1. Introduction and context
Many SME organisations have been running for years on an ERP that is "good enough" for the core processes. The comparison between an existing ERP such as Cubus and a platform like Odoo usually does not arise from curiosity, but from pressure: growth in volumes, more locations, new activities or higher requirements for integrations and reporting. The environment is also changing: customers expect faster delivery times and more transparency, finance wants to close faster, and IT wants less customisation and more control over security and data.
This blog helps with a decision-supporting comparison. Not from the angle of "which system is better", but "which system fits your risks, growth path and organisational capacity".
The questions differ per target group. Management looks at strategic agility, dependence on suppliers and total cost of ownership (TCO). Operations looks at process fit, efficiency and the extent to which exceptions can be supported without workarounds. IT looks at architecture, integrations, data sovereignty, security and manageability.
The scope is deliberately limited to typical ERP core processes (order-to-cash, purchasing, inventory), project/hours/work orders and the way finance and reporting needs are filled in via integrations. Deep industry-specific layers such as advanced production planning (APS), shopfloor/MES or very specific compliance modules are outside this comparison; these often require separate products or specialist add-ons, regardless of the ERP.
As a summary we use five decision criteria: functional coverage (now and over 2–3 years), extensibility (modules/customisation/partners), data and AI capabilities (from BI to automation), costs (one-time and recurring) and the implementation risk including vendor lock-in.
2. Type of ERP and starting point of existing ERP system versus Odoo
Cubus positions itself as an ERP solution for the Dutch SME with attention to sectors such as wholesale, construction, services and manufacturing. In the available public information, the emphasis is on recognisable SME processes: quotes, orders, invoicing, purchasing, inventory and project administration. The Dutch context is an important starting point: language, processes and the partner landscape align with the local market.
Odoo is a modular ERP suite that is more broadly deployable: from SME to larger organisations, often with an international footprint. The core is comparable (sales, purchasing, inventory, finance), but Odoo is often chosen because of the breadth of modules and the ecosystem: expansion towards CRM, marketing, e-commerce, field service or helpdesk can take place in the same suite, depending on the chosen edition and implementation.
If you put the typical use cases side by side, there is overlap. Cubus is often found suitable for trading companies (purchasing/sales/inventory/logistics), project-driven organisations (hours/work orders) and manufacturing, with a usage range that is publicly mentioned of approximately 1 to 250+ users. Odoo serves comparable process profiles, but in practice is also set up for multi-company and multi-country scenarios, which can make it more attractive if you want to standardise multiple entities or scale internationally.
Deployment and ownership differ in starting point. Cubus profiles itself publicly as "cloud". About on-premise or self-hosting, few concrete indications are found in the consulted sources. For Odoo, freedom of choice is usually greater: cloud variants exist, but in many cases self-hosting/on-premise is also possible. This affects data sovereignty, security choices, integration architecture and exit scenarios.
The implementation approach also often differs. With Cubus, the emphasis is on supplier/partner-driven configuration and connecting integrations. With Odoo, the emphasis is on configuration plus module choice: you can standardise a lot, but there is also room for customisation via the platform and via modules from the ecosystem. The latter increases flexibility, but can also require more design choices and governance.
3. Where existing ERP system (Cubus) is stronger
A strong point of Cubus is the recognisable fit for Dutch SME with common processes. Organisations that mainly need "good ERP" for order-to-cash, basic inventory, purchasing and a clear invoicing flow can often quickly extract value from a system that is explicitly set up for that. The functional scope is concrete in public statements and aimed at daily operations in trade, project and (basic) production.
For project organisations, the integrated project administration is an important advantage. If project management, time registration and recharging are closely linked to purchasing and sales processes, you reduce the chance of separate Excel flows and double entry. In practice, this is about things like: hours registration linked to projects, booking materials on work orders, invoicing based on terms or post-calculation and margin reporting per project. A solution that supports this "out-of-the-box" in one process chain reduces the implementation and adoption risk.
Cubus can also deliver value quickly with a relatively standard SME scope, precisely because the number of design choices is often more limited. Less choice sounds negative, but it can also mean: faster decisions, fewer process variants and less customisation. If your organisation is mainly looking for stability and predictability, this can be a rational choice.
Reporting and dashboards are mentioned as a core promise, including real-time reporting. For many SME companies, that is the practical floor: insight into order portfolio, inventory position, work in progress, project margins and DSO. The explicit Power BI link is relevant because Power BI is often already present in the Netherlands. With this you can follow a familiar BI path: ERP as source, Power BI as visualisation layer with standard governance in Microsoft environments.
