Turing Alternative
A named Belgrade team composed as a group, not three separately matched individuals: one CET timezone, one monthly figure, and a delivery lead accountable for the release.
Reviewed by Igor Gazivoda, Co-founder & CEO of StepTo · Published · Updated
Which Turing Alternative Suits Work That Outlives the Match?
StepTo is a direct alternative to Turing when the engagement is measured in years rather than a quarter. We run dedicated teams in Serbia (CET) from Belgrade since 2014, with a 15-20 person engineering organization. Three engineers cost a fixed $15K-25K/month, start inside 2-3 weeks, and stay for the life of the engagement, all in one timezone so design decisions happen in a conversation.
Turing applies algorithmic matching to a very large global network of vetted remote developers. The technology works: for a rare or specific skill, searching a network that size will surface a qualified person faster than any small company can, and that is a real advantage we cannot match.
The limitation is not accuracy, it is scope. A match is a prediction made from evidence that exists at selection time: skills, history, availability, interview signal. What decides whether an engagement still works in month eighteen, judgement about your particular domain, willingness to push back on a bad architectural call, whether three engineers trust each other enough to disagree productively, is not in that evidence, because none of it has happened yet. Match three roles separately and you get three individually excellent answers to three separate questions.
We select for the group instead, put a delivery lead in charge of what it ships, and keep the same people on the product for years. Below: what that changes for cost, continuity and AI work specifically, and where searching a huge network is plainly the better move.
What Does Each Model Optimise For?
Turing optimises for selection speed; StepTo optimises for long-run team performance, and the table below includes the row where matching wins.
| Factor | Turing (marketplace) | StepTo Dedicated Team |
|---|---|---|
| Finding a rare skill fast | Algorithmic search across a very large global network, in days | A Belgrade bench: strong in StepTo's stacks, no help if the skill is exotic |
| What the selection optimises for | Fit between a role description and an available profile | Fit between a person and a group they will work in for years |
| Who is accountable in month six | Each developer for their own scope; the whole is yours to run | A StepTo delivery lead answerable for what the team ships |
| How the roster changes over time | Matching is per-role and per-availability, so composition can shift | Named engineers who stay: StepTo's average tenure is 2.5+ years |
| Where the engineers sit | Global pool; your developers may span many time zones | All CET, one Belgrade office, overlapping the EU, UK and US East Coast |
| How you are billed | ~$50-100+/hr per developer, varying with seniority and utilisation | $15K-25K/month for three engineers, agreed before work starts |
Why Do Companies Look for Turing Alternatives?
Seldom because a match was wrong. Usually because the engagement outlasted what the match was optimised for. Here's what StepTo does differently for teams evaluating Turing.
A Match Score Is a Prediction About Week One
Matching models are trained on what can be observed at selection time: skills, history, availability, interview signal. The things that decide whether an engagement works in month eighteen, judgement about your domain, willingness to argue about architecture, whether three people trust each other, are not in the training data because they do not exist yet.
Individually Optimal, Collectively Random
Match three roles independently and you get three strong individual results and no guarantee about the combination. Nothing in the process asks whether these particular people share a testing philosophy or will agree on a service boundary, because the algorithm was never given the group as the unit. StepTo selects for the group, not just the role.
The Pool Is Global, So Your Timezone Is an Outcome
Drawing from developers worldwide is exactly why a rare skill can be found quickly. It also means working hours are decided by who matched rather than by you, and three separate matches can land in three regions with no hour they all share.
Budgets Move With Utilisation
Per-developer hourly billing tracks reality closely, which is a virtue, and it means the monthly number moves with seniority mix and hours worked. Forecasting a year of spend across a multi-developer engagement becomes an estimate rather than a figure.
Context Expires With the Engagement
When a developer's engagement ends they move to another client, taking with them the reasons behind decisions that are not written down anywhere: why that queue exists, what broke last time someone touched billing, which customer the odd edge case is for. StepTo's engineers stay, so that context stays with them.
