How much does an AI integration sprint cost?

€5,000 flat for a fixed-scope, 2-week engagement. This includes discovery and scoping (days 1–2), implementation in your codebase (days 3–12), and handoff with full documentation, tests, and a walkthrough call (days 13–14). Two weeks of bug fixes post-handoff are also included. Most AI agencies charge €50,000–€300,000+ for comparable multi-month engagements.

What if I need more work than fits in 2 weeks?

Two sprints back-to-back, €10,000 total, 4 weeks of work. Each sprint still has its own fixed scope and deliverable — you see shipped results every two weeks rather than waiting months for one big reveal.

Do you offer hourly billing or retainers?

No. Fixed price per sprint, no hourly billing, no retainers. Fixed pricing forces clarity: we agree on the deliverable before I write code, and there is no incentive to stretch the work.

What if the feature does not meet spec?

I fix it for free until it matches the scoping document, or I refund in full. The delivery guarantee removes the risk of paying for something that does not work.

Why only 2 sprints per month?

Deep focus per client is the main value. Running more than two sprints in parallel means context switching, which hurts quality. If you need to book further out, schedule a call to reserve a slot.

What can an AI integration sprint deliver?

Production-ready features including RAG-powered search over internal data, intelligent document processing, AI-driven classification and summarization, LLM-powered workflow automation, multi-agent systems, and custom AI integrations for your existing stack.

How does the 2-week timeline actually work?

Days 1–2: discovery call and scoping document. Days 3–12: implementation in your codebase with daily async updates, feature in staging by day 8. Days 13–14: handoff with documentation, tests, and a walkthrough call.

How much of my engineering team's time does this require?

Minimal. A 60-minute scoping call on day 1, a 30-minute staging review around day 8, and a 60-minute handoff call on day 14. Roughly 2.5 hours of meetings total, plus async review of code and docs.

How long does it take to add AI features to a SaaS product?

With a structured sprint, production-ready AI features ship in 2 weeks. No discovery workshops, no 6-month roadmaps — just scoped, built, and deployed code.

What tech stack do you work in?

TypeScript and Next.js for the web layer, Python for data and ML pipelines, any modern vector database (pgvector, Pinecone, Weaviate). Compatible with most B2B SaaS stacks. If your stack is not a fit, I will tell you upfront on the scoping call.

Do I need to hire a full-time AI engineer?

No. A fixed-scope sprint lets you ship production AI features without the overhead of hiring. You get production-ready code, documentation, and 2 weeks of post-delivery support — all for €5,000 per sprint.

Who owns the code after delivery?

You do. Everything ships into your repository, branded as your team's code. No recurring fees, no lock-in, no dependencies on me to keep it running.

What happens after the handoff?

Two weeks of free bug fixes are included. After that, the feature is yours to own and extend. If new issues surface months later, you can re-engage me for a small scope or lean on your internal team — the code and docs are written to be maintainable.

AI Sprint vs Hiring Full-Time: Which Is Right for Your SaaS?

A fixed-price AI sprint costs €5,000 and delivers one production feature in 2 weeks. Hiring a full-time AI engineer costs €110,000–€160,000 per year all-in and delivers the first feature in 3–6 months. For most B2B SaaS companies validating their first AI feature, a sprint is 12x faster and 20x cheaper.

You need AI features in your product. The question is: do you hire someone full-time, or bring in a specialist for a focused sprint?

I've been on both sides of this — as a full-time engineer at companies building AI features, and now as an independent consultant running fixed-price sprints. Here's the honest comparison.

Cost comparison

	AI Sprint	Full-Time Hire
Upfront cost	€5,000 per sprint	€15,000 – €25,000 (recruiting + onboarding)
Annual cost	€5,000 – €30,000 (as needed)	€80,000 – €120,000 salary + benefits
Time to first delivery	2 weeks	3 – 6 months (hiring + ramp-up)
Ongoing commitment	None	12+ months effective minimum
Risk	€5K if it doesn't work out	3–6 months of salary before you know if it's working

The math is stark. A full-time AI engineer in Western Europe costs €80K–€120K per year in salary alone (per Levels.fyi and 2024 StackOverflow Developer Survey for Western Europe AI engineers). Add employer taxes, benefits, equipment, and management overhead, and you're looking at €110K–€160K total cost (based on EU employer burden data from Deloitte and PwC total compensation reports). The hiring process itself takes 2–4 months (per LinkedIn Talent Insights for senior engineering roles in Western Europe).

A sprint costs €5,000 and delivers working software in 2 weeks.

Speed comparison

Sprint timeline:

Week 0: Scoping call, agreement on deliverable
Weeks 1–2: Build, test, deploy
Week 2: Feature is in production

Full-time hire timeline:

Months 1–2: Write job description, source candidates, interview
Month 3: Offer, notice period, start date
Month 4: Onboarding, codebase familiarization
Months 5–6: First meaningful feature delivery

That's a 12x difference in time-to-delivery. For a SaaS company testing whether AI features move the needle, waiting 6 months for a first data point is expensive — not in salary, but in opportunity cost.

When to hire full-time

Full-time makes sense when:

You need 3+ AI features per quarter. If AI is a core differentiator and you're shipping continuously, you need someone embedded in the team.
You have ongoing model fine-tuning or ML ops. Custom models, training pipelines, and continuous evaluation need dedicated attention.
AI is your product, not a feature. If you're an AI-first company, your engineering team should include AI specialists.
You have the management bandwidth. AI engineers need technical leadership. If nobody on your team can evaluate their work, hiring one creates more problems than it solves.

When a sprint makes sense

A sprint is the right call when:

You're building your first AI feature. Validate the concept before committing to a hire. A sprint gives you working software and real user feedback in 2 weeks.
You need a specific capability. RAG pipeline, document processing, agent workflow — these are well-defined problems that don't need a full-time person.
You're in a capacity crunch. Your engineering team is busy with core product work. A sprint adds AI capability without pulling anyone off their current projects.
You want to de-risk a hire. Run a sprint first. If the feature succeeds, you'll have working code and a clear spec for what a full-time hire should own.

The hybrid approach

The teams that get the most out of sprints use them to bootstrap — then hire full-time to maintain. The pattern looks like this:

Sprint 1: Build the first AI feature (RAG, classification, whatever the highest-impact use case is)
Validate: Ship it, measure adoption, collect user feedback
Sprint 2–3: Iterate based on real data, add adjacent features
Hire: Now you know AI is working. You have production code, clear requirements, and a roadmap. Hire someone to own it.

This approach costs €10K–€15K in sprints before you commit to a €100K+ annual hire. That's a reasonable insurance premium.

The bottom line

Don't hire a full-time AI engineer to find out if AI features matter to your users. Run a sprint, ship something real, and let the data tell you when it's time to bring someone in-house.