Finally, standard integrations within the Dutch ecosystem are a plus. Public sources mention integrations with, among others, Exact, Twinfield, Office 365 and Mailchimp, often via partners/connectors. For organisations that keep their financial administration (partly) outside ERP or are already dependent on specific local tools, this can be a pragmatic route: ERP connects to what is already there, without complete platform thinking.
4. Where Odoo is stronger
Odoo distinguishes itself mainly in breadth: it is set up as an end-to-end suite with many modules around the ERP heart. That means that expansion to adjacent domains can often take place within the same platform, instead of via separate applications. Practically, that translates to scenarios such as: one customer view in CRM that flows through to quote, order, delivery and invoice; service processes that are linked to sold products; or e-commerce that directly uses inventory and pricing.
Extensibility via ecosystem and marketplace is a second distinction. Where with some ERPs you mainly extend via integrations or supplier customisation, Odoo can extend via existing third-party modules. That can accelerate niche functionality (for example specific logistics flows, document templates or sector add-ons), without you having to adapt the "core". The trade-off is that you need governance: quality, maintenance and compatibility of modules differ per supplier and per version.
Platform choices are often a strategic argument. With Odoo there is in many situations a choice between cloud and (self-)hosting/on-prem. That choice is not only technical: it affects negotiability about hosting location, logging, security controls, backup strategy and exit scenario. If data sovereignty, control over encryption keys or integration into an existing private cloud is important, that freedom of choice can weigh heavily. At the same time, self-hosting also means: more responsibility and competence needed for management and patching.
Internationalisation and scalability are often more practically arranged. For organisations with multiple entities, multiple languages, multiple currencies or country processes, Odoo can be attractive. Multi-company setup can help to harmonise processes across locations, while local deviations remain possible. The uncertainty here lies in implementation: success depends on the process design (what do you standardise, what do you leave local) and the discipline to manage master data centrally.
Finally, standard API and integration patterns play a role. Odoo is often deployed in architectures where ERP is one of the core systems, but not the only one: think of a data warehouse, iPaaS, EDI, webshop platforms or planning tools. API-first capabilities and a broad partner landscape can make integrations more scalable, provided you make integral design rather than stacking many point-to-point connections.
5. Comparison
In positioning you can summarise it as follows: Cubus focuses strongly on Dutch SME with industry focus and recognisable processes; Odoo focuses on a broader segment, is more international and leans on a modular ecosystem. That does not directly say something about "better", but about the assumptions: with Cubus, the chance is greater that you step into a predefined process mould; with Odoo, the chance is greater that you assemble a platform and therefore have to make more choices.
Functionally per domain there are some points of attention to test concretely.
Sales/order/inventory. Both solutions cover the basics of quotes, orders, deliveries and invoicing. The comparison gets interesting in variants: multiple warehouses, reservation logic, dropshipment, returns, consignment, serial batches, or customer-specific pricing agreements. Cubus explicitly names warehouse management and multi-warehouse, which is sufficient for many SME scenarios. Odoo can handle many logistics variants, but the exact fit depends on configuration (for example picking strategies) and possibly extra modules. A practical approach is to inventory the top-10 exceptions in your operations and test those as fit-gaps, not just the "happy flow".
Project/hours/work orders. Cubus publicly emphasises integrated project administration with time registration and link to purchasing/sales. That is relevant for organisations where work in progress, post-calculation and recharging are leading. Odoo can also support this, but the quality of the fit depends on chosen modules, setup of approvals and the way you book costs and revenues (project-to-cash). There is a trade-off here: Odoo can integrate more broadly with service, planning and CRM, but may require more design effort to secure the same "simple" project process.
Document management/workflows/roles. Cubus mentions document management, workflows and roles/rights. For decision-making, what is mainly important is: how auditable is the process, how granular are authorisations, and how well can you support segregation of duties (SoD)? Odoo offers comparable building blocks, but the setup is decisive: with many modules and customisation, the importance of an unambiguous authorisation model, logging and lifecycle management of documents grows. In both cases: governance is not "a feature", but an implementation choice.
Integrations (ERP as hub). Cubus mentions several known Dutch integrations such as Exact, Twinfield, Office 365 and Mailchimp, often via partners. This can be an advantage if you want to quickly connect to existing tooling with a predictable implementation. Odoo generally has a broader integration landscape, partly due to the ecosystem. The risk shifts: less dependent on one connector, but more dependent on choice and management of multiple integration components. The question is not only "can it integrate", but "how manageable does the landscape remain over five years".
Data & reporting. Cubus emphasises real-time reporting and dashboards, plus an explicit Power BI link. Odoo has its own reporting capabilities and can connect well to BI, but maturity depends strongly on your data setup: are you going to report directly on the transaction database, or build a data warehouse (DWH) with standardised definitions? Direct reporting is faster and cheaper, but less robust for complex analyses (historisation, performance, unambiguous KPI definitions). A DWH is more expensive, but supports more reliable management information and AI applications.
