The Forward Deployed AI Engineer The AI FDE is the fastest-growing variant of the Forward Deployed Engineer role. As enterprises race to deploy AI, they need engineers who can take models from demo to production in customer environments. This guide covers everything you need to know. What Makes an AI FDE Different Traditional FDE AI FDE Core work Data platform deployment, integrations LLM deployment, RAG, AI agent building Tech stack Python, SQL, Spark, cloud Python, LangChain, vector DBs, model serving Customer ask "Help us use our data better" "Help us deploy AI that actually works" Key challenge Data quality, integration complexity Hallucinations, evaluation, cost management Comp premium Baseline FDE comp 10-20% premium over traditional FDE Who Is Hiring AI FDEs Tier 1: AI-Native Companies Company Role Comp Range Focus Anthropic FDE / Solutions Eng $250K-$600K Claude enterprise deployment OpenAI Solutions Eng / FDE $280K-$700K GPT deployment, fine-tuning Databricks AI FDE $250K-$440K Mosaic, MLflow, model training Scale AI FD AI Engineer $190K-$400K Data labeling, RLHF, evaluation Cohere FDE $150K-$280K Enterprise LLM deployment Tier 2: Platform Companies Adding AI FDE Company Role Focus Salesforce Agentforce FDE AI agent deployment Palantir FDSE (AIP) Palantir AIP deployment Snowflake AI FDE Cortex AI features Datadog ML Solutions Eng AI observability The AI FDE Tech Stack Must-Know LLM APIs: OpenAI, Anthropic, Google (Gemini), open-source (Llama, Mistral) RAG Frameworks: LangChain, LlamaIndex, Haystack Vector Databases: Pinecone, Weaviate, Chroma, pgvector Prompt Engineering: System prompts, few-shot, chain-of-thought Evaluation: Custom evals, LLM-as-judge, retrieval metrics (MRR, recall@k) Should Know Agent Frameworks: LangGraph, CrewAI, AutoGen Fine-Tuning: LoRA, QLoRA, PEFT Model Serving: vLLM, TGI, Triton, SageMaker endpoints Embeddings: Sentence transformers, OpenAI embeddings, Cohere embed Guardrails: Content filtering, PII detection, output validation Emerging Multi-modal AI: Vision + language models for document processing Voice AI: Real-time speech-to-text + LLM + text-to-speech AI Agents in Production: Tool use, function calling, autonomous workflows What AI FDE Deployments Actually Look Like Engagement 1: Enterprise RAG (Most Common) Customer: Fortune 500 financial services firm Problem: 500 analysts spending 2 hours/day searching internal documents Solution: Ingest 2M documents (PDFs, emails, reports) into vector database Build retrieval pipeline with hybrid search (BM25 + semantic) Deploy chat interface with citations and source linking Custom evaluation pipeline: retrieval accuracy, answer quality, hallucination rate Timeline: 8 weeks to production Result: 60% reduction in research time, 85% user satisfaction Engagement 2: AI Agent for Operations Customer: Manufacturing company Problem: Factory floor managers spending 3 hours/day on reporting and data entry Solution: Build AI agent that can query production databases via natural language Function calling for: inventory checks, quality reports, shift scheduling Guardrails to prevent data modification without human approval Slack integration for natural interaction Timeline: 6 weeks to pilot Challenge: Ensuring agent doesn't hallucinate production numbers Engagement 3: Customer Support Automation Customer: SaaS company with 50K monthly support tickets Problem: 70% of tickets are repetitive, L1 agents burning out Solution: Fine-tune model on historical ticket resolution data RAG over knowledge base and product documentation Confidence scoring — auto-resolve high-confidence, escalate low-confidence Human-in-the-loop review for edge cases Timeline: 10 weeks to production Result: 45% auto-resolution rate in month 1, 62% by month 3 Common Failure Modes (and How to Avoid Them) Failure Mode Root Cause Prevention Hallucinated answers No retrieval grounding, no guardrails Always use RAG, implement citation checking Poor retrieval quality Bad chunking, wrong embedding model Test chunking strategies, evaluate retrieval independently Cost explosion Sending too much context, no caching Implement prompt caching, optimize chunk selection Slow responses Large context windows, no streaming Stream responses, async processing, response caching Customer distrust No explainability, black-box answers Always show sources, confidence scores, human escalation path AI FDE Interview: What Is Different Standard FDE interview + these AI-specific components: AI System Design Round "Design a RAG system for a legal firm with 10M documents" "How would you build an AI agent that can query databases safely?" What they're looking for: Practical architecture, awareness of failure modes, evaluation strategy AI Technical Deep-Dive "Explain how retrieval-augmented generation works end to end" "What's the difference between fine-tuning