ML6 vs Xebia: Benelux AI Consultancies Compared (2026)
ML6 is a specialized AI/ML engineering boutique (100+ experts, Ghent). Xebia is a full-stack digital consultancy (5,500+ professionals, 28 offices). Honest comparison for enterprises choosing between them, plus when neither model fits.
ML6 is a specialized AI/ML engineering boutique founded in Ghent, Belgium in 2013, with 100+ AI experts, 400+ projects delivered, and Google Cloud Benelux Services Partner of the Year 2024. Xebia is a full-stack digital consultancy founded in 2001 in the Netherlands, with 5,500+ professionals across 28 offices worldwide and Google Cloud Premier Partner status since 2007. ML6 is stronger for bounded, technically deep AI/ML projects on Google Cloud in the Benelux and DACH regions. Xebia is stronger for multi-workstream digital transformation programs requiring coordinated AI, cloud, and software delivery at scale.
Both bill by the day. This is an honest comparison of both, followed by an honest question: whether either model is the right structure for your specific problem.
ML6 vs Xebia: overview
| Dimension | ML6 | Xebia |
|---|---|---|
| Focus | Pure AI/ML engineering boutique | Full-stack digital consultancy (AI is one practice) |
| Size | 100+ AI experts | 5,500+ professionals |
| Founded | 2013, Ghent, Belgium | 2001, Netherlands |
| Offices | Ghent, Amsterdam, Berlin, Munich | 28 offices, 16 countries |
| Core expertise | ML engineering, computer vision, NLP, predictive models, MLOps | AI/ML + cloud + data engineering + software dev + agile transformation |
| Cloud partnerships | Google Cloud Benelux Services Partner of the Year (2023, 2024); OpenAI Services Partner | Google Cloud Premier Partner since 2007; Google Cloud Sales Partner of the Year Benelux (2024); Microsoft Solutions Partner |
| Analyst recognition | 4x Deloitte Technology Fast 50 Belgium; FT1000 fastest-growing European companies | Everest Group Leader, Data & AI Services (2025); Avasant GenAI Services RadarView Disruptor |
| Typical engagement | Single AI/ML project, bounded scope | Multi-workstream digital transformation |
| Team you get | Specialized ML engineers and data scientists | Mix of AI engineers, cloud architects, developers, agile coaches |
| Client profile | Enterprises with a specific, well-defined ML challenge | Enterprises running coordinated digital transformation across disciplines |
| Geographic strength | Benelux, DACH | Benelux, global (with nearshore delivery in India, Poland, Vietnam) |
| Notable clients | Randstad, ASML, Pfizer, P&G | Philips, Ahold Delhaize, Tesco, ING |
| Time to production | 3–12 months depending on complexity | 8–16 weeks for AI pilots; longer for multi-workstream programs |
| Pricing model | Day rates, project-based | Day rates, engagement-based |
| Platform ambitions | Unum (Enterprise Superintelligence platform, launched 2025) | Managed AI services; primarily services-led |
ML6 at a glance
ML6 is an AI engineering boutique founded in Ghent, Belgium in 2013. With 100+ AI specialists and 400+ projects across 150+ organizations, their work spans manufacturing, life sciences, retail, media, and the public sector. Reference clients include Randstad, ASML, Pfizer, and P&G.
Their Google Cloud partnership is deep and long-standing. ML6 won Google Cloud Benelux Services Partner of the Year in both 2023 and 2024 — consecutive awards that reflect genuine technical capability on Vertex AI, BigQuery, and GKE, not just a commercial relationship. They are also an OpenAI Services Partner. Four consecutive Deloitte Technology Fast 50 Belgium awards confirm sustained commercial growth alongside the technical work.
ML6's approach is project-scoped: a defined challenge, a dedicated team of ML engineers and data scientists, a delivery timeline. They helped ASML shorten calibration release cycles from monthly to biweekly. They helped Randstad raise predictive sales hit rates from 25% to 70%.
