Malt Tech Trends 2026
Deep-dive AI
The rise of agentic systems marks the shift from "AI that answers questions" to "AI that completes tasks." Autonomous systems now reason through workflows, use tools, and execute actions independently across multiple steps.
GenAI tooling ecosystem remains fragmented across competing solutions with no clear winners emerging. The market is moving too fast for standards to form, making freelancer adoption patterns the clearest signal for what's next.
AI engineers community growing 229% to meet AI agent demand explosion
Embracing the AI two-speed and the Python consensus
This past year has revealed AI's fundamental two-speed evolution: enterprises are industrializing their first agentic systems while freelancers bet on their successors. AI agent demand multiplied 60x year-over-year as organizations transition from RAG architectures to autonomous systems that complete tasks, not just answer questions.
Meanwhile, the freelancer community is adopting emerging tools at explosive rates (LangGraph +1,007%, MCP +2,788%) while client demand for these same tools remains negligible, positioning for future architectures while enterprises stabilize 2025 stacks.
The AI Engineer portrait
Expertise split
Top 6 skills in supply
Meet AI engineers
Market expert
The real cost of custom middleware isn't the code. It's the cognitive load it creates for every engineer who inherits it. n8n externalizes that complexity, which means your senior engineers spend less time deciphering past decisions and more time making better ones. For a CTO, that's not a tooling choice. It's a team health decision.
The LLM battles intensify as multi-model strategies emerge
LLM demand grew 35% overall, but market share dynamics reveals a competitive landscape. OpenAI remains dominant with 57% market share but is losing ground rapidly, down 18 percentage points in a year. The challengers are Gemini (Google) +39% with 19% share and Claude (Anthropic) +89% with 11% share but also Llama (Meta) and Mistral (Mistral AI).
Multi-model strategies emerge as organizations prioritize different factors: some optimize performance for specific workloads, others ensure resilience through vendor diversification, maintain control via customization and sovereignty, or focus on cost efficiency.
Production maturity arrives: MLOps industrializes GenAI, becoming a non-negotiable
MLOps being mentioned in project briefs exploded 178% year-over-year, reflecting the shift from experimental GenAI projects to production deployments. A severe talent shortage is emerging with MLOps supply growing only 63%, creating a 1.7x demand-supply differential.
MLOps freelancers leverage years of traditional ML deployment experience (GPU infrastructure management, model evaluation methodologies, production observability) to address the challenges GenAI projects now face in production.
The ML Ops portrait
Expertise split
Top 6 skills in supply
Market expert
Leaders redesigning operating models ask what AI can automate, but rarely ask what those tasks were quietly building.
The most critical element that is underestimated is the judgment pipeline — the years of hands-on, repetitive practice through which professionals develop the pattern recognition that lets them sense when something is wrong, even when the data looks right.
Yet the more organisations rely on AI, the more they need exactly this kind of human judgment to oversee, challenge, and course-correct. Strip too many of those formative experiences away in the name of efficiency, and in a few years you will have an organisation that depends on human judgment more than ever but has stopped producing the people capable of exercising it.
The 2026 tech trends deep-dives
Key insights
How Europe’s top independent talents are shaping the tech landscape.
Key insights
How Europe’s top independent talents are shaping the tech landscape.
Our research is based on 2,5 million searches on the Malt platform across our community of 1 million freelancers, including 250,000 tech freelancers, and +90,000 companies.
Cloud & Infra
It is now AI-driven cloud evolution, with AI workloads cost exploding.
Cloud & Infra
From migration to AI-driven cost and architecture
Organizations are now architecting specifically for AI workloads rather than general-purpose migration. Generative AI on Cloud demand rose +96%, while LLM-specific infrastructure projects increased by +154%.AI workloads have also driven FinOps from a peripheral exercise into a core engineering mandate, with +72% in dedicated projects.
Data platform
The data landscape is moving from analytics usage to operational focus.
Data platform
Balancing between consolidation and innovation
The 'best-of-breed' era in Business Intelligence is ending. Organizations are stopping the multi-tool headache and consolidating around ecosystems like never before. Power BI now commands over one-third of all demand— 3.5 times its nearest competitor. Meanwhile, Tableau demand has decreased by 46%. It’s a massive shift toward Microsoft's integrated stack
Cybersecurity
With AI penetration, security is at the core of any tech project.
Cybersecurity
A convergence crisis, driven by AI, is reshaping the GRC landscape
The significant growth in GRC demand — +31% ISO-27001, +229% ISO 7816, coupled with slower growth in the number of freelancers, has led to strong market tension and a scarcity of talent in the cybersecurity domain.
Adding to this, the rise of AI is forcing a rapid evolution of both the discipline and the required profiles.
Low-Code
Automation and low-code tools now empowering senior tech experts.
Low-Code
Low-code tools are now empowering tech experts, while software programming landscape is transforming.