and RAG? When do you use each?" "How do you evaluate an LLM application in production?" AI Case Study "A customer's RAG system is returning wrong answers 20% of the time. How do you debug this?" "The CEO wants to deploy an AI chatbot for their customers by next month. What do you do in week 1?" How to Prepare for AI FDE Roles 30-Day Plan Week 1: Build a RAG application end-to-end (document ingestion → retrieval → generation → evaluation) Week 2: Add an AI agent with function calling (database queries, API calls) Week 3: Deploy to cloud with proper monitoring (latency, cost, quality metrics) Week 4: Build an evaluation pipeline (retrieval quality, answer quality, hallucination detection) Portfolio Project Ideas Legal document Q&A — RAG over case law with citations Code review agent — AI that reviews PRs and suggests improvements Customer support bot — Train on your own documentation, measure resolution rate Data analyst agent — Natural language to SQL with guardrails Working as an AI FDE? Share what tools and patterns are actually working in production. The community needs real-world signal, not Twitter hype.

How to Become a Forward Deployed Engineer Whether you're a software engineer looking to pivot, a consultant wanting more technical depth, or a new grad targeting FDE roles — this is your complete roadmap. Path 1: SWE → FDE You have: Strong coding skills, system design experience You need: Client-facing skills, business acumen, comfort with ambiguity Steps: Start volunteering for customer-facing work at your current company. Join customer calls, shadow solutions engineers, present in design reviews. Build a demo project that solves a real business problem (not a toy project). Think: a dashboard that ingests messy data sources and produces actionable insights. Practice explaining technical concepts to non-technical people. Record yourself. Get feedback. Target SE or SE-adjacent roles first if direct FDE roles seem out of reach. Companies like Datadog, MongoDB, and HashiCorp have SE roles that build toward FDE skills. Apply to FDE roles emphasizing: your ability to ship fast, work independently, and communicate with stakeholders. Timeline: 3-6 months of preparation Resume positioning: Lead with projects where you solved customer/user problems Emphasize: cross-functional work, stakeholder communication, rapid prototyping De-emphasize: pure infrastructure work with no user interaction Path 2: Consultant/SA → FDE You have: Client skills, business understanding, presentation ability You need: Deeper coding skills, system design, production deployment experience Steps: Level up your coding. FDE interviews test real coding ability. Spend 2-3 months on LeetCode mediums and system design. Build a full-stack project end-to-end. Deploy it. Make it production-quality. FDE interviewers care that you can ship, not just design. Learn the FDE tech stack: Python, SQL, cloud (AWS/GCP), Docker, basic ML/AI concepts, API design. Create a POC that demonstrates data integration. FDEs constantly integrate messy data sources — show you can handle CSVs, APIs, databases, and make them work together. Leverage your consulting experience in applications. FDE hiring managers value someone who can walk into a room, understand the problem, and start building. Timeline: 3-6 months of technical preparation Path 3: New Grad → FDE You have: Fresh CS education, energy, willingness to travel You need: Portfolio projects, communication skills, understanding of business problems Steps: Internships matter enormously. Target Palantir's internship program ($60/hr, direct conversion to FDSE). Also: Databricks, Scale AI, Stripe. Build 2-3 portfolio projects that demonstrate: Data ingestion and transformation A user-facing dashboard or app Integration between multiple systems Practice the decomposition interview. This is the unique FDE interview format — you're given a vague business problem and must break it into technical components. Practice with our 50 Decomposition Problems. Join our community and network with working FDEs. Many companies hire through referrals. Don't overlook smaller companies. 