Best Places to Hire Forward Deployed Engineers (2026)

A forward deployed engineer (FDE) is an engineer who sits inside your team, learns the messy workflow, and ships production code against your real systems. The role came out of Palantir and was picked up by the frontier AI labs. Now buyers want one, and a full in-house search is slow.

Hiring a full-time FDE in-house means a long search and a ramp-up before the first deployment ships. A second route has grown up: firms that supply forward deployed engineers as a service. This guide compares 8 of the best places to hire forward deployed engineers through a firm or a network, with the price each one publishes, what it is good at and who it fits. Every fact comes from the firm's own site, checked on 2026-11-09. We left out the big consultancies, the AI labs' own deployment units and the recruiting platforms that already dominate search results.

Best places to hire forward deployed engineers: a brief overview

• AY Automate: Best overall for a senior engineer placed in your team in 2 to 4 weeks, from $3K a month, with a 90-day replacement guarantee.
• Plank: Best for AI product companies that want trained FDE pods plus QA pods from one vendor.
• FDE (forwarddeployedengineers.agency): Best for a fixed-fee, six-week sprint that takes one AI workflow to production.
• NeuralChainAI: Best for regulated teams that want a compliance-scoped FDE and a stated 7 to 14 day embed target.
• Gigabit: Best for the lowest published per-engineer rate with a short trial.
• Siblings Software: Best for buyers who want published monthly bands for one FDE, a pair or a pod.
• Alephic: Best for marketing and brand teams that want AI software built from their own expertise.
• Tribe AI: Best for enterprises that want forward-deployed engineers paired with industry experts.

1. AY Automate, best overall for a forward deployed engineer who ships in weeks

AY Automate places a senior engineer inside your team, and that engineer directs a fleet of AI agents to cover more ground than one person normally could. The offer is on our forward deployed engineers page. You get production code, not a recommendation deck. Placement takes 2 to 4 weeks, and the engineer comes with a 90-day replacement guarantee.

The work follows one loop. Week one is an audit where the engineer sits with the people who do the job and maps how the work really happens. Weeks one and two build a golden set of real cases with correct answers, so quality is measured before anything touches production. Week two and three deploy on your existing systems, sandbox first, and autonomy rises step by step. After that the engineer keeps iterating and reports the outcome in terms a technical lead and a VP both trust. The stack is Claude Code, MCP connections to your tools, Anthropic managed agents, Cursor and n8n, and AY is a formal Claude partner. If you want the Claude Code angle specifically, see our Claude Code development agency page.

Proof is published on our case studies page. A legal and compliance automation saved a client 10K euros in fines. A marketing operations build automated 95% of tasks. A customer support build resolves tickets around the clock. Elie Salame, COO of Adstronaut.io, says of the team: "The team is super fast, sometimes we had to slow them down. We managed to scale the company without investing into hiring." Roald Larsen of Untaylored and Connor Miller of Uniworx are two of the technical leads our engineers have worked inside directly.

Why it's on the list

• AI-native engineers embedded in your team, directing a fleet of AI agents, build and ship production agents and automation against your real systems.
• "Live in 10 days, not 10 weeks." The engineer joins your Slack and repo and ships the first working automation in week two (how we work).
• Monthly retainer: most teams stay with us on a monthly retainer after launch (FAQ). Engineers start at $3K a month, with a 90-day replacement guarantee.
• Proof: the EdTech platform case study, one platform built from scratch that replaced three disconnected tools.

Key features

• Senior engineer placed in 2 to 4 weeks, against 3 to 6 months for a comparable US hire
• 90-day replacement guarantee
• Audit, evals, deployment and iteration loop with a measured golden set
• Free 15-minute call, plus a free AI hiring audit that names what an engineer could ship first

Best for

• Founders and operators who need production agents and automation shipped in weeks, with one accountable engineer

Pricing

• A single embedded FDE starts from $3K a month, full-time in your sprint
• Shorter or narrower engagements, such as a single agent or workflow, are quoted after a discovery call

Our hire AI developers page shows the same placement from one engineer. Our guide on how to hire a forward deployed engineer covers recruiting in-house.

2. Plank, best for AI product companies that want trained FDE pods

Plank sells FDE as a service. Its model is to train engineers in its own AI Accelerator, then embed them in pods with teammates they have already shipped with. The site lists three lines of work: AI-native product engineering, forward-deployed AI engineering and on-demand QA pods, where the QA pods return bugs, repro steps and a merge verdict within 24 hours.

Plank says it has worked with more than 50 AI companies, and names clients such as Procore, OpenVPN, Near AI and Entelligence. Its engineers work with Claude Code and Codex and stay vendor-neutral across OpenAI, Anthropic and open models.