In 2025, ML6 launched Unum — an Enterprise Superintelligence platform designed to turn enterprises into what they describe as "living, learning entities" through coordinated AI agents. Unum represents ML6's move from pure services toward a product-led model, though the platform is still early stage relative to their established consulting practice.
Xebia at a glance
Xebia is a full-stack digital consultancy founded in the Netherlands in 2001. With 5,500+ professionals across 28 offices in 16 countries, they span AI/ML, cloud, data engineering, custom software, and agile transformation. Clients include Philips, Ahold Delhaize, Tesco, and ING.
Xebia has held Google Cloud Premier Partner status since 2007, winning Google Cloud Sales Partner of the Year for Benelux in 2024. They strengthened their Google Cloud alliance in January 2025 with a new multi-year strategic partnership focused on agentic AI and generative AI workloads. They are also a Microsoft Solutions Partner and a recognized Databricks Benelux Top Growth Regional Partner.
Analyst coverage is strong: Everest Group named Xebia a Leader and Star Performer in Data & AI Services for mid-market enterprises (2025). Avasant named them a Disruptor in their Generative AI Services RadarView.
Xebia has grown significantly through acquisition. They acquired Google Cloud Premier Partner g-company, expanding their cloud and AI practice. Their nearshore delivery centers in India, Poland, and Vietnam give them flexible staffing capacity that a boutique like ML6 cannot match.
Where ML6 handles the AI piece, Xebia is structured to coordinate multiple workstreams simultaneously — AI, cloud, data, application development, and organizational change management — under one engagement.
Where ML6 is the stronger choice
You have a specific, bounded ML engineering challenge. A computer vision model. A predictive maintenance algorithm. An NLP pipeline for contract analysis. ML6's entire practice is built around problems like these. You get dedicated ML engineers, not generalists assigned from a larger bench. For technically deep, well-defined AI work, that focus translates into faster delivery and sharper solutions.
Your project runs on Google Cloud. If your AI initiative is built on Vertex AI, BigQuery, or GKE, ML6's depth in that ecosystem is a genuine asset. Winning the Benelux Google Cloud Services Partner of the Year in back-to-back years reflects a level of certification, architecture experience, and Google relationship that matters for projects where that infrastructure is central.
You want the highest concentration of AI expertise per person. ML6's 100+ people are all AI specialists. At Xebia, the team serving your AI project works within a much larger organisation that also handles cloud infrastructure, agile transformation, and application development. For projects that are purely AI/ML, ML6's specialist-to-generalist ratio is more favorable.
You are based in Benelux or DACH. ML6's offices in Ghent, Amsterdam, Berlin, and Munich mean they can be on-site, they speak the local language in both Dutch and German markets, and they have established enterprise relationships in both regions. For Benelux enterprises in particular, ML6 is one of the most referenced names precisely because they have been doing this work locally for over a decade.
Where Xebia is the stronger choice
Your AI initiative is one part of a broader digital transformation. If the program involves cloud migration, data platform modernization, application development, and agile coaching alongside AI, Xebia can staff and coordinate the full program. ML6 covers the AI component only. Managing ML6 plus separate vendors for cloud, software, and organizational change adds coordination overhead that scales with program complexity.
You need scale or multiple parallel workstreams. With 5,500+ professionals, Xebia can staff large teams quickly and run multiple workstreams simultaneously. ML6's focused team is well-suited to bounded engagements but not to enterprise-wide programs that require dozens of engineers across disciplines at once.
You need nearshore delivery. Xebia's delivery centers in India, Poland, and Vietnam give clients cost-effective development capacity with European management. For enterprises running extended programs with large development components, this model has a cost and flexibility advantage that ML6 cannot match from its four Western European offices.
Your challenge requires multiple disciplines coordinated under one engagement. Some programs need AI engineers working alongside data engineers, cloud architects, and software developers in a single, managed engagement. Xebia's multidisciplinary structure makes this smoother than assembling multiple boutiques and managing them as separate vendors.
The key difference: scope and depth
The most useful way to separate ML6 and Xebia is along two axes: scope and depth.