58% of FDE roles are at companies with 11-200 employees. Less competition, faster growth. Timeline: Start preparing 6+ months before graduation The FDE Skill Matrix Rate yourself 1-5 on each. You need 3+ on all required skills. Required Skills Skill What "good enough" looks like Python Can build a data pipeline or API in a day SQL Comfortable with joins, CTEs, window functions System Design Can design a data ingestion + serving architecture Communication Can explain a technical decision to a VP of Sales Problem Decomposition Can break a vague business need into buildable components Cloud (AWS/GCP/Azure) Can deploy and manage services, understand networking basics Strongly Recommended Skill Why Docker/Kubernetes Most FDE deployments are containerized JavaScript/TypeScript For building demo UIs and dashboards ML/AI Fundamentals AI FDE is the fastest-growing subtype Data Engineering Spark, Airflow, or equivalent Git + CI/CD Professional deployment workflows Nice to Have Skill Context Terraform/IaC For infrastructure-heavy deployments Security clearance $30K-$80K salary premium for defense FDE roles Industry knowledge Healthcare (HIPAA), finance (SOX/PCI), defense (ITAR) FDE Interview Prep Checklist Solve 50+ LeetCode problems (focus on mediums, data processing patterns) Practice 10+ decomposition/case study problems Build and deploy a full-stack data project Prepare 5 STAR stories about stakeholder interaction Research target company's product deeply — use it if possible Prepare a 5-minute presentation on a technical topic Practice whiteboarding architecture diagrams Mock interview with someone in a customer-facing role Where to Apply (Ranked by New-Grad Friendliness) Palantir — Dedicated new grad FDSE pipeline. Structured program. Databricks — Growing fast, willing to train. Great equity. MongoDB — SE roles that function like FDEs. Remote-friendly. Datadog — Strong SE program, good mentorship. Scale AI — Small team, high impact. AI focus. HashiCorp — Remote, good work-life balance. Stripe — Selective but excellent training. Compensation Expectations by Path Entry Point Expected Starting Comp Time to Senior FDE New grad $150K-$200K TC 4-5 years SWE (2-4 YOE) $200K-$300K TC 2-3 years Consultant (3-5 YOE) $180K-$270K TC 2-4 years Senior SWE (5+ YOE) $280K-$400K+ TC 0-1 years Currently preparing for FDE interviews? Share your experience and ask questions below. The community is here to help.

FDE vs SE vs Sales Engineer vs SA: What's Actually Different? One of the most common questions we get: "How is a Forward Deployed Engineer different from a Solutions Engineer?" The confusion is understandable — these roles share DNA but differ in critical ways that affect your career, compensation, and daily work. The Quick Comparison FDE Solutions Engineer Sales Engineer Solutions Architect Primary job Build custom solutions at client sites Demo product, support sales Demo product, close deals Design system architecture Code daily? Yes, production code Sometimes, mostly demos Rarely Design docs, some code Reports to Engineering Engineering or Sales Sales Engineering or CTO office Comp range $170K-$700K $140K-$350K $130K-$300K (+ commission) $160K-$400K Quota/commission No Sometimes Yes No Travel 20-50% 15-30% 30-50% 10-25% Client access Deep, embedded Moderate, project-based Pre-sale only Design phase Career path Eng leadership, CTO, founder SE management, product Sales leadership, AE Principal architect, CTO Forward Deployed Engineer (FDE/FDSE) The defining trait: You write production code that solves specific customer problems, often on-site or embedded with the client team. Day-to-day: Deploying and customizing software at client sites Building integrations between your product and client systems Translating business requirements into technical solutions Presenting progress to client stakeholders Debugging production issues in real-time with the customer watching Who hires: Palantir (invented the role), Databricks, Scale AI, Anduril, Anthropic, OpenAI Best for you if: You love building things AND talking to people. You thrive in ambiguity. You want engineering comp without pure IC track monotony. Watch out for: Travel fatigue, context-switching between clients, scope creep, feeling disconnected from core product team. Solutions Engineer (SE) The defining trait: You bridge the gap between the product and the customer, typically supporting sales with technical expertise. Day-to-day: Running product demos tailored to prospect requirements Building POCs and proof-of-value projects Answering technical questions during sales cycles Writing integration guides and technical documentation Collaborating with product teams on feature requests Who hires: Nearly every B2B SaaS company — Datadog, Snowflake, MongoDB, HashiCorp, Twilio Best for you if: You like variety across customers but prefer working with your own product rather than customizing it. You enjoy being the technical expert in the room. Key difference from FDE: SEs typically don't write production code. They demo, configure, and prove value — FDEs build and deploy. Sales Engineer The defining trait: You are part of the sales team. Your job is to help close deals by removing technical objections. Day-to-day: Joining sales calls to handle technical questions Building custom demos for specific prospects Running RFP/RFI responses Competitive analysis (why us vs. them) Travel to customer sites for executive presentations Who hires: Enterprise software companies, often titled "Pre-sales Engineer" or "Technical Account Manager" Best