Someone Internal Becomes the Manager
Marketplaces supply people, not a delivery structure, so performance conversations, sequencing and integration land on your engineering leads. That is a real job, and it is usually taken on by whoever you could least afford to stop writing code. StepTo assigns a delivery lead to carry that job instead.
How Does Hourly Billing Compare With a Fixed Monthly Fee?
At 3 full-time equivalents, Turing's hourly billing moves with utilisation while a fixed monthly fee does not, and the breakdown below shows what happens to each number.
Three matched developers (~$75/hr avg, 160 hrs each)
A snapshot, not a forecast: per-hour billing moves with seniority mix and hours actually worked, and the figure omits the internal engineering time spent coordinating three people nobody else is accountable for.
Monthly
~$36,000/month
Annual
~$432,000/year
One StepTo team (3 devs)
Agreed before work starts and unchanged by utilisation. Delivery management, HR, career development and equipment sit inside the figure rather than beside it.
Monthly
$15K-25K/month
Annual
$180K-300K/year
Difference
Roughly 40-60% on equivalent headcount and seniority. The gap narrows if you staff more junior developers and widens once you price the management time.
Monthly
$11K-21K+/month
Annual
$132K-252K+/year
Read this before quoting the numbers: the marketplace column uses an illustrative blended ~$75/hr for a mixed-seniority trio, drawn from publicly available information as of 2026 rather than from the provider. Rates vary widely, and a junior-weighted team would land well below it. The durable difference is not the gap but the variance: one column moves with utilisation and seniority drift, the other does not.
What Does a Matching Algorithm Miss?
Any selection process, algorithmic or human, works from evidence that exists before the work starts: what someone has built, what they know, when they are free, how they performed in an assessment. Matching at scale does this exceptionally well, and the bigger the network the better the prediction gets. There is no criticism of the method here.
The constraint is what that evidence covers. It is a strong basis for predicting the first month and a weak one for predicting the eighteenth, because the qualities that matter later have not been generated yet: whether this engineer will develop good instincts about your specific domain, whether they will argue when a plan is wrong, whether they and the two people beside them will build enough mutual trust to disagree without it becoming your problem to arbitrate.
There is a second gap, which is that the algorithm is given one role at a time. Three independent matches produce three individually optimal answers and say nothing about the combination, so nobody has asked whether these particular people share a view on testing or will converge on a service boundary. Nor is anyone accountable for the answer. Each contractor owns their scope; the whole belongs to whoever on your side is senior enough to hold it, which is usually the person you could least afford to stop writing code.
Our approach is smaller and slower and covers exactly that gap. We select for the group rather than the role, using engineers who are our employees and already share review standards. A delivery lead owns whether the release lands. With 2.5+ years of average tenure and consistently low turnover, the undocumented knowledge, why that queue exists, what broke last time someone touched billing, stays in the room instead of expiring when an engagement ends.
So the comparison is not speed against quality. It is a process optimised for finding the right individual quickly against one optimised for how a specific group performs over years. If you need a rare skill this month, the first wins outright. If you are staffing a product for the next three years, the qualities that decide the outcome are the ones no profile can contain.
Why Is StepTo the Right Turing Alternative?
StepTo is the right Turing alternative when you are optimising for the eighteenth month instead of the first, and these are the things that compound over that period.
The Undocumented Reasons Stay in the Room
At StepTo, most of what makes an engineer valuable on a mature codebase is unwritten: why that queue exists, what broke last time someone touched billing, which customer the strange edge case is for. Keeping the same people means never re-deriving it.
A Number You Can Put in a Budget
One monthly figure agreed before work starts, unaffected by utilisation or seniority drift, with management, HR and equipment inside it. Annual planning becomes arithmetic rather than an estimate with error bars.
Composed as a Group, Not Assembled From Matches
StepTo chooses people who will work well with each other as well as with your codebase, then presents them together. Candidate profiles arrive within days and the team is contracted and productive inside three weeks.
Developers Who Stay for Years
Engineers stay on client engagements an average of 2.5+ years, and they are StepTo employees rather than contractors between placements, so there is no roll-off point at which your context walks out.
