Most companies that hire an outside team for AI work start with the wrong question. They compare countries and hourly rates, pick a region, then shortlist vendors inside it. The projects that go badly rarely fail because of geography. They fail because nobody senior was on the team, because the contract left the code ownership vague, or because a four-hour overlap turned into forty minutes once daylight saving changed.
This guide is meant to help a CTO, head of product or operations lead compare options honestly. It covers the three location models, what actually drives cost, the contract terms that matter for AI work in particular, and a scoring table you can use to compare vendors side by side.
Onshore, nearshore, offshore: what the labels mean
Onshore means the team is in your own country. Nearshore means a nearby country with significant working-hours overlap: Latin America for US companies, Central and Eastern Europe or Portugal for UK and German companies, and to some extent Southeast Asia for Australia. Offshore usually means a distant region with limited overlap, such as South Asia for US clients.
These labels are relative. A team in Eastern Europe is nearshore for Berlin and offshore for San Francisco. A team in Pakistan or India overlaps comfortably with a UK or German morning and with most of the Australian afternoon, but only marginally with the US West Coast. Rather than trusting the label, write down the hours your team actually works and count the overlap.
| Model | Typical strengths | Typical weaknesses |
|---|---|---|
| Onshore | Same hours, same legal system, easy in-person workshops, simpler compliance for regulated data | Highest rates; senior AI engineers are scarce and often booked months out |
| Nearshore | Good overlap, moderate rates, short travel, often similar business culture | Rates have risen in popular hubs; quality varies widely between vendors |
| Offshore | Lowest rates, large talent pools, can run work around the clock | Limited overlap, more reliance on written specs, higher risk if the vendor is junior-heavy |
Cost: compare the project, not the rate
Hourly rates differ several-fold between regions, which is why buyers anchor on them. But AI projects are dominated by a few expensive mistakes rather than by hours of routine coding. A retrieval pipeline built on the wrong chunking strategy, an evaluation set that does not reflect production traffic, or an agent design that calls the model ten times where two would do can each cost more than the entire rate difference between two vendors.
Ask every vendor for a fixed-price or capped quote for a clearly scoped first phase, then compare those totals. Also include the costs that never appear on the invoice: your own team's time spent in meetings, reviewing work and rewriting specs. A cheap team that needs a full-time product manager on your side to steer it is not cheap. Finally, ask what model and infrastructure costs the design will produce once it is running. An architecture that costs 30% less to build and twice as much in API fees each month is a bad trade for most products.
Timezone overlap and how work actually flows
Two to four hours of daily overlap is enough for most AI projects, provided the vendor works in writing well. Less than two hours makes debugging production issues slow, because every question becomes a 24-hour round trip. More overlap than that helps less than people expect, since engineers need long uninterrupted blocks to do the actual work.
What matters more is how the overlap is used. Good partners keep a shared channel, post written daily updates, demo working software every week or two, and keep decisions in a log you can read later. Ask a vendor to show you a real (anonymized) status update from a current project. It tells you more than any sales deck. Check daylight saving too: the US, UK, EU and Australia shift clocks on different dates, and Australia shifts in the opposite direction, so an overlap that looks fine in January may shrink in April.
IP ownership and contracts
This is the section to read twice. In many jurisdictions, code written by a contractor belongs to the contractor unless the contract assigns it to you. For AI work there are also prompts, evaluation datasets, fine-tuned weights and pipeline configuration, which some vendors treat as their own reusable assets.
- Assignment of IP: the contract should assign all work product to you on payment, explicitly including source code, prompts, evaluation sets, model fine-tunes and documentation. A license to use the code is not the same thing.
- Pre-existing materials: vendors legitimately reuse their own libraries. The contract should list them and grant you a perpetual, royalty-free license to anything embedded in your deliverables.
- Repository access: work should happen in your repository and your cloud accounts from day one. If the vendor insists on its own repo with code delivered at the end, ask why.
- Data handling: a data processing agreement if personal data is involved, a list of subprocessors, and a clear statement of which model providers will see your data and under what terms.
- Governing law and jurisdiction: ideally yours, or at least a neutral and enforceable one. Enforcing a contract in a distant jurisdiction is slow and costly.
- Exit terms: handover obligations, documentation, and a notice period that lets you move the work elsewhere without a cliff.
Red flags during the sales process
- The people on the sales call will not be the people building the system, and you cannot meet the actual engineers before signing.
- Every problem gets the same answer, usually a chatbot or an agent, before anyone has looked at your data.
- No mention of evaluation. A serious AI team will ask how you will measure whether the output is good enough, and propose a test set early.
- Case studies with impressive percentages but no way to verify them, no reference you can call, and no detail about what was actually built.
- Reluctance to work in your repository or to put IP assignment in writing.
- A quote produced within a day for a project nobody has scoped, or a time-and-materials contract with no cap and no milestones.
- Very large teams proposed for a small first phase. A good first phase for most AI features needs two or three senior people, not ten.
A scoring table you can actually use
Score each shortlisted vendor from 1 to 5 on each criterion, multiply by the weight, and add up. Adjust the weights for your situation. A regulated healthcare company should weight data handling more heavily, and a seed-stage startup might weight cost higher.
| Criterion | Weight | What a 5 looks like |
|---|---|---|
| Relevant AI delivery experience | 20% | Shipped comparable systems to production; can walk through architecture and failure modes |
| Seniority of the actual team | 20% | Named senior engineers you have interviewed, committed for the duration |
| Contract and IP terms | 15% | Full assignment, your repo, clear subcontracting and exit clauses |
| Total cost for phase one | 15% | Fixed or capped price with defined deliverables, plus a run-cost estimate |
| Working-hours overlap | 10% | At least 2–4 hours daily, all year, including DST changes |
| Data protection and security | 10% | DPA available, subprocessors listed, EU or local residency options where needed |
| Communication quality | 10% | Clear written updates, sensible questions in the first call, references you can contact |
Run a small paid discovery or pilot with your top one or two candidates before committing to a large engagement. Two weeks of real work tells you more than any amount of proposal reading.
For transparency: CoreNexes is one of the vendors you might put through this process. We work with clients in the US, UK, EU and Australia with agreed overlap hours, work in the client's repository, and transfer full source ownership to the client. We would rather be scored on the table above than chosen on location alone.
Questions we get asked
- Is nearshore AI development better than offshore?
- Not inherently. Nearshore usually gives more overlap at a higher rate, offshore gives lower rates with less overlap. Team seniority, contract terms and communication habits have a bigger effect on outcomes than location.
- How much timezone overlap do I need with an AI development partner?
- Two to four hours a day is enough for most projects if the partner communicates well in writing. Below two hours, debugging and decision-making slow down noticeably.
- Who owns the code when I outsource AI development?
- Only you, if the contract says so. Insist on an explicit assignment of all work product, including prompts, evaluation data and fine-tuned models, and a license to any pre-existing vendor components.
- Should I start with a pilot project?
- Yes. A short, paid, fixed-scope discovery or pilot lets you assess the real team, their engineering habits and their communication before committing to a larger contract.