TL;DR: That "three-month MVP" your team estimated will take 12+ months to reach production quality, require ongoing maintenance from a dedicated team, and still lag behind the state of the art. Here's why buying infrastructure beats building it—even for technically sophisticated organisations.

This question becomes urgent when you understand that we're in the middle of a major platform shift where early movers capture disproportionate value—but only if they move decisively during the window of opportunity.

The Seductive MVP Trap

Every technical leader has been here: Marketing comes with a compelling use case for conversational commerce. You run the numbers, scope an MVP, and think "we can build this."

Initial estimate: Three months, two engineers, leverage our existing stack.

Reality: Twelve months, five engineers, ongoing maintenance overhead, and you're still catching up to capabilities that were available off the shelf.

This isn't a hypothetical. Large-scale ecommerce platforms consistently underestimate the complexity of production-grade conversational commerce infrastructure. Even organisations with world-class engineering talent and massive resources find that maintaining conversational commerce requires significant ongoing investment.

The gap between what seems achievable in a planning session and what production quality actually demands is where most build-it-yourself initiatives falter. This is partly because most teams don't fully understand the difference between chatbots and real conversational UI infrastructure—they scope for the former while needing the latter.

What "Production-Grade" Actually Means

Let's be specific about the gap between MVP and production-ready conversational commerce:

Your MVP Scope (3-4 months):

Basic intent classification
Simple product search
Rule-based responses
Integration with product API
MVP UI implementation

Production Requirements (12-18 months):

Multi-intent understanding (customers rarely ask one thing at a time)
Context management (maintaining conversation state across sessions)
Vector search optimisation (finding products from natural language)
Hallucination prevention (ensuring responses are factually accurate)
Guardrails implementation (preventing harmful or off-brand responses)
Prompt engineering (continuously refining to improve outcomes)
Model management (evaluating, switching, and optimising LLMs)
Analytics infrastructure (understanding what's working and what's not)
A/B testing framework (measuring impact and iterating)
Security and compliance (GDPR, data processing, audit trails)
Integration layer (ecommerce platform, CRM, analytics, support ticketing)
Performance optimisation (sub-second response times under load)
Fallback mechanisms (graceful degradation when systems fail)
Monitoring and alerting (knowing when something breaks)
Model versioning (managing updates without breaking production)

Each of these isn't a feature—it's an entire workstream requiring specialised expertise.

The Hidden Maintenance Iceberg

Even if you successfully build the initial system, the real cost is maintenance:

Model Updates

GPT-4 turbo to Gemini 2.5 Flash to Pro to Claude Sonnet 4.5 to whatever comes next. Models improve constantly, but they also change behaviour. Every update requires:

Regression testing across your entire conversational flow
Prompt re-engineering to maintain quality
Cost-benefit analysis (newer isn't always better)
Migration planning to avoid customer-facing disruption

Prompt Engineering

This is not a "set it and forget it" task. As your catalogue changes, as customer language evolves, as new use cases emerge, your prompts need continuous refinement. You'll need:

Someone monitoring conversation quality daily
A/B testing different prompt strategies
Analysis of failure modes and edge cases
Systematic improvement processes

Context Engineering

Understanding customer intent requires more than just the current message. You need:

User behaviour tracking (what they've viewed, clicked, searched)
Session context (how they arrived, what device, time of day)
Historical context (past purchases, preferences, support tickets)
Real-time inventory and pricing data
Continuously optimised context windows to balance cost and quality

Vector Search Optimisation

Finding the right products from natural language queries requires sophisticated vector embeddings:

Regular re-indexing of your product catalogue
Embedding model updates and evaluation
Query understanding improvements
Relevance tuning based on conversion data
Performance optimisation as catalogue grows

Guardrails and Safety

Preventing your AI from saying something harmful, inaccurate, or off-brand requires:

Ongoing monitoring of conversations
Systematic identification of failure modes
Implementation of safety mechanisms
Regular updates as new edge cases emerge
Compliance with evolving regulations (especially in EU)

The Composable Architecture Advantage

Modern conversational commerce infrastructure is built on headless, composable principles. This means:

Your ecommerce backend stays yours:

Shopify, Salesforce Commerce Cloud, Centra, Magento—whatever you're using
No migration required, no vendor lock-in
Your data model, your business logic

Payments stay yours:

Stripe, Adyen, whatever processor you prefer
PCI compliance remains where it is
Transaction data under your control

Analytics stay yours:

Your existing stack (Google Analytics, Segment, Amplitude)
Same tracking, same dashboards
No disruption to reporting

CRM stays yours:

Customer data remains in your systems
Integration via standard APIs
GDPR compliance maintained

What you're adding is the intelligence layer—the conversational interface, intent understanding, context management, and orchestration that makes the experience work.

