When a scaleup reaches the point where customer growth and marketing complexity start to outpace spreadsheets and single-person operations, the question of martech architecture becomes inevitable: do you centralize on an all-in-one platform, or stitch together a best-of-breed stack? I’ve helped teams make this call enough times to know there’s no one-size-fits-all answer — but there are reproducible trade-offs and a framework you can use to make a decision that aligns with your growth, team and technical capacity.
Why this feels like a binary choice — but isn’t
Most founders and CMOs frame the decision as “one platform vs lots of tools.” That binary thinking is useful for conversation, but it hides nuance. All-in-one platforms like HubSpot, Salesforce Marketing Cloud and Adobe Experience Cloud promise integrated data, fewer vendors and simpler onboarding. Best-of-breed approaches — think Segment + Braze + Amplitude + Iterable + Posthog — promise flexibility, specialist features and the ability to iterate quickly on point problems.
In practice, teams often end up in a hybrid middle ground: a dominant platform for CRM/marketing automation, augmented by specialised tools where the platform’s capability is weak or missing. The trade-offs you should care about are: speed-to-value, operational complexity, vendor lock-in, cost predictability, data fidelity, and the ability to innovate.
Trade-offs: what you gain and what you give up
- Speed-to-value vs precision: All-in-one gets you up and running fast. With HubSpot or Salesforce bundles you can centralise contacts, email, landing pages and reporting quickly — useful when you need predictable campaigns tomorrow. Best-of-breed requires more wiring (events, identity, data models) but gives you precise control over each capability.
- Operational overhead vs specialised outcomes: Fewer vendors = fewer contracts, simpler permissions and a single support channel. But specialist tools often produce better outcomes for niche use-cases (e.g., Braze for sophisticated lifecycle messaging, VWO/Optimizely for experimentation, or Looker/Mode for bespoke analytics).
- Vendor lock-in vs modular agility: All-in-ones can own your data model and inhibit migration. Moving off a consolidated platform is expensive. A best-of-breed stack designed around open data standards (e.g., CDPs + data warehouses) keeps migration paths open.
- Cost predictability vs escalating complexity: An all-in-one licence can look cheaper initially. But as you scale contacts, API calls and feature usage, costs can spike. Best-of-breed costs can be more transparent per tool, but aggregate costs — and engineering time — can climb if you’re not proactive.
- Time-to-hire vs engineering dependency: Smaller teams can operate an all-in-one without heavy engineering support. Best-of-breed very often requires engineers or a central data team to instrument events, maintain pipelines and ensure data quality.
A practical decision framework I use with scaleups
When advising teams I run them through three lenses: Business priority, Data gravity, and Organisational capability. Work through these and you’ll move from debate to decision.
- Business priority: What must the martech stack deliver in the next 6–18 months? If your priority is reducing CAC via predictable paid acquisition and simple onboarding funnels, an all-in-one might be fastest. If the priority is personalised multi-channel lifecycle journeys that drive retention, best-of-breed will likely win.
- Data gravity: Where does your source-of-truth data live and how large/complex is it? If you already have a data warehouse and analytics maturity, it’s easier to adopt best-of-breed and orchestrate via a CDP (Segment, RudderStack) or a streaming layer. If data is fragmented and you lack a warehouse, an all-in-one with built-in reporting can be a sensible interim step.
- Organisational capability: Do you have engineers and a data team to maintain integrations, event schemas and pipelines? If not, prioritise platforms that minimise engineering lift. If yes, you can safely pursue high-performance specialist tools.
Technology patterns that reduce the risk of each approach
There are practical architectures that hedge your bets.
- All-in-one with extension points: Choose platforms that are explicitly designed to integrate — HubSpot has an app marketplace, Salesforce has strong APIs, and Adobe has Experience Platform. Treat the all-in-one as core CRM/operational layer and plug in specialist tools for experimentation, analytics or creative workflows. Keep an exported copy of your schema and a migration playbook.
- Best-of-breed anchored by a CDP/warehouse: Use a customer data platform (Segment, RudderStack) or event stream to centralise events, and mirror everything into a warehouse (BigQuery, Snowflake). From there, connect downstream tools. This gives you single source of truth and reduces duplicated instrumentation.
- Layered approach: Adopt a “core + microservices” model: CRM and email as core, analytics and personalisation as microservices. This reduces complexity in day-to-day ops while keeping specialist capability accessible.
Cost modelling — the spreadsheet you should build
Everyone talks about license cost but forgets two big line items. Build a spreadsheet with these rows:
- Platform licenses (current and projected growth of contacts/MAU/API calls)
- Implementation & onboarding (one-off professional services)
- Ongoing engineering hours for integrations and maintenance (FTE or contractor cost)
- Opportunity cost of lost feature velocity (time to launch new campaigns) — estimate in weeks and multiply by your campaign value
- Migration cost and risk (if deciding to switch later)
When I run this for clients, the most surprising insight is often the engineering line. Best-of-breed stacks can look cheap on license costs but require sustained engineering investment, which is where total cost of ownership often concentrates.
Signals you’re on the wrong path (and how to course-correct)
Watch for these early warning signs:
- Slow campaign cycles — if it takes weeks to launch a campaign because of integration work, you need to simplify.
- Data mismatch across reports — inconsistent active users, revenue or conversion metrics indicate brittle or duplicated pipelines.
- Tool fatigue — too many point tools with overlapping features is a sign you haven’t clearly defined core vs edge capabilities.
- Ballooning support tickets and escalating vendor disputes — operational complexity is killing velocity.
Course corrections:
- If you’re stuck with an underpowered all-in-one, extract key datasets into a warehouse and start a parallel analytics pipeline to regain control.
- If your best-of-breed stack is hard to manage, rationalise: decommission tools with low ROI, document the event schema, and invest in automation for onboarding new tools.
Real-world examples (short)
I’ve seen a fintech scaleup choose Salesforce as the CRM and use Braze for messaging because they needed advanced lifecycle orchestration tied to payment events. The trade-off was engineering time to sync events and a higher license bill — but retention improved enough to justify it.
Conversely, a B2B SaaS startup I advised used HubSpot for the first 18 months to centralise contacts and automate inbound. When ARR and contact counts grew, they exported historical data into BigQuery and gradually replaced the analytics and experimentation layer with Looker and GrowthBook, keeping HubSpot for CRM and sales processes — a pragmatic hybrid move.
Recommended next steps for scaleups
- Audit your current stack: list tools, owners, costs and integrations.
- Map the top three business outcomes your martech must deliver in 12 months.
- Run the cost model above and quantify engineering effort required for both approaches.
- Choose an architecture pattern (core+extensions, CDP+warehouse, or single-platform) and document a 6–12 month roadmap that includes clear exit/migration plans.
Deciding between all-in-one and best-of-breed is less about picking a tribe and more about matching technology to the stage, data posture and capabilities of your business. If you plan for migration risk, quantify engineering costs and prioritise a single source of truth for customer data, you’ll preserve optionality while still moving fast.
