If you need to get an AI system into production and your own team does not have the time or the experience to do it alone, you will usually choose between three ways of buying help: forward deployed engineering, consulting, and staff augmentation. All three put outside people on your problem. They differ in who is responsible for the result, where the work ends up living, and what you hold when the engagement is over.
This article defines each model, compares them side by side, and explains when each one is the right choice. We are an AI forward deployment company, so we have an obvious preference. We have tried to be fair anyway, because the wrong model for your situation will cost you more than the wrong vendor.
The three models
Forward deployed engineering
Forward deployed engineers embed with your team and build one production system inside your own cloud account. The engagement is scoped around a single workflow and a measurable result, and it ends with your team owning the code, the infrastructure and the runbooks. The partner is accountable for shipping a working system, not for hours worked or documents delivered.
At Solvren, that means a fixed-scope deployment of 6 to 8 weeks in your AWS, Azure or GCP account, after a free 30-minute call and a validated ideas demo within 5 days. The deployment starts once the agreement is signed. See how we deploy for the stages.
Consulting
A consulting engagement gives you analysis and advice: an assessment of where AI fits, a strategy, an architecture recommendation, a vendor selection, or a roadmap. The deliverable is usually a document or a set of recommendations, and your team (or another vendor) does the build. Good consulting is valuable when the question is genuinely open and you need an outside view before committing money.
Staff augmentation
Staff augmentation adds individual engineers or data scientists to your team, usually billed by time. They work under your direction, on your backlog, using your processes. You get capacity without a long hiring cycle, but you also keep full responsibility for what gets built, how it is designed and whether it works.
Side-by-side comparison
| Forward deployed engineering | Consulting | Staff augmentation | |
|---|---|---|---|
| Who owns the outcome | The partner owns delivery of a working system; you own the business decision | You own the outcome; the consultant owns the quality of the advice | You own the outcome and direct the work |
| Where the system runs | Your own cloud account, from the first day | Usually nowhere yet; the build comes later | Wherever your team builds it |
| What you own at the end | A production system, its code and infrastructure, and runbooks your team can operate | A strategy, assessment or architecture document | Whatever the team built, plus the knowledge that leaves with the contractors |
| Typical timeline | Weeks per workflow (6 to 8 at Solvren, after a 5-day validated ideas demo) | Weeks for an assessment; longer for broad programs | Open-ended; months is common |
| How you pay | Fixed scope and fixed timeline per deployment, then optional monthly support | Fixed fee per study or time and materials | Time and materials, per person |
| Best when | You know the workflow, need it in production, and must keep data in your own environment | The problem is undefined or you need an independent view before investing | You have a clear plan and strong technical leadership but not enough hands |
What the differences mean in practice
Accountability for a working system
The biggest difference is who carries the risk that the system never reaches production. With staff augmentation, that risk stays with you: contractors can do excellent work on a project that still stalls because nobody owns the path through security review, data access and launch. With consulting, the risk sits after the engagement, when the plan meets your real infrastructure.
A forward deployment is scoped so that the partner cannot finish without a working system. That changes behavior: data access, identity and access management, and security approvals get handled at the start, because they block delivery.
Where the work lives
For regulated teams, location matters as much as capability. When the system is built in your own cloud account from the start, your security team reviews infrastructure it already controls, using models and services it has already approved. Nothing has to be migrated out of a vendor environment later, and your data does not pass through the partner’s infrastructure.
Consulting rarely touches infrastructure at all. Staff augmentation can work in your environment, but only if your team sets that up and enforces it.
What you are left with
At the end of a consulting engagement you have a decision and a plan. At the end of a staff augmentation contract you have whatever was built, but the people who understood it may be gone. A forward deployment is designed around handover: the code is in your repositories, the infrastructure is defined in your account, and the runbooks explain how to operate, monitor and change the system. Our case studies, such as the AI skills registry and the fast LLM router, show what that looks like for specific workflows.
Cost structure
Time-and-materials pricing ties cost to effort, which is fair when scope is genuinely unknown and frustrating when it drifts. Fixed-scope pricing ties cost to an agreed result, which forces both sides to define it up front. Neither is cheaper by default. The useful question is which structure puts the cost of uncertainty on the party best able to manage it.
When each model is the right choice
Choose consulting when the question is still open
If you do not yet know which workflow matters most, whether AI is the right tool, or how a decision will affect your organization, start with advice rather than a build. An assessment or readiness audit is cheaper than building the wrong thing. Consulting is also the right call when you need an independent opinion for a board, a regulator or an internal investment decision, where the advisor should not be the one who profits from the build.
Choose staff augmentation when you have the plan and the leadership
If you already have a technical lead who knows what to build, an approved architecture and a backlog, and your bottleneck is simply people, staff augmentation is often the most efficient option. You keep full control of design and priorities, and you can scale the team up or down as the work changes. It works less well when nobody internal has shipped a production AI system before, because the contractors will inherit that gap.
Choose forward deployment when you need one workflow in production
Forward deployment fits when you can name the workflow, you need it running on real data rather than in a demo, and your security and compliance requirements mean it has to live in your own environment. It suits mid-market teams in biotech, healthcare, defense, legal and fintech that do not have a large internal AI platform group but do have strict rules about where data goes. It is a poor fit for open-ended research, for programs that need a large standing team for a long time, or for organizations that want a vendor to run the system for them indefinitely.
Combining models
These models are not mutually exclusive. A common sequence is a short assessment to pick the workflow, a forward deployment to put it into production, then your own team or monthly support to run and extend it.
Questions to settle before you pick
- Can you name the one workflow you want in production, and the number that should change?
- Does the system need to run in your own cloud account, under controls your auditors already review?
- Do you have someone internal who will own the system after launch?
- Are you buying a decision, extra capacity, or a working system?
If the honest answer to the last question is “a working system”, read our guide to choosing a forward deployment partner, check the FAQ, or get in touch to book a free 30-minute call.