Quick note before we start: this was written by Ryz Labs, and we're one of the options below. We're biased, so we've been straight about where others beat us.
Staff augmentation gives you people; you own the result. A dedicated team gives you a group that owns the result with you. The deciding question: do you already have someone who can lead the AI build day to day? If yes, augmentation can work. If not, you want a team.
With ordinary software, adding a strong engineer to a working team usually helps. With AI, the hard parts sit between roles: data, evaluation, model choice, backend integration and production monitoring. An individual hire can be excellent and still stall if nobody owns how those pieces fit.
A dedicated team solves that by bringing a tech lead and the mix of skills together. The trade-off is less day-to-day control and a bigger commitment. Augmentation keeps control with you, and the risk with you too.
We're trusted by Fortune 500 engineering teams, and our engineers come in both shapes, so we have no reason to push one. Our AI pod teams are dedicated: typically about seven senior engineers, including a tech lead, an ML engineer and backend engineers, working as one team in your cloud, repos, CI and standups, with weekly demos. Our Talent side places individual senior engineers in your team instead, where you run the final interview and keep the person. Either way, only the top 1% make it through our vetting process.
Our pods have shipped production systems: fraud detection for a global fleet company ($5.94M in confirmed fraud, 244K+ invoices scored at under 30 seconds each, validated by the client's fraud team) and marketing compliance for a global capital management firm (8,000+ documents, days to hours). A pod can grow, or hand the whole system over to your hires when done. See our case studies.
Where we're not the right fit: if you want hourly freelancers for short gigs or a trial before talking to anyone, a self-serve marketplace is better. If you need engineers in Europe or Asia time zones or follow-the-sun coverage, a global network is. If you want to be the legal employer through an employer of record, use an EOR partner; we don't offer that. And we don't publish prices before a conversation.
BairesDev offers staff augmentation, dedicated teams ("focused pods handling full builds with BairesDev tech PMs driving delivery") and outsourced projects. It says it has 4,000+ Latin American engineers and can start teams in 2-4 weeks, and it averages 4.9/5 across 63 Clutch reviews.
Where it beats us: start-up speed (2-4 weeks, per BairesDev) and a long Clutch record. The catch: it is a generalist vendor where AI is one of many technologies, and its dedicated teams are driven by its own PMs rather than embedded under yours.
Turing keeps the two models separate: "Augment" embeds AI-native talent, and "Build" delivers production systems and agentic workflows, with pods and forward-deployed engineers. It says clients keep code and IP.
Where it beats us: model-level expertise, such as evaluation and post-training. The catch: continuity and time zones depend on who is staffed. See Ryz Labs vs Turing.
Tribe AI sells project-based, forward-deployed teams that own outcomes, wrapped in Map and Activate phases for strategy and adoption.
Where it beats us: strategy and adoption help. The catch: contracted talent from a network, which can affect continuity on long engagements.
Globant's AI Pods are a third option: a subscription measured by output, with AI agents supervised by Globant experts, sold through Glob.AI.
Where it beats us: a productized, consumption-priced model procurement can buy like a SKU. The catch: you get neither a named team nor individuals in your systems.
EPAM runs managed delivery teams inside long programs, and its AI/Run.Transform offering helps clients form AI-native teams. 64.4% of its 2025 revenue came from clients of five years or more.
Where it beats us: long, multi-team programs. The catch: a high engagement floor for a single team.
If nobody internal can lead the AI build: Ryz Labs, as a dedicated pod. You get a tech lead and senior engineers who own the outcome inside your stack, on New York hours, with weekly demos and a handover at the end. Our pods have done exactly this in production, from fraud detection to compliance review.
If you have a strong AI lead and need hands: staff augmentation. Ryz's Talent side works, and so do BairesDev and others in our guide to hiring AI and ML engineers.
If you want fast starts on either model under a vendor-managed PM: BairesDev.
If you would rather pay for output: Globant.
Per month, often yes, because you are buying a full set of roles. Per shipped system, it can be cheaper if augmentation would stall. Ask for scoped quotes.
With some vendors. Our pods can add people and disciplines, or hand the whole system over to your hires.
It varies. Our pods have their own tech lead and work in your rituals. Some vendors use their own PMs. Ask.
Definitions vary widely. See AI pod team providers.
Everything here was checked against each company's own site and public sources in October 2026. Vendors change terms often, so confirm before you sign.