ML6 offers narrow scope and deep AI expertise. They are built to go very deep on a specific technical problem within a defined boundary. Their Google Cloud specialization, their team composition (all ML engineers), and their project structure all reinforce this. If you know exactly what you want built and it is an AI/ML engineering problem, ML6 is optimized for that.
Xebia offers broad scope and substantial breadth across multiple technical disciplines. Their Everest Group and Avasant recognition reflects the ability to manage complex, multi-disciplinary programs at enterprise scale. If you are not sure what to build and need advisory and strategy alongside technical execution, or if the program spans multiple technology domains, Xebia's multidisciplinary structure covers more ground.
This distinction matters most when scoping. A single predictive model for a manufacturing client is an ML6 engagement. A five-year digital transformation for a financial services firm that includes AI, cloud, application development, and agile coaching is an Xebia engagement. Confusing the two — hiring ML6 for a transformation program, or hiring Xebia for a bounded ML project where you need maximum specialist depth — leads to the wrong fit.
What they share (and why it matters)
Both ML6 and Xebia are strong at what they do. Both also share structural characteristics that are worth understanding before signing a contract.
Both bill by the day. ML6 charges day rates for ML engineers. Xebia charges day rates for multi-disciplinary teams. The pricing model is the same regardless of outcomes: you pay for effort (time and headcount), not for results delivered. The incentive structure rewards longer engagements, not faster ones.
Both hand off and leave. Both firms build a solution, transfer knowledge, and move to the next engagement. Your team inherits the codebase. When requirements change — and they always do — modifications require either re-engaging the consultancy at new day rates, or internal engineers who understand a custom-built system. Each re-engagement is a new project scope and a new budget.
Both scale linearly. Your second use case requires a new engagement. Your fifth use case requires another. There is no compounding advantage. Each new initiative is a fresh project, because that is how consultancies generate revenue.
Neither gives business teams direct ownership. In both models, engineers build and maintain the solution. Business teams are stakeholders. They cannot iterate directly on what was built without technical mediation. For AI agents on business workflows, where the team running the workflow needs to adjust the agent's behavior, this creates a dependency that slows iteration.
These shared characteristics are structural features of the consulting model, not flaws specific to either firm. For custom ML models, complex data engineering, and bespoke software, that model is appropriate and often the right choice. The limitation surfaces when the goal is deploying AI agents across business workflows at speed, with business team ownership, scaling across departments without starting over each time.
When neither model fits: the platform alternative
A pattern shows up consistently across enterprise AI programs. An organization engages an AI boutique — ML6, Xebia, Artefact, or someone similar. The consultancy does good work. The project delivers. Then the enterprise wants to scale: more agents, more workflows, more departments.
The math stops working. Each new use case is a new engagement, a new scoping process, a new budget request. Business teams still cannot iterate without engineering support. The total cost and timeline for a fleet of agents across the organization becomes prohibitive.
This is where a platform approach changes the economics.
Nexus is an enterprise AI agent platform paired with Forward Deployed Engineers who embed with your team from day one. The model is structurally different from both ML6 and Xebia:
- Per-agent pricing, not day rates. You pay for agents delivering value in production, not for hours building them. The incentive structure is aligned toward getting agents live and working.
- Business teams own the agents. They build and iterate directly, with FDE support on-site. No filing change requests with an external consultancy.
- 2–6 weeks to production, not months. Orange deployed customer onboarding agents across multiple European markets in 4 weeks — ~$6M+ in yearly revenue impact, 100% team adoption.
- Scaling compounds, not multiplies. Each new agent deploys in days, not months. The second use case does not cost what the first did.
- No handoff gap. FDEs stay embedded. The platform handles infrastructure, integrations, and ongoing optimization. Business teams focus on outcomes, not maintenance.
- Enterprise governance from day one. SOC 2 Type II, ISO 27001, ISO 42001, GDPR. Full audit trails and decision traceability. No custom compliance engineering required.
