Fig DevTool \USA

Fig was a next-generation terminal tool that aimed to modernize the command-line interface experience for developers. Launched in 2020, Fig provided IDE-style autocomplete, visual command builders, and collaborative features directly in the terminal. The value proposition was compelling: developers spend hours daily in terminals, yet the UX hadn't evolved in decades. Fig offered autocomplete for 300+ CLI tools (git, npm, docker, kubectl, etc.), visual parameter builders, and team script sharing. The 'why now' was strong—remote work explosion, DevOps complexity, and a new generation of developers who expected modern tooling. They raised $2.4M from Y Combinator and angels, built a passionate early adopter community, and achieved significant GitHub stars. However, the product existed in a challenging space: free/open-source terminal alternatives, low willingness-to-pay for developer tools, and the fundamental challenge of monetizing productivity improvements that users could replicate with shell scripts and aliases.

SECTOR Information Technology
PRODUCT TYPE Developer Tools
TOTAL CASH BURNED $2.4M
FOUNDING YEAR 2020
END YEAR 2024

Discover the reason behind the shutdown and the market before & today

Failure Analysis

Failure Analysis

Fig's failure was fundamentally a unit economics and monetization problem, not a product-market fit issue. They built a beloved product with strong engagement metrics—users...

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Market Analysis

Market Analysis

The developer tools market has evolved significantly since Fig's 2020 launch. Today, the landscape is dominated by AI-native tools that have reset expectations for...

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Startup Learnings

Startup Learnings

Developer tools require transformational, not incremental, value to justify paid conversion. Fig's autocomplete was useful but not 10x better than free alternatives. Modern rebuilds...

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Market Potential

Market Potential

The TAM for developer productivity tools is large (50M+ developers globally, $50B+ market), but willingness-to-pay for terminal enhancements specifically is low. Fig proved there's...

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Difficulty

Difficulty

The core technical challenge—parsing CLI tool schemas and providing intelligent autocomplete—is significantly easier today. Fig had to build custom parsers for 300+ tools and...

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Scalability

Scalability

Developer tools have inherently challenging unit economics. Fig's model required significant per-user support (terminal compatibility issues, shell conflicts, OS-specific bugs), ongoing maintenance for CLI...

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Rebuild & monetization strategy: Resurrect the company

Pivot Concept

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An AI-native terminal assistant for DevOps and platform engineering teams that prevents production incidents before they happen. Unlike Fig's autocomplete, Sentinel uses LLMs to understand the full context of your infrastructure (connected AWS/GCP/K8s accounts, recent deployments, team runbooks) and provides proactive guidance: 'That kubectl delete command will affect 47 production pods—did you mean staging?' It learns from team incidents, suggests safer alternatives, and auto-generates rollback commands. The wedge is safety and compliance for platform teams; the expansion is full workflow automation. Built on modern infrastructure (Tauri for desktop, Rust for performance, Claude for reasoning, Supabase for team data), it can ship in 12 weeks and target the 2M+ platform engineers who have budget authority and clear ROI metrics (reduced MTTR, fewer incidents, faster onboarding). Monetization is $49/user/month for teams, with usage-based pricing for AI features. The moat is proprietary incident data and deep integrations with cloud providers that take competitors 18+ months to replicate.

Suggested Technologies

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Tauri (cross-platform desktop app with web tech, smaller than Electron)Rust (terminal integration, shell hooks, performance-critical parsing)React + TypeScript (UI components, command palette)Claude API (context understanding, natural language to command, error explanation)Supabase (user data, team knowledge base, incident history)Vercel (marketing site, docs, API endpoints)AWS SDK / GCP SDK / Kubernetes client-go (infrastructure context)Stripe (billing, usage metering)PostHog (product analytics)Sentry (error tracking)

Execution Plan

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Phase 1

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Step 1 - Safety Wedge (Weeks 1-4): Build a Rust-based terminal wrapper that intercepts destructive commands (rm, kubectl delete, terraform destroy) and uses Claude API to analyze context. If risky, show a confirmation dialog with explanation and safer alternatives. Target Kubernetes platform teams on Reddit, HN, and DevOps Slack communities. Free beta with waitlist to build urgency. Goal: 500 beta users, 50% daily active, 10+ testimonials about prevented incidents.

Phase 2

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Step 2 - Team Knowledge (Weeks 5-8): Add team features—shared runbooks, incident postmortems, and command templates. When a user runs a complex command, Sentinel suggests saving it as a team template. When an incident happens, it auto-generates a postmortem with timeline and commands run. Integrate with Slack for notifications. Launch paid beta at $49/user/month for teams of 5+. Goal: 20 paying teams (100 seats), $5K MRR, 80%+ retention after 30 days.

Phase 3

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Step 3 - Infrastructure Context (Weeks 9-12): Deep integrations with AWS, GCP, and Kubernetes. Sentinel reads your infrastructure state (running services, recent deployments, resource quotas) and provides proactive warnings. 'That deployment will exceed your memory limits' or 'This service was deployed 3 minutes ago—wait for health checks before rolling back.' Add natural language interface: 'Show me all pods in production using more than 80% memory.' Launch on Product Hunt and HN. Goal: 100 paying teams (500 seats), $25K MRR, 5+ enterprise pilots.

Phase 4

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Step 4 - Workflow Automation (Months 4-6): Expand beyond safety to full workflow automation. Sentinel learns common patterns (deploy, rollback, scale, debug) and suggests one-click workflows. 'It looks like you're debugging a memory leak—I can restart the pod, capture a heap dump, and open it in your profiler. Run this?' Integrate with incident management tools (PagerDuty, Opsgenie). Add compliance features (audit logs, approval workflows) for enterprise. Launch enterprise tier at $99/user/month with SSO, RBAC, and dedicated support. Goal: 500 teams (2500 seats), $125K MRR, 10+ enterprise contracts, Series A readiness.

Monetization Strategy

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Freemium with hard paywall after 14-day trial. Free tier does not exist—this avoids Fig's mistake of training users to expect zero cost. Pricing is $49/user/month for teams (5+ seats), billed annually with 20% discount. Enterprise tier at $99/user/month adds SSO, RBAC, audit logs, SLA, and dedicated support. Usage-based pricing for AI features: included quota of 1000 AI assists per user per month, then $0.10 per additional assist. This aligns incentives—heavy users pay more, and we capture value from power users. Revenue model targets $1M ARR in year one (500 seats average, 50% on standard tier, 50% on enterprise), $5M ARR in year two (2500 seats, 60% enterprise mix), and $20M ARR in year three (10K seats, enterprise-focused). Expansion revenue comes from seat growth within accounts (platform teams grow as companies scale) and upsells to enterprise tier (compliance requirements kick in at 50+ engineers). The key metric is net dollar retention—target 130%+ by year two through expansion and upsells. CAC payback is under 12 months through product-led growth (bottom-up adoption, top-down procurement) and strong word-of-mouth in DevOps communities. Gross margins are 80%+ (SaaS model with usage-based AI costs as the main variable expense). The business model works because we target users with budget authority (platform/DevOps engineers), solve a hair-on-fire problem (production incidents), and have clear ROI metrics (reduced MTTR, fewer incidents, faster onboarding). Unlike Fig, we monetize the core value prop (safety and context) rather than peripheral features, and we design for enterprise procurement from day one.

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