Build vs buy for product image workflows

During the State of GenAI in Marketplaces webinar, Jeff Strauss, Head of Imaging at Photoroom, asked the question on every enterprise leader's mind: Do you build or buy solutions in generative AI, and what helps you decide?

A question to which Laura McGinnis, Principal at Balderton Capital, shared: "Strategic brands focus internal teams on proprietary data and workflows, and partner where speed, quality, and reliability matter most."

Laura’s response is a clear principle for deciding when to build and when to buy. However, applying it is where things get complicated. As generative AI reshapes how products are photographed, listed, and merchandised online, knowing where your proprietary advantage actually lies and where a partner delivers more value requires a harder look at real costs, timelines, and technical tradeoffs than most teams expect.

This piece compares the build vs buy tradeoffs, explains when to choose what approach, and provides a build vs buy decision framework to help you make a data-driven decision—drawing on conversations with technical and product leaders who've navigated this exact choice.

First, what does it mean to build and buy an image processing infrastructure?

• Build: Develop a custom tool in-house with your engineering team.

• Buy: Purchase or subscribe to an existing third-party solution, either as a complete workflow platform or as a specific component (like an API) that complements internally built solutions.

Table of content

• How to compare build vs buy options

• When to build in-house image processing infrastructure

• Why businesses buy Photoroom 

• How to decide whether to build or buy (decision framework)

• Build vs buy: FAQs

How to compare build vs buy options

Building your own image processing infrastructure versus buying from a vendor is no longer a question of feasibility; it's a question of tradeoffs. Today's cloud infrastructure and open-source tools have reduced the technical and financial barriers associated with software development. And should you choose to buy, you'll often find several solutions in the market to pick from.

But opting for either path means gaining one advantage and losing another. For e-commerce brands managing tight margins, massive catalogs, and competitive time-to-market pressure, that tradeoff plays out most visibly across three areas: cost, speed, and production robustness.

What's the total cost of ownership vs partnership?

The cost of integrating a third-party platform depends on a vendor's subscription model, while the complete cost of building a custom image production architecture extends beyond initial development expenses.

With building, there's often a gap between upfront and future costs. Even leveraging a foundation model like Gemini 3 Pro Image (at $2 per million input tokens) still requires developer time to integrate, tune, and maintain. The API fees are just the entry point. Buying often means more predictable subscription fees.

Here's a complete cost comparison of building vs. partnering for product image editing:

While buying comes with risks like vendor dependency, these risks are bounded and predictable. The costs of building are open-ended and compound over time.

As Laura advises, "We favor partnering or buying when the tech is evolving too quickly, which it is with AI. The risk with building in-house is the opportunity cost. Your internal tools will often lack model improvements, while best-in-class providers are iterating every week."

You can manage risks with vendors by choosing a platform with flexible AI integration, transparent pricing, and a roadmap aligned with your business needs. Brands like the luxury reseller Valuence Japan have evolved their workflow and now saves $80K per year on outsourcing costs alone since integrating Photoroom’s architecture.

Photoroom is a model-agnostic image production platform with a flexible processing workflow that ensures you don't have to choose between outgrowing your system or committing to a single vendor's AI model. You get long-term scalability without the costs of migrating platforms.

What's the time-to-value for build vs buy?

Time to value measures how quickly the solution you adopt starts delivering measurable results. From our partnerships with future-focused e-commerce brands, we’ve seen that buying consistently delivers faster time to value than building.

Here's how the time tradeoff compares for both options:

According to McKinsey, enterprises with high-performing IT teams have up to 35% higher revenue growth, and the main factor driving this performance is faster time-to-market for implementing changes.

Building gives you specialized functionality that doesn't exist in the market, but you pay for it in time. A custom image editing tool doesn't start delivering value the day you decide to build it. It delivers value the day it's stable enough to run in production. For most teams, that requires months of engineering work across building, testing, and rollout.

Buying means shipping fast within a vendor's capabilities. With a specialized vendor like Photoroom, the infrastructure works on arrival due to years of continuous iteration. You're plugging into compressed development cycles that your team would otherwise run themselves.