This is not a criticism of either ML6 or Xebia. It is a structural observation about the job to be done. If the job is "build us a custom ML model," a consultancy is the right choice. If the job is "get AI agents completing business workflows in production, owned by our business teams, scaling across departments," the consulting model's incentive structure and ownership model work against you.
Practical decision framework
| If your situation is... | Consider... |
|---|---|
| You need a custom ML model (computer vision, NLP, predictive analytics) | ML6 |
| You need deep Google Cloud AI engineering (Vertex AI, BigQuery, GKE) | ML6 |
| You're in Benelux or DACH and want a local AI specialist | ML6 |
| You need AI + cloud + data + software in a coordinated program | Xebia |
| You need nearshore delivery capacity alongside European management | Xebia |
| You need multi-workstream scale (dozens of engineers across disciplines) | Xebia |
| You need data strategy and marketing analytics | Artefact |
| You need AI agents on business workflows live in weeks | Nexus |
| You need business teams to own and iterate on agents directly | Nexus |
| You need to scale from one agent to a fleet without starting over | Nexus |
Frequently asked questions
What is ML6's primary specialization? ML6 specializes in AI/ML engineering: building custom machine learning models, deploying ML pipelines, and operationalizing AI systems in production. Their specific strengths are computer vision, NLP, predictive analytics, and MLOps. They are not a general IT or digital transformation consultancy — every engagement is an AI engineering project. They work primarily on Google Cloud (Vertex AI, BigQuery, GKE) and are an OpenAI Services Partner.
What makes Xebia different from a pure AI boutique like ML6? Xebia is a full-stack digital consultancy, not an AI boutique. AI is an important practice but one of several — alongside cloud engineering, data engineering, custom software development, and agile transformation. Where ML6 covers the AI component of a program, Xebia can coordinate an entire multi-workstream transformation: AI, cloud migration, application development, and organizational change management under one engagement. Xebia is the stronger choice when the AI initiative cannot be cleanly separated from broader infrastructure or product development work.
How does ML6's Google Cloud partnership compare to Xebia's? Both firms hold significant Google Cloud partnerships. ML6 won Google Cloud Benelux Services Partner of the Year in both 2023 and 2024, reflecting deep specialist AI/ML engineering on that platform. Xebia has been a Google Cloud Premier Partner since 2007 and won the Google Cloud Sales Partner of the Year for Benelux in 2024. The difference is scope: ML6's partnership reflects concentrated AI/ML engineering depth on Google Cloud; Xebia's reflects broader enterprise delivery across Google Cloud's full stack.
What is Unum, ML6's platform? Unum is ML6's Enterprise Superintelligence platform, launched in 2025. It is designed to transform enterprises into "living, learning entities" through coordinated AI agents — essentially a product-led layer on top of ML6's existing AI engineering capabilities. Unum is positioned for enterprises that want an ongoing AI operating system rather than discrete consulting projects. The platform is still early stage relative to ML6's core consulting practice, but it signals a strategic shift toward product-led revenue alongside services.
What day rates do ML6 and Xebia charge? Neither firm publishes pricing. Based on market benchmarks for European AI consultancies of their seniority and specialization, ML6 day rates are estimated at €1,200–€2,800/day per specialist depending on seniority and scope. Xebia rates are estimated at €1,500–€3,500/day depending on the discipline (AI engineers typically at the higher end; agile coaches at the lower end). For both firms, engagement-level pricing depends on team composition, project duration, and whether work is delivered onshore or via nearshore delivery centers. Confirm directly during scoping.
Worth exploring?
Every Nexus engagement starts with a 3-month proof of concept tied to measurable outcomes. Forward Deployed Engineers embed with your team from day one. You see results before committing. You can exit anytime.
Related reading
- Nexus vs ML6: full comparison
- Nexus vs Xebia: full comparison
- Top 10 ML6 alternatives for AI consulting
- Top 10 AI boutique consultancies in Europe vs platforms
- AI boutique consultancy vs AI platform: how to choose
- Nexus vs Artefact: data consultancy vs platform
- Top 10 AI consulting alternatives: platforms vs firms