Strategic fit. Cubus often fits well when process standardisation within a Dutch SME context weighs more heavily than platform flexibility, and when your organisation is mainly looking for operational stability with limited IT load. Odoo often fits better when platform strategy, growth, international scope and extensibility weigh more heavily, and when you are willing to make more choices in architecture and governance.
6. AI and Integration
AI is a container term in the ERP world. For decision-making, it is useful to bring AI maturity back to concrete applications: automation of classifications (e.g. invoice coding), predictions (demand/inventory), assistance (search and writing support) and anomaly detection (deviations in margins or bookings). Based on public signals, Cubus is mainly strong in BI/reporting; there are no clear indications for built-in AI features such as forecasting or copilots. That does not mean AI is impossible, but you probably need external tooling or a data platform for it.
With Odoo, AI is more dependent on version, hosting model and additional tooling. In practice, AI applications often arise via three routes: (1) standard features that are added in the suite, (2) modules from the ecosystem and (3) integration with external AI services. That offers possibilities, but also introduces questions: where is the data, which prompts/logs are stored, and how do you ensure that company data does not unwantedly end up outside your control?
The data foundation is a precondition, separate from the software choice. AI initiatives rarely fail at model level, but often at data quality and process discipline. Concretely: are customer and article master data unambiguous, are projects consistently set up, are statuses reliable, are exceptions correctly booked? Master data governance, validation rules and logging determine whether analyses and AI outcomes are reliable. A system that enforces processes helps, but also requires change management.
For integration architecture there are roughly two flavours: point-to-point integrations (fast, but stacks complexity) versus an integration layer such as iPaaS/ESB (more design, but better manageable). In addition, there is the choice between batch (e.g. nightly synchronisation) and event-driven integration (near real-time via queues/webhooks). The choice criteria are mainly: complexity of the landscape, need for real-time data, error handling, auditability and management capacity. An SME organisation with limited IT capacity can better steer on simplicity and standard connectors; growth and multi-entity make an integration layer more profitable sooner.
BI/analytics setup follows. The Power BI route is explicit at Cubus and can be practical: define KPIs, build datasets, and ensure a data model that finance and operations share. With Odoo, Power BI is often also feasible via connectors or ETL, but the design is decisive: are you going for a quick set of operational dashboards or are you building a DWH for unambiguous definitions, historisation and performance? That choice also affects AI: forecasting and anomaly detection work better on a standardised, historised dataset than on live transaction tables.
Security, data sovereignty and compliance deserve explicit attention. For Cubus there is an indication of EU hosting (Frankfurt) in the available documentation, but public details about tenant choice, sub-processors, backups, export options and exit procedures are limited. For Odoo this depends on hosting choice: cloud can be fast and compliant, but requires contractual clarity; self-hosting gives control over data residency and technical measures, but shifts responsibilities to the organisation or management partner. In both cases, it is advisable to do a short due diligence on: processor agreement, sub-processor list, logging, encryption, backup/restore tests and data portability (export formats, lead times, costs).
10. Costs and impact of a switch
The costs of ERP choices are rarely only in licences. For an honest decision, a TCO structure is needed with both one-time and recurring components.
One-time costs typically consist of implementation (process design, configuration, testing), integrations (build or rebuild of integrations), data migration (extract, mapping, cleaning, test migrations), training and possibly temporary double staffing or external support. With Odoo, additional costs may come if you add many modules or develop customisation. With Cubus, costs may mainly lie in configuration and connecting integrations, depending on how standard your processes are.
Recurring costs consist of licences/subscriptions, hosting (if not included), management/maintenance (functional management, technical management, updates), further development and support. With an ecosystem-driven solution, you must also take into account maintenance of modules and compatibility on upgrades. With a more standardised suite, the recurring management list can be smaller, but the dependence on supplier/partner greater.
Organisational impact is often the largest hidden cost item. A switch means redesign of workflows, role changes (who does which controls), authorisations and training. In the first months, temporary productivity loss is normal: users learn new screens, definitions change, and exceptions surface. The size of that impact depends on process differences and the extent to which you enforce standardisation. Odoo implementations with much freedom of choice can require more change; Cubus can land faster if it better fits existing ways of working.
Expected ROI you must explicitly make in measurable effects, otherwise it remains a feeling. Typical ROI drivers are: less manual administration (hours, invoices, inventory corrections), shorter lead times (order processing, project invoicing), better margin control (project and articles), lower integration and management costs, and fewer errors. Set those off against total migration costs and the realistic time to stabilisation (often 6–12 months after go-live, depending on scope).