for you if: You're motivated by closing deals and want a path to sales leadership. You like the energy of the sales floor. Key difference from FDE: Sales engineers have quotas or are comp'd on deals closed. FDEs are measured on deployment success, not revenue. Solutions Architect (SA) The defining trait: You design the technical architecture for how a product integrates into the customer's environment. Day-to-day: Creating architecture diagrams and design documents Evaluating customer infrastructure and recommending patterns Leading technical workshops and design reviews Setting standards for implementation teams to follow Long-term technical relationship with strategic accounts Who hires: AWS, Google Cloud, Microsoft, large enterprise vendors Best for you if: You think in systems. You enjoy designing but are okay with others implementing. You want depth over breadth. Key difference from FDE: SAs design but often don't implement. FDEs design AND build. SAs focus on architecture; FDEs focus on delivered outcomes. Compensation Comparison (2026 Data) Junior (0-2 YOE) Role Base Total Comp FDE $130K-$170K $170K-$230K SE $100K-$140K $120K-$180K Sales Eng $90K-$130K $110K-$180K (w/ commission) SA $110K-$150K $130K-$190K Senior (5-8 YOE) Role Base Total Comp FDE $200K-$280K $300K-$500K SE $160K-$220K $200K-$320K Sales Eng $150K-$200K $200K-$350K (w/ commission) SA $180K-$240K $230K-$380K Staff/Principal (8+ YOE) Role Base Total Comp FDE/Field CTO $260K-$350K $450K-$700K+ SE Director $200K-$280K $280K-$450K Sales Eng Dir $200K-$260K $300K-$500K (w/ commission) Principal SA $230K-$300K $350K-$550K Career Transitions Between These Roles The most common transitions: SE → FDE: Natural move. You already know the product and customers. Build more, demo less. FDE → Founder: FDEs see customer problems firsthand — many start companies solving them. Sales Eng → SE: Drop the quota, keep the technical depth. SA → FDE: Go from designing to building. Higher comp, more travel. FDE → PM: Your customer empathy and technical skills translate directly. Bottom Line Choose FDE if you want to build real things for real customers at the highest comp. Choose SE if you want variety and technical depth without quota pressure. Choose Sales Eng if you're motivated by deals and want sales leadership options. Choose SA if you love designing systems and want principal-level technical depth. Have experience in multiple roles? Share your comparison in the replies — the community benefits from real stories.

Understanding the FDE Landscape Forward Deployed Engineer is often confused with similar roles. Here is a detailed comparison. FDE vs Software Engineer (SWE) Dimension FDE SWE Where you work Client site or embedded with customers Company office or remote What you build Custom solutions per client Product features for all users Skills Breadth - full stack, data, infra, communication Depth - specialized in a domain Feedback loop Immediate from end users Through PMs, tickets, analytics Travel Often 25-75% Minimal Career risk Breadth can feel like lack of depth Depth can feel narrow Compensation 10-20% premium for equivalent level Standard market rates Advancement speed 3-4 years junior to senior 5-7 years junior to senior FDE vs Solutions Engineer (SE) Dimension FDE Solutions Engineer When involved Post-sale, during deployment Pre-sale, during evaluation Code Writes production code daily Demos, POCs, limited production code Quota Usually no sales quota Often tied to sales metrics Depth Deep technical implementation Broad product knowledge Client relationship Months-long engagement Days to weeks per deal FDE vs Technical Consultant Dimension FDE Technical Consultant Product Deploys their company's product Technology agnostic Employment Full-time at a tech company Consulting firm or independent Billing Salary + equity Hourly or project-based Loyalty To the product and customer To the customer primarily Code ownership Contributes to core product Builds custom solutions FDE vs Customer Success Engineer Dimension FDE Customer Success Engineer Technical depth Deep - writes production code Moderate - configuration and support Proactive vs reactive Builds new solutions proactively Responds to issues reactively Engagement length Project-based, months Ongoing account relationship Which Role Is Right for You? Choose FDE if: You want variety, client interaction, and immediate impact. You are comfortable with ambiguity and travel. Choose SWE if: You want deep technical focus, predictable schedule, and long-term system ownership. Choose SE if: You enjoy the sales process and want to influence deals without long deployments. Choose Consulting if: You want maximum variety and are comfortable with project-based work. Which role do you identify with? Has anyone transitioned between these? Share your experience.

