Comparison
Devlyn vs Indian staff augmentation: precise AI roles or broad developer bench?
Traditional staff augmentation gives you a broad, cost-effective offshore bench for capacity and long-running team extension. Devlyn is built differently: one named AI role, role ownership, public pricing, and proof in your repo before you continue.
Choose by operating model.
Choose Devlyn when you want an AI-native engineer matched to one named production role, with public pricing, a 48-hour shortlist, and trial proof in your repo. Choose broad staff augmentation when you mainly need cost-effective capacity, many commodity roles, or long-running team extension, and AI specialization is not the deciding factor.
How the two models actually differ
Same goal, different operating models.
A fair, model-based comparison. Descriptions of traditional Indian staff augmentation reflect how that hiring model generally works; confirm current terms with the provider.
| Dimension | Devlyn | Broad staff augmentation |
|---|---|---|
| Pricing model | Public monthly ladder: $2,500 / $3,500 / $4,500 per role level. | Typically price-led and rate-card based, often lower per seat and quoted by the vendor for capacity. |
| How talent is matched | Role-diagnosis call assigns one of eight AI roles, then 2-3 vetted profiles in 48 hours. | Resources are allocated from a broad bench against a requirements list, often with several candidates per seat. |
| Specialization | Narrow: production AI engineering and data science only. | Broad: many stacks, seniority levels, and commodity roles across a large bench. |
| Trial / proof of fit | Two-week paid trial in your repo with inspectable AI artifacts. | Often interview-and-resume based with a ramp period; real-environment trial varies by vendor. |
| Contract / lock-in | Month-to-month after trial, free replacement, no conversion fee. | Often longer engagements or minimum commitments suited to sustained team extension; confirm terms. |
| Who it is best for | Teams needing one precise, ownable AI role. | Teams needing broad capacity, many roles, or long-running team extension at lower per-seat cost. |
When broad staff augmentation is the better choice
- You need many roles or commodity capacity rather than one precise AI specialty.
- Per-seat cost is the deciding factor and you can manage ramp internally.
- You are extending an existing team for a long, steady workload.
- The work is general engineering, QA, or maintenance rather than production AI.
When Devlyn is the better choice
- You want one AI-native engineer who owns a named role end to end, not a seat on a bench.
- You want the price published before the call rather than negotiated on a rate card.
- You want fit proven by real AI work in your repo, not by resumes and interviews.
- You want month-to-month flexibility with free replacement, not a long minimum commitment.
Buyer checklist
Questions to ask before buying broad staff augmentation
- Is this resource dedicated and accountable for a role, or a seat allocated to my project?
- Has the person shipped production AI work, not just general development?
- Can we trial the person in our actual repo before committing, and for how long?
- What is the minimum commitment, notice period, or ramp-down cost?
- How are replacements handled, and does the clock and quality reset when someone rotates off?
Cost, risk, and process
Where the two models diverge in practice.
On cost transparency, broad staff augmentation is usually price-led with vendor rate cards, while Devlyn publishes a fixed monthly ladder per role level. On risk ownership, staff augmentation often validates through resumes, interviews, and a ramp period, whereas Devlyn carries fit risk through a paid trial in your repo with free replacement. On process speed, a bench can staff many seats quickly for capacity, but Devlyn front-loads a role-diagnosis call so the 48-hour shortlist is one accountable AI role rather than several interchangeable resources.
FAQ
Comparison questions.
Is Devlyn more expensive than offshore staff augmentation?
Often the per-seat rate is lower with broad staff augmentation. Devlyn competes on AI-role precision, accountability, and trial proof rather than on being the cheapest seat.
When does broad staff augmentation win?
When you need capacity across many roles, commodity work, or long-running team extension and AI specialization is not the deciding factor.
How is role ownership different from a staff-aug seat?
Devlyn matches one engineer to one named AI role with a defined first-14-day proof. A staff-aug seat is usually capacity allocated against a requirements list.
Can Devlyn still extend our team long term?
Yes, month-to-month after the trial, with free replacement and no long lock-in. The difference is precision per role, not volume per bench.
Next step
Scope the AI role in 30 minutes.
Bring the bottleneck. We will map it to one of eight AI roles, send a vetted shortlist in 48 hours, and prove fit with a two-week paid trial in your codebase.