For most e-commerce operations handling large catalogs on tight timelines, delay is a competitive cost. Anne-Claire Baschet, Chief Data & AI Officer at Mirakl, emphasized this advantage: "For us, the decision starts with speed. You can iterate as much as you want internally, but you're not sure what your users actually need until you're in the market. So we ask: what can help us get to market faster? We leverage Photoroom to move faster."

Photoroom delivers complete image workflows needed for large-scale businesses, ensuring brand compliance across SKUs. This way, teams can focus engineering capacity on what differentiates their business.

Which is more production-ready: in-house or purchased image infrastructure?

One of the most recurring challenges businesses face before partnering with Photoroom is gradual performance decline. The pattern looks like this:

A team builds with open-source models → early results look promising → accuracy stalls a few months in → edge cases pile up → the system performs poorly when processing large volumes.

In-house tools don't fail because the AI model is bad. They fail because models are only one component of an image production solution.

A production platform is a system comprising AI models and several other components, including scale infrastructure, edge-case handling, and compliance. While a general-purpose model like Google’s Gemini 3 Pro Image can get you 80% quality output, the remaining 20% relevant to e-commerce businesses comes from these additional variables.

As Laura puts it: "You don't win by owning every model. You win by owning the feedback loop in the data."

Here's what separates a working model from a working system:

When you buy a production-grade solution, you're not buying a model wrapper; you're buying the system around the model. Owning a model doesn't mean owning the outcome. Owning infrastructure doesn't mean owning reliability or speed. And building without staying current with the latest AI models means your system stagnates while the market moves forward, which can lead to low-quality outputs, causing seller churn and a decline in revenue.

Photoroom runs on NVIDIA GPU-accelerated infrastructure (including H100 GPUs and TensorRT-LLM) for faster inference, continuously trains on new models, and passes those improvements directly to your production stack, so image quality and processing speed improve automatically, without your team rebuilding infrastructure.

"We're a food delivery company, not a photo-enhancing company. We can't move at the same speed as AI models; that's why we partner with companies like Photoroom."

–Nicolas Morales, Product Director at Rappi

The build versus buy comparison ultimately comes down to three core tradeoffs: upfront versus future costs, speed to market versus customization, and operational robustness versus internal ownership. For most e-commerce businesses, buying from a specialized vendor like Photoroom means accessing years of production-grade infrastructure without the ongoing engineering tax of maintaining it yourself.

When to build in-house image processing infrastructure

Building is the right call in specific scenarios where control, differentiation, or unique requirements outweigh the cost and time investment.

1. The capability is core intellectual property

If image editing is your product or your competitive moat depends on proprietary algorithms, building in-house protects differentiation. Custom solutions can't be replicated by competitors who simply buy the same vendor product. Building is a strategic investment when the tool you're building maps directly to what makes your business unique.

2. Your requirements are genuinely unique

Building may be the only path when no existing AI-powered solution addresses your specific use case. However, this situation is rarer than teams often assume. Most "unique" needs are variations of problems vendors have already solved at scale. So, it's worth pressure-testing whether the requirement is truly unique or simply a workflow automation preference a vendor could accommodate.

3. You have a dedicated ML team with 18+ months of runway

Building a production-grade image editing tool isn't a side project. It requires specialized engineers committed for the long haul—not just for the initial build but for the ongoing iteration, retraining, and maintenance that follows.

If you have dedicated ML and infrastructure resources with a long runway, you can justify the investment in building and maintaining custom systems. Without that capacity, builds drain business resources.

4. You need full control over data, security, and roadmap

One legitimate reason to build is for model and data control, specifically if you're a marketplace business in a regulated category.

But it's worth distinguishing between needing to own the model itself and needing control over where data is stored and processed. Both are different problems, and only the first one truly requires a full build. A reliable vendor can provide data protection through data handling policies, deletion guarantees, and regional hosting.

If your competitive advantage comes from proprietary image processing or you have dedicated ML teams for ongoing R&D, building gives you control. However, if your edge is assortment, user experience, or speed to market, which is the case for most e-commerce brands, then buying lets you focus engineering time on what differentiates your business.

Photoroom enables teams to maintain competitive focus by handling production-grade image workflows so engineering resources drive revenue.