Why it's on the list

• Trains engineers in its own AI Accelerator and embeds them in pods for AI product engineering, forward-deployed AI engineering and on-demand QA.
• Fits AI startups and scale-ups that also deploy into their own customers' systems.
• Pricing not published; pod engagements quoted on a call.
• Proof: it names Procore, where it ships production AI, and OpenVPN, where it is embedded (Plank).

Best for

• Startups and scale-ups shipping AI products that also need deployment into their own customers' systems
• Teams that want build, deploy and QA from one vendor

Pricing

• Not published. Book a call.

3. FDE (forwarddeployedengineers.agency), best for a fixed-fee six-week sprint

FDE is a small team that takes one AI workflow from prototype to production in six weeks, then hands back the code, the evals and the runbook. Its pricing page is the clearest in this list. An AI readiness sprint runs $8K to $15K over two weeks, a six-week production build runs $25K to $90K, an embedded engineer on retainer runs $10K to $18K per month with a quarterly minimum, and regulated-industry deployments run $55K to $120K or more. IP transfers to you, and the first pull request lands within 7 days.

It says it takes on three new engagements per quarter, so capacity is limited.

Why it's on the list

• Takes one AI workflow from prototype to production in six weeks, then hands back the code, evals and runbook.
• Fits teams with one clear workflow and a fixed budget, such as B2B SaaS, financial services and healthcare.
• Pricing: entry $8K to $15K, six-week build $25K to $90K, retainer $10K to $18K a month, regulated deployments $55K to $120K+ (pricing).
• No public case study found.

Best for

• Teams with one clear workflow and a fixed budget

Pricing

• $8K minimum engagement; retainer $10K to $18K per month

4. NeuralChainAI, best for compliance-scoped forward deployed engineers

NeuralChainAI offers four engagement models: project-based, retainer, fractional FDE and compliance-scoped. Its stated target is a senior FDE embedded within 7 to 14 days, a scoped plan in 2 weeks, staging in 2 to 4 weeks and production in 8 to 12 weeks for typical work. The engineer stays through stabilization, then hands off runbooks and two to four weeks of side-by-side knowledge transfer.

The site covers SAML and OIDC integration, legacy databases and evaluation suites that catch drift and hallucinations.

Why it's on the list

• Offers project, retainer, fractional and compliance-scoped FDE engagements, with SAML and OIDC integration and evaluation suites.
• Fits regulated and security-heavy teams that want compliance in scope.
• Pricing not published; quoted upfront against a defined scope after a free 30-minute call.
• Proof: its case study on a BERT4Rec item recommender for a 10M-user e-commerce catalogue, served in under 70ms in production (NeuralChainAI case studies).

Best for

• Regulated teams that need compliance in scope
• Buyers who want a fractional FDE

Pricing

• Not published. It says pricing is quoted upfront against a defined scope, after a free 30-minute call with a mutual NDA.

5. Gigabit, best for the lowest published per-engineer rate

Gigabit embeds a small pod of senior AI engineers in your sprint board, Slack and code review. The pod can include AI and agent engineers, ML and data engineers, MLOps engineers and a delivery lead. Pricing is $3,000 to $7,500 per engineer per month, with a 2 to 4 week trial, no lock-in and 30 days notice. It says the pod assembles in two weeks, with first pull requests by day 5.

One case study on its page compares three embedded engineers at $207,600 a year with $600,000 for equivalent US hires. That is one client's numbers, not an average.

Why it's on the list

• Embeds a pod of senior AI, ML and MLOps engineers and a delivery lead in your sprint board, Slack and code review.
• Fits budget-aware teams that want a short trial.
• Pricing: $3,000 to $7,500 per engineer per month, with a 2 to 4 week trial and 30 days notice.
• Proof: its published case study compares three embedded engineers at $207,600 a year with $600,000 for equivalent US hires, which it calls one engagement and not an average (Gigabit).

Best for

• Teams that want a short trial and a low rate

Pricing

• $3,000 to $7,500 per engineer per month

6. Siblings Software, best for published bands on one FDE, a pair or a pod

Siblings Software sells FDEs through staff augmentation contracts led from Miami. Its page gives monthly bands: $9,500 to $14,000 for one embedded senior with a three-month minimum, $16,000 to $24,000 for a senior plus an integration engineer, and $24,000 to $38,000 for a three-to-four person pod with a four-month minimum. LLM API usage and tool licenses stay on your accounts.

It lists 12 to 18 business days to the first guarded production workflow.

Why it's on the list

• Supplies forward deployed AI engineers through staff augmentation contracts: one senior, a senior plus an integration engineer, or a three-to-four person pod.
• Fits buyers who want a named senior and a price band before the first call; led from Miami with engineers in Argentina.
• Pricing: $9,500 to $14,000 a month for one senior with a three-month minimum.
• Proof: its case studies hub lists 8 senior Go engineers embedded with NetApp platform teams (Siblings Software case studies).