Bring Your Own Model (BYOM): True Flexibility

One of the most common concerns about using platform infrastructure: "We'll be locked into whatever model you choose."

Not with modern architecture. Proper conversational commerce infrastructure supports:

Model flexibility: Use GPT-4, Claude, Gemini, or your own fine-tuned models
Data ownership: All conversational data belongs to you, not the platform
Infrastructure ownership: Host on your own cloud if required (AWS, GCP, Azure)
Agent-to-agent protocols: Participate in emerging standards (Agentic Commerce Protocol with OpenAI/Stripe)

This matters for two reasons:

Cost optimisation: You're not locked into one vendor's pricing model
Capability evolution: As models improve, you can switch without rebuilding infrastructure

The MCP Integration Layer: Future-Proofing Your Stack

Model Context Protocol (MCP) represents the next evolution of AI tool integration. Rather than building hardcoded integrations to every system you need, MCP provides:

Dynamic tool access: AI agents can discover and use capabilities on demand
Standardised interfaces: Connect to any MCP-compatible service
Extensibility: Add new capabilities without rewriting core infrastructure
Interoperability: Works across different LLM providers

If you build in-house without MCP support, you're creating technical debt from day one. Every new integration becomes a custom project. Every new capability requires engineering time.

Platform infrastructure built on MCP gives you access to an expanding ecosystem of capabilities without additional development work.

Data Pipelines: The Often-Forgotten Complexity

Your conversational UI needs real-time access to:

Product catalogue (with all variants, specifications, inventory)
Pricing (including promotions, customer-specific pricing, currency conversion)
Inventory (real-time stock levels across warehouses)
Customer data (purchase history, preferences, support tickets)
Order status (shipping, tracking, returns)
Content (blog posts, guides, FAQs for recommendations)

Each of these requires reliable, performant data pipelines with:

Change detection and incremental updates
Error handling and retry logic
Validation and transformation
Monitoring and alerting
Performance optimisation

Building these pipelines is straightforward but time-consuming. More importantly, maintaining them as your systems evolve is where the real cost lives. Your product data model changes? Your pipelines break. New inventory system? Rebuild integration. Pricing logic updates? Hope your pipeline handles it.

Platform infrastructure handles this with pre-built connectors and transformation logic that's been battle-tested across dozens of clients.

The Security and Compliance Minefield

If you're operating in the EU (or selling to EU customers), GDPR compliance isn't optional. Conversational commerce adds complexity:

Data processing agreements: Who's processing customer data and how?
Right to deletion: Can you actually delete conversation history from your vectors?
Data minimisation: Are you collecting more than necessary?
Audit trails: Can you demonstrate compliance?
Cross-border transfers: If using US-based models, how are you handling data sovereignty?

Platform providers handle this as data processors, taking on regulatory burden. When you build in-house, all of this falls on your team.

Same applies to security:

Prompt injection attacks
Data leakage through context windows
API abuse and rate limiting
Model inversion attacks
Adversarial inputs

These aren't theoretical concerns—they're active attack vectors that require constant vigilance.

Agent-to-Agent Protocols: The Next Platform Shift

Agentic Commerce Protocol (ACP), co-developed by OpenAI and Stripe and released in September 2025, represents the next phase of AI shopping. When AI assistants like ChatGPT can transact directly with merchant systems:

Customers shop conversationally through AI agents
Agents discover products, compare options, complete purchases
Merchants need to expose their capabilities through standard protocols

If you've built custom conversational infrastructure, adding ACP support means another major development project. Platform infrastructure providers are implementing this as a core capability, giving you instant compatibility when the ecosystem goes live.

