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  • Here’s my current opinionated stack for a modern SaaS in 2025—mixing hosted products and OSS libs that feel “bleeding edge but safe.”

  • The AI tooling & LLM ecosystem moves quickly. As always see x.com/the mewc for latest commentary.

  • Intro

    Welcome to the blog! This is a stub MDX post to verify routing.

  • Now

    Now

  • Today I Learned

    TIL

Projects

  • distribute analytics to teams connect meaning of internal bi events
  • directory, voting and guides for vertical based AI tools
  • profiles for luxury good enthusiasts cost too much to run on GCP and haven't bothered migrating to another provider

Best-of-breed vendor stack

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Here’s my current opinionated stack for a modern SaaS in 2025—mixing hosted products and OSS libs that feel “bleeding edge but safe.”

[!tip] Adoption principle
Start hosted, keep exits clear. Switch to OSS when cost/latency/lock‑in is proven by traces and traffic.

Core app platform

  • web/app: Next.js 15 + React 19 (RSC on by default)
  • hosting: Vercel for frontends and simple Node runtimes; Railway for long‑running services
  • background jobs & schedules: Inngest (simple, reliable), alternative: Temporal (complex, powerful)
  • queues: Upstash Redis (serverless), alternative: Cloudflare Queues

Auth, users, billing

  • auth + orgs + subscriptions: Clerk (fastest to ship)
  • alt/OSS: Auth.js + Lucia + Stripe Billing if you must self‑host
  • payments: Stripe (no contest for SaaS in 2025)

Data layer

  • OLTP: Postgres (Neon or Supabase); Prisma client
  • cache/kv: Upstash Redis; edge KV where needed (Cloudflare KV)
  • object store: Cloudflare R2 or S3 compatible
  • warehouse/OLAP: ClickHouse Cloud; alt: BigQuery

Product analytics, flags, experimentation

  • product analytics + feature gates: Statsig (strong default)
  • OSS alternative: PostHog + GrowthBook

Observability & reliability

  • errors: Sentry
  • logging: Better Stack or Axiom
  • traces/metrics: OpenTelemetry everywhere; vendor: Grafana Cloud or Honeycomb
  • incidents & runbooks: Rootly

Content & docs

  • marketing/docs: Mintlify for docs, MDX for product surfaces
  • CMS alt: Contentlayer or Sanity if you need authorship workflows

Communication

  • email: Resend + react‑email
  • support: Plain (shared inbox that doesn’t fight you)
  • hosted: Algolia (still excellent for relevance tooling)
  • OSS: Meilisearch for speed and control

AI/LLM building blocks

  • model router: OpenRouter (breadth, price); keep OpenAI/Anthropic direct as fallbacks
  • SDK: Vercel AI SDK (streaming, RSC‑friendly)
  • extraction & crawling: Firecrawl
  • tracing/evals: Langfuse; offline evals with Braintrust or Phoenix
  • embeddings & RAG: pgvector on Postgres for simplicity; ClickHouse + hybrid search when scale demands

Security & secrets

  • secret management: Doppler
  • runtime hardening: per‑request authZ, signed requests, audit logs; do not put permissions in prompts

Developer experience

  • code: Cursor as the primary IDE; GitHub + Actions for CI
  • UI: Tailwind CSS + shadcn/ui primitives

  • Vercel • Railway • Upstash • Neon • Supabase • Stripe • Clerk
  • Statsig • PostHog • GrowthBook • Sentry • Better Stack • Grafana
  • Mintlify • Resend • Plain • Algolia • Meilisearch
  • OpenRouter • OpenAI • Anthropic • Vercel AI SDK • Firecrawl • Langfuse

[!warning] When to avoid the “cool” choice
Don’t adopt a new vector DB, router, or agent framework unless a concrete bottleneck exists. Your latency and cost graphs should make the case first.

Tech Things

a list of some work, categories, keywords and valuable things I've done for people

  • calcs.com - structural engineering workflows
    https://calcs.com
    • Customer analytics
    • Integrations marketplace
    • Docs, Search, AI
    • ..other new verticals
  • computer vision ai for contextual advertising
    https://trendii.com
    • Custom ai models w cuda, mlops sagemaker, RAG, embeddings/vector databases
    • Customer analytics w pubsub, bigquery, metabase
    • Self serve customer portal w supabase
  • fractional cto / principal
    • product strategy
    • tech strat + selection
    • capital allocation
  • customer analytics
    • A number of times now build from scratch, or wrapped around partial setups
    • aws, gcp, tinybird, clickhouse, vendors platform engineering
    • Customer analytics w pubsub, bigquery, metabase
    • Also tinybird, custom workflows into postgres, looker, tableau
  • platform / devops
    • Multi region across us, au, uk, eu (gdpr compliant DE)
    • Several times takes deployments from months or weeks to minutes
    • ship to prod fast and async rollout to users (ofc)
  • growth engineering from 1m to 30m at linktree
    https://linktr.ee
    • employee 14
    • Analytics engineering, customer analytics
    • Pricing & Packaging
    • Martech, vendors, tiktok & facebook partnership
  • martech product engineering
    • enterprise boxever integrations
    • product analytics & experimentation tools