Migration impact differs per data domain. Master data (articles, customers, suppliers) seems simple, but often requires a lot of cleaning: UoM, VAT codes, price lists, delivery conditions. Orders and inventory levels require a cutover strategy: what do you migrate, what do you close, how do you handle backorders? Project history is often the most difficult: which granularity do you need (hour lines, purchase lines, terms), and how do you reconcile work in progress? Financial history is a choice: only opening balance and open items (cheap) versus full journal history (more expensive, but better for audits and trend analyses).
Process and change impact also touches governance. New workflows mean new approvals, new exception handling and often new KPI definitions. Make clear in advance who is "process owner" per chain and who makes decisions on deviations. Without that governance, scope creep arises: every department wants something extra, causing implementation and costs to rise.
Integration and reporting impact is often underestimated. If there are integrations with Exact/Twinfield/Office 365/Mailchimp or other systems, they must be redesigned, built and tested. Power BI reports are also not "free" to take along: datasets, data models and definitions change. This is also an opportunity to standardise: define KPIs centrally (e.g. revenue, margin, WIP, inventory value) and prevent different dashboards from using different definitions.
Main risks are scope creep, customisation explosion, data quality and a too-short parallel run. Control measures are practical: work with an MVP scope (minimum viable process), phase per process chain, draw up a test strategy with chain tests and reconciliation, and conduct at least one test migration with measurement points (difference lists). Consider a parallel run if finance and inventory are critical, but weigh it against extra burden for the organisation.
For go/no-go it helps to make three scenarios explicit. Scenario 1: optimise within Cubus (process improvement, better reporting, cleaning data, rationalising integrations). Scenario 2: hybrid (keep Cubus as ERP core, supplemented with extra apps for specific needs). Scenario 3: migration to Odoo (platform strategy, consolidation of applications, international harmonisation). A rational decision chooses the scenario that delivers the most value within your change capacity and risk appetite.
11. Conclusion and next steps
The core differences are basically well to identify. Cubus is strongly positioned for Dutch SME with recognisable processes, with emphasis on order, inventory and project administration and a pragmatic integration and reporting route (including via Power BI). Odoo is stronger as a modular platform with broad suite, ecosystem-driven extensibility, internationalisation options and usually more deployment choice, which is relevant for growth, multi-entity and data sovereignty.
A practical decision framework helps to translate this to your situation. Test at least: what are the strategic goals (growth, internationalisation, consolidation), what is the growth plan in entities and volumes, how much IT capacity is available for management and integrations, how complex is the current application landscape and which compliance and data requirements apply (EU hosting, auditability, exit, backup). The outcome is often not black and white, but a preference with preconditions.
Recommended next steps remain most valuable when they are concrete and short-cycle. Start with a process inventory (top-5 chains and top-10 exceptions), followed by a fit-gap workshop with operations, finance and IT. Do a parallel data audit on master data and critical transactions, and an integration scan on all integrations and dependencies. Round this off with a TCO calculation in which you make assumptions explicit (scope, number of users, migration choices, DWH or not).
A Proof of Concept or pilot works best when it is delineated. Choose one chain, for example order-to-cash or project-to-cash, with clear success criteria: lead time, error percentages, number of manual steps, quality of management information and security/compliance requirements. Define measurement points and a short timeline (for example 6–10 weeks) so that you quickly learn where the real fit-gaps are and how much customisation/organisational change is needed.
12. How pantalytics can help with a switch
A switch is mainly a decision under uncertainty: only after design and testing do you know how big the fit-gaps really are. pantalytics can support with an objective fit-gap analysis between Cubus and Odoo, by mapping processes per department and prioritising requirements (must/should/could). This makes explicit where standardisation is possible and where deliberately chosen deviations are needed, including risk assessment on implementation complexity.
On data and migration preparation, pantalytics can help with a data quality scan and a migration plan: mapping of master data, test migrations, reconciliation approach and choices about historical data (how much, in what detail). This reduces the chance that migration problems only become visible just before go-live.
For integration and architecture, pantalytics can design a target architecture, including choice criteria for iPaaS/ETL, API strategy and security-by-design. This is especially relevant if ERP becomes part of a broader data and application strategy, in which BI, DWH and external tools play a structural role.
In implementation governance, a clear structure helps to control scope and risks: sprint planning, test strategy, cutover/parallel-run plan and KPIs for adoption. With this, the implementation remains controllable, even if new wishes arise along the way.
After go-live, aftercare is often decisive for the eventual return. pantalytics can support with monitoring, performance, a roadmap for further development and steps to make reporting/BI more mature, so that the organisation does not just "run", but also remains focused on optimising.