The AI vendor landscape is a mess. Every quarter brings new platforms, new model providers, new startups claiming to solve every problem, and new pricing structures that make comparison nearly impossible. For a Mittelstand company making its first significant AI investment, the risk of choosing wrong is not abstract — it is a six-figure mistake that can set you back 12 months.
The good news: you do not need to pick the perfect vendor. You need to pick a good-enough vendor while maintaining the ability to switch. That distinction — good enough with optionality — is the key principle behind every vendor decision that works.
Why vendor selection is harder in AI than in traditional software
Traditional enterprise software is stable. SAP works the same way this year as it did last year. The vendor landscape is consolidated. Switching costs are high but predictable. You make a decision, live with it for 5-10 years, and plan upgrades on a known timeline.
AI is the opposite. Model capabilities improve dramatically between versions. Today's state-of-the-art is next quarter's baseline. Pricing structures change as providers experiment with per-token, per-seat, and per-outcome models. New open-source alternatives emerge that make expensive proprietary solutions unnecessary. Vendors pivot, merge, or shut down.
Making a 5-year commitment to an AI platform today is like signing a 5-year mobile phone contract in 2008. The landscape will look fundamentally different by the time the contract expires.
The four-layer evaluation framework
We evaluate AI vendors across four layers. Each layer carries different risk and different switching costs.
Layer 1: Model providers
The foundation models — OpenAI, Anthropic, Google, Meta (open-source), Mistral, and others. This layer changes fastest and should be treated as interchangeable.
Evaluation criteria: Model quality for your specific use case (benchmark scores are misleading — test with your data), pricing per transaction at your expected volume, data processing terms (where is data sent, stored, processed?), EU data residency options.
Lock-in risk: Low, if you architect correctly. Models are accessed via APIs with similar input/output formats. Switching from one provider to another requires days, not months — provided you have not embedded provider-specific features deeply into your workflow.
Recommendation: Design your system to be model-agnostic from day one. Use an abstraction layer that lets you swap model providers without changing your application code. This is standard practice and adds minimal complexity. For more on why buying models and building integration is typically the right approach, see Build vs. Buy for Enterprise AI.
Layer 2: AI platforms and orchestration
The middleware — tools for building AI workflows, managing prompts, handling retrieval pipelines, orchestrating multi-step processes. This includes LangChain, LlamaIndex, Haystack, as well as managed platforms from cloud providers (Azure AI, AWS Bedrock, Google Vertex AI).
Evaluation criteria: Does the platform support your implementation patterns (classification, extraction, retrieval, orchestration)? What are the hosting options (cloud, on-premise, hybrid)? How mature is the monitoring and observability? What is the learning curve for your team?
Lock-in risk: Medium. Platform-specific abstractions, workflow definitions, and integration patterns create switching costs. Moving from one orchestration platform to another is a project, not a configuration change.
Recommendation: Prefer open-source orchestration tools over proprietary platforms where functionality is comparable. If you use a managed cloud platform, ensure your core logic (prompts, validation rules, business logic) lives outside the platform and can be extracted if needed.
Layer 3: Data infrastructure
Where your data lives, how it is processed, and how it feeds into AI workflows. This includes vector databases, data pipelines, feature stores, and integration layers with your existing systems (ERP, CRM, DMS).
Evaluation criteria: Compatibility with your existing infrastructure. Data sovereignty compliance (DSGVO, EU data residency). Performance at your data volumes. Export capabilities — can you get your data out?
Lock-in risk: High. Data infrastructure is the stickiest layer. Once your data pipelines are built and your vector databases are populated, switching is expensive and risky. This is where most real lock-in occurs.
Recommendation: Own your data infrastructure. Use standard formats and open protocols where possible. Ensure that every data pipeline includes an export capability. Never let a vendor be the sole repository for your operational data.
Layer 4: Implementation and operations partners
The companies that help you build, deploy, and operate AI workflows. Consultancies, system integrators, and specialised AI partners.