When to buy Photoroom's solution for product image editing

Companies choose Photoroom for its speed, quality results, and end-to-end image processing infrastructure. We develop commerce-grade features and constantly iterate to ensure businesses produce consistent quality images, launch products faster, and gain tangible ROI.

Here are three e-commerce use cases for Photoroom's API and why savvy teams continue to integrate Photoroom into their workflows.

1. Background removal at scale

Say you're processing tens to thousands of product images daily. Seller uploads are inconsistent—some studio-quality, others smartphone shots with messy backgrounds. Your listings need to stay true-to-product, preserving color, texture, and subject fidelity across every SKU. You’ll need a reliable batch background remover solution to solve these issues at scale.

Why partner with Photoroom?

• Get industry-leading accuracy: Handle complex subjects like jewelry, hair, fur, glass, and reflections with precision. Benchmark reports consistently show Photoroom outperforms other background removal APIs on difficult product images.

• Maintain consistent output: Ensure on-brand, accurate, and realistic results across all images regardless of input or seller upload quality.

• Process at sub-second speed: Execute API calls in milliseconds to handle high-volume workflows without bottlenecks.

See how Photoroom’s background removal API compares with other solutions →

Why teams integrate Photoroom:

Most teams choose Photoroom’s Remove Background API to move faster, ensure consistency across millions of images, and meet their image requirements from day one.

• Fashion tech platform OpenWardrobe US initially built an in-house solution for background removal but faced challenges with multi-category editing, performance, and high cost. Since switching to Photoroom, they've improved user experience and seen a 2x reduction in editing cost.

• Digital marketing automation platform Smartly partnered with Photoroom after facing data sorting issues with their internal background removal solution. The result: 18.42% increase in Return on Ad Spend (ROAS) and 73% increase in Click-Through Rate (CTR), while saving 20 hours of work.

• Warner Bros used Photoroom to power mass-personalized visuals for the Barbie movie, generating 13M+ user-generated shares and global social reach.

When leading brands use the same AI-powered platform to handle background removal at scale, common challenges have shared solutions. You benefit from existing AI-powered solutions rather than solving issues from scratch and ensure product image standardization. Photoroom provides accurate background removal and the right support for enterprise businesses.

Backgrounds removed from complex photos, with details preserved, using Photoroom.

2. API-first custom workflows

Consider this: you're building a marketplace where image editing is one step in a larger workflow. When a seller uploads a product photo, your system needs to validate quality, remove the background, resize for multiple placements, add overlays, check compliance, and push to your content delivery network (CDN) before the listing goes live. The editing itself isn't what differentiates your platform. You need it to work reliably so your team can focus on building what actually sets you apart: seller tools, recommendations, and fraud detection.

Here’s why you should use the Photoroom API:

• Remove backgrounds with enterprise SLAs: Process images reliably with guaranteed uptime and support for mission-critical workflows.

• Enable virtual model and try-on workflows: Generate fashion model product shots without photoshoots, reducing production costs and time.

• Process in batches with multi-edit flows: Chain together background removal, shadow addition, lighting adjustments, resizing, repositioning, and background generation in a single batch editing workflow.

• Handle async processing with webhooks: Submit large volumes without blocking your application, receiving results when processing completes.

• Transform images and videos at scale: Access flat lay, ghost mannequin, product video generation, clothing recolor, and product beautifier tools without needing separate platforms.

• Integrate across your stack: Connect to PIM systems, DAM workflows, and in-app editing features to automate image editing.

Why teams integrate Photoroom:

Japan's dominant C2C marketplace Mercari built and launched a custom background blur feature using Photoroom's API with a small team of 3–4 engineers. Today, Photoroom processes millions of images monthly in the background, freeing Mercari's team to focus on product innovation.

Photoroom supports your product goals, ensuring that you ship better features for your customers without managing the cost of owning image infrastructure.

Product image transformed to virtual model and ghost mannequin using Photoroom

3. Compliance and enterprise readiness

Let’s say your sales team is closing deals with enterprise retailers, but IT and legal need to sign off before any contract moves forward. They're asking: Is your image processing vendor secure? How do they handle data privacy and legal issues? Are they SOC 2 compliant? Obtaining that compliance alone costs $100K+ and can take up to a year to achieve. Without clear answers and documentation, the deal stalls in procurement for months.