The Forward Deployed AI Engineer The intersection of FDE and AI/ML is the fastest growing segment in tech. Job postings for AI-focused FDE roles increased 800-1000% in 2025, and the trend is accelerating. Why AI Needs FDEs AI products are uniquely difficult to deploy: Every customer's data is different - Models need customization per environment Integration is complex - RAG pipelines, vector databases, fine-tuning workflows Trust requires hand-holding - Enterprises need someone on-site to prove AI works with their data The last mile is the hardest - Going from demo to production requires deep FDE work Who Is Hiring AI FDEs? The demand is massive: OpenAI - Deploying ChatGPT Enterprise and API integrations Anthropic - Claude enterprise deployments Cohere - Enterprise NLP solutions Databricks - ML platform deployments Scale AI - Data labeling and AI infrastructure ElevenLabs - Voice AI deployments Palantir AIP - AI Platform forward deployment Anduril - Defense AI systems Plus hundreds of AI startups that need someone to get their product working in customer environments. Skills You Need On top of standard FDE skills, AI FDEs need: RAG architecture - Retrieval Augmented Generation pipelines Vector databases - Pinecone, Weaviate, ChromaDB, pgvector Fine-tuning - When and how to fine-tune models for specific use cases Prompt engineering - Systematic prompt design and evaluation Agentic workflows - Multi-step AI systems that execute tasks autonomously Evaluation and observability - Measuring AI system quality in production Data pipelines - Getting customer data into the right format for AI Compensation AI FDE roles command a premium: Full-time: $180,000 - $450,000+ total comp Contract: $200 - $400+/hour for senior AI FDEs Career Path The AI FDE path is still being defined, but common trajectories: AI FDE to Head of Solutions Engineering at an AI company AI FDE to AI Product Manager - you understand customers deeply AI FDE to Founder - you have seen dozens of customer problems firsthand AI FDE to Field CTO - the strategic technical partner for enterprise Are you working as an AI FDE? What does the work actually look like? Share below.

Forward Deployed Engineer Salary Guide 2026 Compensation is one of the most searched topics for FDEs. Here is a comprehensive breakdown based on current market data. Full-Time Salary Ranges (US) Level Base Salary Total Comp (with equity/bonus) Junior FDE (0-2 years) $120,000 - $160,000 $140,000 - $200,000 Mid-Level FDE (2-5 years) $160,000 - $220,000 $200,000 - $320,000 Senior FDE (5+ years) $200,000 - $280,000 $300,000 - $450,000+ Company-Specific Comp Palantir - Average total comp around $238,000, ranging from $205,000 to $486,000 depending on level Databricks - Field Engineering roles typically $180,000 - $350,000 total comp Scale AI - FDE roles range $150,000 - $300,000+ AI Startups (OpenAI, Anthropic, Cohere) - Often $200,000 - $400,000+ with significant equity Contract and Freelance Rates FDE contract work is a growing market: Junior: $90 - $150/hour Mid-Level: $150 - $225/hour Senior/Specialist: $225 - $300+/hour Senior FDEs with expertise in complex enterprise deployments or AI/ML can command rates at the top end. FDE vs SWE Compensation FDE roles often pay 10-20% more than equivalent SWE roles at the same company because: Client-facing pressure and travel requirements Broader skill set needed Revenue-generating nature of the work Smaller talent pool Negotiation Tips Know your leverage - FDEs directly impact revenue. Quantify your client outcomes Factor in travel - If the role requires 50%+ travel, negotiate a premium Equity matters - At startups, FDE equity can be significant since you prove product-market fit Remote premium - Some companies pay less for remote FDEs, push back if the output is the same Competing offers - FDE skills transfer across companies easily, use that What is your compensation experience as an FDE? Share anonymously below to help the community.