Evaluation criteria: Do they have relevant industry experience (not just AI experience — your industry)? Do they transfer knowledge or create dependency? What is the engagement model (project-based, retainer, embedded team)? Will your team be able to operate the system independently after the engagement?
Lock-in risk: Variable. A partner that builds opaque systems and maintains exclusive operational knowledge creates high lock-in. A partner that builds transparently, documents everything, and trains your team creates low lock-in.
Recommendation: Choose partners who work themselves out of a job. The engagement should end with your team able to operate and evolve the AI workflow independently. Any partner who structures engagements to create ongoing dependency is optimising for their revenue, not your capability.
Data sovereignty: the non-negotiable requirement
For DACH enterprises, data sovereignty is not a nice-to-have. Under DSGVO, sending personal data to non-EU processors requires adequate safeguards. Under sector-specific regulation (BaFin for financial services, insurance supervisory authorities), certain data may not leave German jurisdiction at all.
The practical implications for vendor selection:
Model providers: Ensure EU data processing options exist. OpenAI via Azure offers EU data residency. Anthropic offers EU processing through AWS Frankfurt. Open-source models can be hosted entirely on-premise.
Cloud platforms: Azure (Frankfurt, Berlin regions), AWS (Frankfurt region), and Google Cloud (Frankfurt region) all offer EU data residency. Confirm that data processing — not just storage — remains in-region.
Vector databases and data infrastructure: Host in EU. If using managed services, verify the data residency terms. Self-hosted options (Qdrant, Milvus, Weaviate on your infrastructure) eliminate the concern entirely.
Do not accept vendor assurances without verification. Ask specifically: where is data processed (not just stored)? Is data used for model training? Can you provide a Data Processing Agreement (Auftragsverarbeitungsvertrag) that satisfies DSGVO requirements?
The exit clause test
Before signing any AI vendor contract, apply the exit clause test: If we needed to switch vendors in 6 months, could we?
This test reveals hidden dependencies. Can you export your data in a standard format? Can you replicate the workflow on a different platform? Do you own your prompts, validation rules, and business logic? Is the operational knowledge documented or locked in the vendor's team?
If the answer to any of these questions is "no," you have identified a lock-in risk. That does not mean you should not proceed — it means you should negotiate terms that mitigate the risk (data portability clauses, source code escrow, documentation requirements) or architect your system to reduce the dependency.
Common mistakes
Choosing based on demos. Every vendor demo looks impressive. Your procurement decision should be based on a proof of concept with your data, at your scale, addressing your use case — not on a polished presentation with cherry-picked examples.
Over-investing in platform before proving value. Do not buy an enterprise AI platform before you have a single workflow in production. Start with lightweight tools, prove the business case, then invest in infrastructure. This is the reverse of what most vendors recommend — because most vendors sell platforms.
Ignoring total cost of ownership. API costs are the visible expense. Compute, storage, integration development, operational monitoring, and team training are the invisible ones. A model that costs EUR 0.01 per API call but requires EUR 50K in integration development is not cheap.
Treating vendor selection as a one-time decision. Review your vendor stack annually. The landscape changes fast enough that last year's optimal choice may not be this year's. Build the optionality to switch without making switching your default — stability has value too.
Making the decision
Vendor selection paralysis is real. The landscape is complex, the stakes feel high, and the options are overwhelming. Here is the pragmatic approach:
- Define your first use case specifically (see Process Mining for AI Candidates)
- Identify the minimum vendor stack needed for that use case
- Prioritise data sovereignty and exit flexibility over feature completeness
- Run a time-boxed proof of concept (2-4 weeks) before committing
- Start small, learn, and expand
If you want help navigating vendor selection for your specific situation, book a Fit Call. We are vendor-agnostic — we do not resell platforms or take referral commissions — which means our recommendation is based entirely on what works for your use case, your data sovereignty requirements, and your team's capabilities.
For the broader operational context, see AI in Operations.
This article is part of the AI in Operations series by Andreas Anding. For the full methodology, see The AI Operating System.