Why choose Photoroom?

• Photoroom is SOC 2 Type II certified for API security, availability, and privacy.

• PCI DSS is covered via Stripe for payment processing, so teams don't handle sensitive card data directly.

• Photoroom is GDPR compliant, ensuring your data is handled with transparency, care, and respect for your rights.

• Enterprise contracts include indemnification provisions for AI-generated images.

• Photoroom does not store API customer images long-term, and no training on customer data without consent.

• Dedicated technical support to guide teams through implementation.

What’s more, our platform provides the servers and computing power needed to process millions of images, plus automatic adoption of better models without migration work.

"We'd been thinking about photo editing with AI for a long time, but we didn't have the knowledge or experience to do it well in-house. That's why we chose to partner with Photoroom. And it was collaborative from the start; our teams ideated and ran a hack week together, which helped us move faster."

–Jacek Rebkowski, Lead Product Designer at Depop

Photoroom delivers the global scalability and security standards that enterprise operations can count on, so your team invests its resources in growth.

Same product picture from above in flat lay and recolored using Photoroom

How to decide whether to build vs buy an image infrastructure

Choosing between building and buying an AI-powered image processing infrastructure requires asking a series of questions about what kind of problem you're solving, how central it is to your business, and what you're optimizing for: speed, control, cost, or differentiation.

Use the build vs buy decision framework below to make a confident decision about building or buying an AI image editing solution.

Consider the following questions before choosing:

• What are we optimizing for: speed or control? Buying optimizes for speed to value. Building optimizes for data and model control and differentiation.

• Does a good solution already exist? If there's a robust market with competing tools and successful users, the vendor has a head start you probably can't close.

• Do we need best-in-class, or just good enough? If a temporary, functional solution will do, building a lightweight version might work. If you need the real thing to handle large volumes, a vendor with years of iteration and dedicated teams is hard to match.

• Do we actually need this? You could solve the problem with existing tools, simpler e-commerce photo automation features, or a lighter-weight approach. Not every problem requires an API. Some simply need an accessible web app.

• Can we solve this problem with a hybrid approach? Use bought tools for well-defined, repeatable tasks and custom-built solutions for the work that differentiates your business.

• What's the total cost of ownership over 3–5 years? Include hiring, infrastructure, maintenance, technical debt, and hidden costs for builds. Include lock-in risk, pricing changes, and switching costs for buys.

The build vs buy analysis isn’t really about build vs. buy. It’s about your business needs vs your available resources. Making a decision requires careful thought. And at Photoroom, we want you to invest in an automated product photography solution that will meet your needs in the short and long term.

For most marketplaces, competitive advantage comes from product strategy, pricing, and speed to launch, rather than building internal image-processing infrastructure.

Photoroom is the production-grade platform for e-commerce photo automation at scale, ensuring product fidelity, brand consistency, and speed, so enterprise teams can focus on work that drives revenue while maintaining full ownership of content and data.

Build vs buy: FAQs

1. Is it cheaper to build or buy software?

For most e-commerce businesses, buying is cheaper. Building requires ML engineers ($150K–$300K), GPU infrastructure, and permanent maintenance costs. During your build or buy analysis, understand the total cost of ownership vs partnership. Vendors like Photoroom cost a fraction of build costs, with predictable pricing and no engineering overhead.

2. What does "build vs buy" mean in software decisions?

Build means developing software in-house with your team. Buy means integrating a third-party solution. In the context of the build vs buy analysis for product images, buying Photoroom means getting background removal, processing, and compliance ready to use. Building means creating all of that yourself.

3. How should I evaluate vendors after choosing to buy?

To evaluate vendors after choosing to buy, check if they're built for your use case, whether it’s simple background removal or scaling production. Photoroom is trained on over a billion e-commerce images, handles edge cases like jewelry and glass, and provides SOC 2 certification with 99.9% uptime. Generic image tools won't deliver the same quality at scale.

4. What are the risks of switching from build to buy mid-project?

The risk of switching from build to buy mid-project is wasted engineering time on the build. But continuing a failing internal solution usually costs more than switching. Migration to Photoroom's API typically takes days to weeks, and teams switch because their build can't handle scale or consistency.