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Role Comparisons FDE vs Software Engineer Dimension FDE SWE Where you work Client site Company office What you build Custom solutions per client Product features for all Skills Breadth Depth Travel 25-75% Minimal Comp 10-20% premium Standard rates Advancement 3-4 years to senior 5-7 years to senior FDE vs Solutions Engineer Dimension FDE SE When involved Post-sale deployment Pre-sale evaluation Code Production code daily Demos and POCs Quota No sales quota Tied to sales FDE vs Technical Consultant Dimension FDE Consultant Product Company product Technology agnostic Employment Full-time tech company Consulting firm Code ownership Contributes to core product Custom solutions Which Is Right for You? FDE: Variety, client interaction, immediate impact SWE: Deep technical focus, system ownership SE: Sales process, shorter engagements Consulting: Maximum variety, project-based Which role do you identify with? Share your experience.

The Forward Deployed AI Engineer Job postings for AI-focused FDE roles increased 800-1000% in 2025. Why AI Needs FDEs Every customer data is different - Models need customization per environment Integration is complex - RAG pipelines, vector databases, fine-tuning Trust requires hand-holding - Enterprises need proof AI works with their data The last mile is hardest - Demo to production requires deep FDE work Who Is Hiring AI FDEs? OpenAI - ChatGPT Enterprise and API integrations Anthropic - Claude enterprise deployments Cohere - Enterprise NLP solutions Databricks - ML platform deployments Scale AI - Data labeling and AI infrastructure ElevenLabs - Voice AI deployments Palantir AIP - AI Platform forward deployment Skills You Need RAG architecture and vector databases Fine-tuning and prompt engineering Agentic workflows and evaluation Data pipelines for AI systems Compensation Full-time: $180,000 - $450,000+ total comp Contract: $200 - $400+/hour for senior AI FDEs Are you working as an AI FDE? Share below.

Forward Deployed Engineer Salary Guide 2026 Compensation is one of the most searched topics for FDEs. Here is a comprehensive breakdown. Full-Time Salary Ranges (US) Level Base Salary Total Comp Junior FDE (0-2 years) $120,000 - $160,000 $140,000 - $200,000 Mid-Level FDE (2-5 years) $160,000 - $220,000 $200,000 - $320,000 Senior FDE (5+ years) $200,000 - $280,000 $300,000 - $450,000+ Company-Specific Comp Palantir - Average total comp around $238,000, ranging $205,000 to $486,000 Databricks - Field Engineering roles $180,000 - $350,000 total comp Scale AI - FDE roles $150,000 - $300,000+ AI Startups (OpenAI, Anthropic, Cohere) - $200,000 - $400,000+ with equity Contract and Freelance Rates Junior: $90 - $150/hour Mid-Level: $150 - $225/hour Senior/Specialist: $225 - $300+/hour FDE vs SWE Compensation FDE roles often pay 10-20% more than equivalent SWE roles because of client-facing pressure, broader skill set, travel requirements, and smaller talent pool. Negotiation Tips Know your leverage - FDEs directly impact revenue Factor in travel - 50%+ travel warrants a premium Equity matters - FDE equity is significant at startups Competing offers - FDE skills transfer easily across companies What is your compensation experience as an FDE? Share below.

Welcome to the FDE Community! This is a space for Forward Deployed Engineers, aspiring FDEs, and anyone interested. Guidelines Be respectful - No personal attacks or harassment Share knowledge freely - Help each other grow No NDA violations - Do not share proprietary client information Keep it professional - Constructive discussions only No spam - Job postings go in the Job Board category Use the right category - Keep discussions organized Get Started Introduce yourself in the Off-Topic category Browse categories and join discussions Ask questions - no question is too basic Share your experiences Let us build this community together!

What is Forward Deployed Engineering? Forward Deployed Engineering (FDE) is a role at the intersection of software engineering, consulting, and customer success. FDEs work directly with clients to understand their problems, build custom solutions, and deploy technology in real-world environments. Key Responsibilities Client-facing technical work - Embedded with the customer, not coding in isolation Rapid prototyping - Building solutions quickly to prove value and iterate Full-stack problem solving - From data pipelines to frontend dashboards Technical translation - Bridging what the client needs and what engineering builds Deployment and integration - Getting software running in messy production environments Who hires FDEs? The role was popularized by Palantir Technologies but has expanded to Databricks, Scale AI, Anduril, and many startups. FDE vs Traditional SWE Location: On-site with clients vs company office Scope: End-to-end solutions vs specific features Skills: Breadth over depth vs depth over breadth Feedback: Immediate from users vs through PMs Share your thoughts below - what drew you to FDE?