Do you work on pure equity?

I love working with ambitious founders, and I happily structure hybrid engagements — a reduced service fee combined with equity — when the fit is right. Pure-equity arrangements, however, aren't something I take on. A service fee is required on every project: it ensures focused delivery, protects both sides against misaligned timelines, and keeps the engagement sustainable the same way any senior technical hire is compensated. If you're an early-stage founder, let's talk — there's almost always a structure that works.

How much does a Fractional CTO cost in Europe?

Hourly consulting is €150/hour, day rate is €700/day. Ongoing Fractional CTO retainers start from about €2,100/month for a 2-3 days/month minimum and scale up with the cadence — a 1-day-a-week engagement typically lands between €5,000–€8,000 per month depending on scope. Partnership deals (reduced fee + equity) are available for aligned early-stage startups. All prices exclude VAT and French TVA applies where relevant; EU B2B clients with a valid intra-community VAT number benefit from reverse charge.

Fractional CTO vs full-time CTO — which should I hire?

Hire a full-time CTO when the role genuinely needs 40+ hours/week of executive tech leadership — usually post-Series A with 10+ engineers, a real product in market, and a 3-year roadmap. Hire a Fractional CTO when you need senior technical leadership but: you're pre-seed to Series A, your engineering team is under 10 people, you want to validate the product before committing to a cap-table-impacting hire, or you need a specific expertise (AI strategy, EU compliance, sovereign cloud) that a generalist full-time CTO wouldn't bring.

How does the Partnership model work?

It's a hybrid engagement: a reduced cash rate (typically 30-40% of my standard) combined with equity. The exact split is discussed case by case after we've talked through your stage, timeline, runway, and goals. The service-fee component is always present; it's what keeps the engagement focused and fair to both sides.

What's a typical engagement duration?

Consultations run 30 or 60 minutes. MVPs typically ship in 4-8 weeks. Fractional CTO engagements are usually 2-3 days per month minimum, with the cadence scaling up based on traction and fit. Shorter sprint engagements (1-2 weeks) are also possible for well-scoped prototypes.

Can you help with EU AI Act compliance?

Yes. I help companies classify their AI systems by risk tier (prohibited / high-risk / limited-risk / minimal-risk), implement the technical documentation and post-market monitoring required by Articles 9-15, align with GPAI obligations for foundation-model users, and produce the DPIA, Transfer Impact Assessments (Schrems II), and Article 28 DPAs needed. I also cross-walk with GDPR, NIS2, DORA (for financial services), and ISO/IEC 42001 / 23894. Sign-off always rests with your DPO or legal counsel; what I deliver is a defensible, documented compliance posture.

Can you help us migrate from OpenAI to sovereign infrastructure?

Yes — this is becoming a common request from European companies facing compliance pressure or customer procurement scrutiny. Typical path: audit current OpenAI/Anthropic usage, map workloads by quality/latency/cost sensitivity, select replacement targets (Mistral Large for most chat, self-hosted Llama-3 or Mixtral for sensitive data, Claude Sonnet via Bedrock EU where acceptable), build an eval harness that proves parity, migrate behind a feature flag, and cut over gradually. End state: zero prompt/response egress to non-EU jurisdictions, with an auditable trail.

Do you work with non-technical founders?

Yes — this is one of my most common engagements. Non-technical founders hire me to make the build vs buy decision, run the first hiring, set up the stack, ship the MVP, and represent the company technically with investors, partners, and early customers. I translate between product intent and engineering reality, and write everything in plain language so the founder stays in the loop without needing to code.

Do you work remote or on-site?

Remote-first, based in Paris. I'm open to on-site days in Paris and short on-site sprints across Europe for the right engagements. Most clients are 100% remote with weekly syncs and async daily updates.

How quickly can you start?

Usually within 1-2 weeks of signing. For urgent MVPs or discovery sprints I can often kick off within days, starting with a scoped 3-5 day discovery phase before we commit to the full build.