Timezone Chosen, Not Inherited
Everyone works CET/CEST from Belgrade, the same day as Germany, France and Scandinavia, with 3-4 hours of afternoon overlap with the US East Coast. Nobody has to be awake at an unreasonable hour for a design discussion.
Someone Other Than You Runs the Team
Delivery leadership is part of the price. A delivery lead handles performance, sequencing, coordination and escalation. That role has no equivalent in a matching model, which is why the job silently transfers to your best internal engineer.
How Do We Select Without an Algorithm?
Four steps built around the things a profile cannot tell you
A Human Reads the Code
Every candidate works through a domain-specific assessment on your stack, covering architecture patterns, data structures and hands-on tasks, and a senior engineer reads the result rather than scoring it against a rubric.
Deliverable: A technical scorecard written by someone who could do the work
We Test for Disagreement
Candidates walk through real production code with our senior engineers and are pushed on their choices. What we are looking for is whether they can hold a position under pressure and change it when the argument is better, which no profile predicts.
Deliverable: Code quality rating and communication assessment
You Meet Them Before They Are Yours
You run a 60-90 minute technical interview and make the final call. Nobody is allocated to your product by a process you did not participate in, and nobody joins without your explicit yes.
Deliverable: Your go/no-go on every individual
Two Weeks to Test the Prediction
Selection is a hypothesis whoever makes it, algorithm or human, so the first two weeks run as a structured trial. If the fit is wrong for any reason we replace the engineer at our cost.
Deliverable: Free replacement if the hypothesis fails
How Do Teams Decide Between StepTo and Turing?
How is StepTo different from Turing?
Turing applies algorithmic matching to a very large global network to find individual developers, billed per developer per hour. It is genuinely good at that, and for a rare skill it will beat us on speed. StepTo does something narrower: we compose a small group of our own employees around one product, price it as a single monthly figure, and put a delivery lead in charge of what it ships. The distinction is what each process optimises for. Matching optimises the fit between a role and a profile at selection time. We optimise for how a specific set of people perform together over years, which is not a property any profile contains.
When is algorithmic matching clearly the better tool?
When the binding constraint is finding the skill at all. If you need a Rust engineer with payments-protocol experience or a specialist in an unusual ML framework, searching a very large global network will find them faster than any 15-20 person company in Belgrade, and we would say so rather than pretend otherwise. Matching is also the right call for genuinely short engagements, where nothing has time to compound and the coordination overhead never materialises.
What does StepTo cost compared to Turing?
Marketplace engagements are billed per developer per hour, commonly around $50-100+ depending on seniority and stack (approximate, from publicly available information as of 2026). Three developers at a mid-range blended rate is roughly $36,000/month. Three StepTo engineers are $15,000-25,000/month, agreed before work starts and unaffected by utilisation, with management, HR and equipment included, roughly a 40-60% difference on equivalent headcount. Compare against the seniority mix you would actually staff, because a junior-weighted marketplace team would come in well under the figure above.
Can StepTo provide AI and machine learning engineers?
Yes, across LLM integration, RAG systems, data engineering and MLOps alongside full-stack web and mobile. The relevant difference for AI work specifically is that most of it is not modelling. It is retrieval quality, evaluation harnesses, data plumbing and the slow business of finding out which failure modes matter for your users, all of which depends on domain knowledge accumulated over months. A specialist matched for a quarter is optimised for the modelling part; a team that stays is optimised for the rest. See our AI & Automation Solutions and Hire AI Developers pages.
What does a two-week trial add if the vetting is already rigorous?
Vetting predicts, a trial observes. A 2-week trial adds honesty about what selection can and cannot know. Every screening process, algorithmic or human, is making a prediction from evidence that exists before the person has touched your codebase, and predictions are wrong sometimes. So we structure the first two weeks as a trial and replace the engineer at our cost if the fit is wrong. Ahead of that, candidate profiles arrive 3-5 business days after your requirements call, interviews run in week two, and the team is contracted and onboarding in week three.
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Staffing for Years, Not for a Quarter?
StepTo comes back with a named team composition, a monthly figure and a start date. Tell us what the product needs to become. If the real constraint is one rare skill, we will tell you to search a bigger network.
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