan options faster, reduce misclicks, and reach purchase decisions with less friction.
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