Yes. Mutual NDAs are standard. I can send you a clean one to start, or sign yours if the terms are reasonable. All client work, product ideas, and internal data stay strictly confidential — during and after the engagement.

Can you join as a technical co-founder?

Rarely, and only for startups I'm deeply aligned with — on mission, market, and long-term collaboration. Equity-heavy co-founder arrangements need mutual conviction on both sides and a realistic path to milestones. More often than not, the Partnership model (reduced service fee + equity) is a better fit for both of us: it gets you senior technical leadership without the co-founder commitment, and it keeps my focus across a handful of great projects rather than locked into one.

Can you work with US or Canadian clients?

Yes — North American clients are a regular part of the practice. Time-zone overlap with the US East Coast is 3-4 synchronous hours daily (your morning, my afternoon), and engagements typically run async-friendly with weekly syncs.

How to choose between OpenAI, Claude, Mistral, and Llama for your product

There is no "best" LLM — the right choice depends on what you are optimising for. The four model families that matter for most production builds in 2026 are OpenAI (GPT-4o, GPT-5), Anthropic Claude, Mistral, and Meta Llama. Each one wins on a different axis, and the right architecture often combines two or three.

The four families at a glance

OpenAI

The widest ecosystem, the most mature tooling, the broadest capability surface. Strong on reasoning (o-series), real-time multimodal (GPT-4o), structured outputs, function calling, image generation. Weakness: data flows to OpenAI infrastructure (US-based), with EU-residency only via Azure OpenAI Service. API rate limits and pricing are predictable.

Anthropic Claude

Strongest on long-context reasoning (200k+ token windows), nuanced instruction following, and code understanding. The Constitutional AI training approach produces a model that pushes back on ambiguous or risky requests rather than confabulating. Weakness: smaller ecosystem than OpenAI, fewer real-time and multimodal features, no first-party image generation.

Mistral

The European sovereign option. Mistral Large for general use, Codestral for coding, Pixtral for multimodal. Models are available both through Mistral's API and as open-weight downloads (for some variants), enabling self-hosting on EU infrastructure (OVHcloud, Scaleway). Weakness: smaller scale of training and frontier capability than OpenAI/Anthropic; ecosystem still maturing.

Meta Llama

Open-weight, fully self-hostable. Llama 3.x families ship in multiple sizes (8B / 70B / 405B parameters). The strength is total infrastructure control: data never leaves your environment, no API dependency, no per-token pricing. Weakness: you take on the inference infrastructure, the cost engineering, and the safety tuning yourself. Not a drop-in for managed APIs.

A decision framework

Five questions, in priority order:

1. Where can your data legally go?

If your data must stay in the EU under GDPR (sensitive customer data, regulated-industry corpora) or under sectoral rules (HIPAA, PSD2, banking secrecy laws, attorney-client privilege), the choice narrows immediately:

EU-only: Mistral on Mistral's EU API, Mistral or Llama self-hosted on OVHcloud / Scaleway / Hetzner, Claude via AWS Bedrock eu-central-1 / eu-west-3 with EU-only routing, OpenAI via Azure OpenAI EU endpoints.
Anywhere: any of the four families on any provider.

Data residency is the first filter. Capability is the second.

2. What capability axis matters most?

Different families are still meaningfully different on specific tasks:

Long-context document reasoning (legal, medical, regulatory): Claude is the strongest default.
Real-time multimodal (voice, vision-as-input, low latency): OpenAI GPT-4o has the most mature stack.
Structured outputs and function calling: OpenAI and Anthropic both ship this well; Mistral has it but the ecosystem is younger.
Code generation and understanding: Claude and Codestral both strong; OpenAI competitive.
Cost-sensitive batch processing: Llama or smaller Mistral variants self-hosted are typically cheapest at scale.
Image generation: OpenAI DALL-E or third parties (Black Forest Labs Flux). Not a strength of Claude / Mistral / Llama.