Best for

• Buyers who want the price band before the first call

Pricing

• $9,500 to $14,000 per month for one senior, all-in

7. Alephic, best for marketing teams that want AI software built from their expertise

Alephic describes itself as forward-deployed engineering for marketing. Its engineers work next to your best marketers and sellers and turn their judgment into software you own. Typical jobs are a brand brain that encodes guidelines and tone, and production hardening for stuck AI pilots. An engagement runs in two-week sprints through intake, prototype, validate, harden and handoff, and you own the code and the data at the end.

Its customer stories name Amazon, Lands' End and EY.

Why it's on the list

• Builds AI software from your marketers' and sellers' expertise, in two-week sprints, with you owning the code and data.
• Fits marketing and brand organizations at larger companies.
• Pricing not published; sprint-based engagements.
• Proof: Lands' End reports double-digit growth on Amazon after Alephic built its catalog content system (Alephic customer story).

Best for

• Marketing and brand organizations at larger companies

Pricing

• Not published

8. Tribe AI, best for enterprise programs that pair engineers with industry experts

Tribe AI says it pairs industry expertise with forward-deployed engineers who own the problem end to end. Its three steps are map, build and activate. The build step puts engineers inside your organization, against your real systems, until the work runs in production. The activate step covers how your people and agents work, plus early wins and internal champions.

It states partnerships with the frontier labs and publishes no prices.

Why it's on the list

• Pairs forward-deployed engineers with industry experts in a map, build and activate model.
• Fits large organizations with a funded AI program and a change-management need.
• Pricing not published; start with a conversation.
• Proof: Sumo Logic cut root-cause analysis from hours to under a minute with a Claude-powered solution Tribe built (Tribe AI case studies).

Best for

• Large organizations with a funded AI program and a change-management need

Pricing

• Not published. Start a conversation.

How we ranked these firms

We scored every firm on five points, using only what each site publishes. Does the firm place engineers inside your team, or hand back a project? Does it publish a price? Does it state a time to start? Does it show proof, such as named clients or quotes? Do you keep the code, the evals and the runbook?

We did not use Clutch ratings or review counts. All eight pages were read on 2026-11-09. Firms that already hold the top search results for this term, the recruiting platforms and the large consultancies were left out on purpose.

How to choose where to hire a forward deployed engineer

Start with the problem. Pick one workflow where deployment is the bottleneck. If your product self-serves, you do not need an FDE yet.

Next, decide between a person and a pod. One senior engineer is enough for one workflow. A pod fits when discovery, integration and rollout run in parallel.

Compare price on the same basis. Some firms quote per engineer, some per sprint and some per pod. Siblings, for example, keeps LLM API costs on your accounts.

Check ownership. You want the code, the evals and the runbook in your repo at the end.

Ask how a bad match is handled. A replacement guarantee, a short trial or a 30-day notice period all limit your risk. Our comparison of a forward deployed engineer vs a consultant explains why the accountable-for-outcome model matters.

FAQ

What are the best places to hire forward deployed engineers?

Our pick is AY Automate for a senior engineer placed in 2 to 4 weeks from $3K a month with a 90-day replacement guarantee. Plank, FDE, NeuralChainAI, Gigabit, Siblings Software, Alephic and Tribe AI are the other firms covered here, each with a different model and price.

How much does it cost to hire a forward deployed engineer through a firm?

Published prices in this guide run from $3K a month at AY Automate and $3,000 to $7,500 per engineer per month at Gigabit, to $9,500 to $14,000 per month at Siblings Software and $10K to $18K per month at FDE. For the in-house side, see our forward deployed engineer salary guide.

How is an FDE different from a consultant or a contractor?

A consultant gives you a recommendation and a contractor fills a seat for billed hours. A forward deployed engineer works inside your team and owns a production outcome. Our forward deployed engineer vs consultant guide goes through the four models side by side.

How fast can I get a forward deployed engineer?

Stated windows in this guide are 2 to 4 weeks at AY Automate, 7 to 14 days at NeuralChainAI, about 2 weeks to assemble at Gigabit and 12 to 18 business days to a first guarded workflow at Siblings Software. A comparable US hire takes 3 to 6 months to start, per AY Automate's hire page.

When should I hire a forward deployed engineer instead of a developer?

Hire an FDE when deployment is what blocks you, for example a pilot that works in a demo but never reaches your operators or your customers. Hire a regular developer when the requirements are clear and you only need capacity. Our forward deployed engineers page lists when an FDE fits.

Who owns the code when the engagement ends?

It depends on the firm. FDE states that IP transfers to you, Alephic says you own the code and the data, and Siblings says you keep architecture direction and IP. Ask for this in writing before you start.