This matters because AI shopping agents are already here. Perplexity's "Buy with Pro" launched in November 2024, ChatGPT shopping is live, Amazon's Rufus is operational—these aren't future concepts. They're live, growing fast (Adobe reports traffic doubling every two months), and will become a primary shopping channel.

Brands without conversational infrastructure won't be discoverable. Those with custom-built systems will need to rebuild to support agent protocols. Platform infrastructure gives you both today.

The Real Build vs Buy Calculation

Let's run the actual numbers:

Building In-House:

Initial development: 12-18 months, 5 engineers (€500K-750K)
Ongoing maintenance: 3 engineers dedicated (€300K annually)
Model costs: Direct provider costs (~€2-5K monthly depending on volume)
Opportunity cost: What else could your team build with that capacity?
Technical debt: Every custom decision becomes maintenance burden
Lag time: Always 6-12 months behind state of the art

Total first-year cost: €800K-1M

Ongoing annual cost: €350K+

Platform Infrastructure:

Initial integration: 2-4 weeks, minimal engineering time
Monthly platform cost: €99-2,000/month depending on volume
Ongoing maintenance: Handled by platform provider
Model costs: Transparent, often optimised through platform
Updates and improvements: Automatic, no engineering required
Time to market: Weeks, not quarters

Total first-year cost: €1,200-24,000

Ongoing annual cost: €1,200-24,000

The cost differential is 30-50x, even before considering opportunity cost.

When Building Makes Sense (Rarely)

There are legitimate scenarios where building in-house is the right choice:

You have enterprise-scale resources comparable to major ecommerce platforms with dedicated teams
Conversational commerce is your core differentiator (not for most brands)
Regulatory constraints prevent using external infrastructure (rare, but exists)
Highly custom requirements that no platform can support (what are they?)

For 95% of ecommerce brands, these don't apply. You're better served by platform infrastructure that lets you focus on what actually differentiates your business: product selection, brand experience, customer relationships, marketing effectiveness.

The Headless Advantage: Control Without Building

Modern conversational commerce platforms operate as headless services:

JavaScript widget loads your UI
Backend handles all intelligence and orchestration
You control the visual experience, branding, positioning
Platform handles the complexity

This gives you the best of both worlds:

Control: Your brand, your design, your experience
Flexibility: Integrate with your existing stack
Speed: Deploy in weeks, not months
Evolution: Benefit from continuous platform improvements

Making the Strategic Decision

The question isn't "can we build this?" The question is "should we?"

Your engineering team is capable of building conversational commerce infrastructure. But capability doesn't equal strategic wisdom. What's the opportunity cost? What else could they accomplish with that time?

More importantly: what happens when they move on? The engineer who built your custom conversational system won't be there forever. When they leave, the institutional knowledge leaves with them. Platform infrastructure survives people changes.

Starting Today, Not Next Quarter

The AI shopping revolution is happening now. Adobe reports traffic from AI sources grew 1,200% between July 2024 and February 2025, doubling every two months. Capgemini's research shows 58% of consumers globally are already using AI tools for product recommendations. Brands without conversational capabilities are becoming invisible.

If you commit to building in-house today, you're looking at:

Q1 2026: MVP ready for internal testing
Q2 2026: Production deployment
Q3-Q4 2026: Working out the kinks and building missing features
2027: Finally competitive with 2025 platform capabilities

Platform infrastructure gets you live in weeks with capabilities that are production-tested across dozens of brands.

The Bottom Line

Building conversational commerce infrastructure in-house is possible. It's also expensive, slow, and strategically questionable for most organisations.

The real question isn't build vs buy—it's where your engineering team creates the most value. Is it maintaining conversational infrastructure, or is it building features that actually differentiate your business?

Platform infrastructure gives you headless composability, model flexibility, data ownership, and continuous evolution without the maintenance burden. You get to market faster, stay current with minimal effort, and focus your engineering talent on what matters.

The technology landscape is moving fast. The question is whether you'll be ready when AI agents become the primary shopping interface.

Ready to evaluate platform infrastructure vs building?

Book a technical demo to see architecture, integration patterns, and deployment options.