3. What does cost look like at your projected volume?

Hosted-API pricing is roughly comparable across OpenAI / Claude / Mistral at the same capability tier, with differences usually within 2-3x. Self-hosted Llama or Mistral can be cheaper at high volume — but only after the engineering cost of running the inference is paid. Below ~10M tokens/day, hosted APIs are almost always cheaper than self-hosting once you account for engineering time. Above ~100M tokens/day, the math reverses.

4. How much does ecosystem maturity matter?

OpenAI has the deepest tooling: SDKs in every language, fine-tuning, evals, batch API, real-time API, assistants, file management. Claude has good tooling but a narrower surface. Mistral and Llama (especially via cloud providers) are catching up but still less integrated. Teams that need to ship fast and rely on community-built tooling lean toward OpenAI; teams comfortable building their own primitives have more freedom.

5. What is your hedge against vendor risk?

OpenAI and Anthropic both have rate-limit and pricing-change risks. Mistral hedges via European jurisdiction. Llama hedges via self-hosting — your inference does not depend on any vendor at all. Most teams should design for swap-ability between two providers from day one (e.g., OpenAI primary, Claude secondary) rather than betting on one.

Common combinations that work

EU-regulated B2B SaaS

Mistral Large or Claude (via Bedrock EU) for the core LLM layer; Llama self-hosted on OVHcloud or Scaleway for sensitive inference where data must never leave your environment. OpenAI not used for primary inference but possibly for non-sensitive dev tooling.

US-incorporated SaaS, no special data sensitivity

OpenAI primary (broadest ecosystem), Claude secondary (long-context tasks, fallback if OpenAI rate-limits). Mistral and Llama only if cost optimisation becomes pressing at scale.

Cross-border product (US-incorporated, EU customers)

OpenAI via Azure EU endpoints for general inference, Claude via Bedrock EU for long-context, Mistral as fallback for the most regulated EU customer segments. Architecture must route per-tenant based on data residency requirements.

Heavy cost-sensitive batch workload

Llama 3.1 70B self-hosted on a GPU cluster. Hosted API only for the small percentage of queries that require frontier capability the open-weight model does not match.

What fails first

A few real-world failure modes worth knowing before you commit:

OpenAI rate limits during peak load. Predictable but surprises teams who did not plan for a fallback.
Claude refuses things it should not refuse. The Constitutional AI training is conservative; some legitimate use cases (security analysis, certain medical queries) get refused unhelpfully.
Mistral output quality on long-tail tasks. Solid on common tasks; can underperform OpenAI / Claude on edge-case reasoning that frontier-scale models handle better.
Self-hosted Llama becomes a maintenance burden. The day-to-day cost of running your own inference (GPU monitoring, model updates, scaling) is real and underestimated by teams that have not done it before.
Vendor model deprecations. OpenAI in particular deprecates older model names; production code that hardcodes gpt-4-0613 breaks on a schedule.

A practical default

For a typical European AI-first SaaS in 2026:

Primary: Claude via AWS Bedrock eu-west-3 for the LLM layer. Strong reasoning, EU residency, mature ecosystem.
Secondary: OpenAI GPT-4o for tasks where Claude under-delivers (real-time multimodal, image-heavy use cases).
Sovereign tier: Mistral Large or Llama 3.x self-hosted on OVHcloud for the most regulated tenants where data must never leave EU sovereign infrastructure.
Architecture: abstract the model behind a thin internal API so you can swap providers without rewriting your application logic.

Bottom line

The right model is the one whose trade-offs match your data-residency requirements, capability needs, cost envelope, and risk tolerance. Most production systems combine two or three model families behind a unified internal API — not because it is fashionable, but because no single family wins on every axis. Insightrix Sovereign AI structures these decisions for European deployments specifically; submit a project brief for a tailored architecture review.

Editorial content. Informational only — not legal, financial, or professional advice.