Startup tech stack guide 2026: what to build with at every stage

The 2026 default startup stack

For most startups, this is the right stack in 2026:

• Frontend: Next.js 15 + TypeScript + Tailwind CSS
• Backend: FastAPI (Python) or Node.js/Express for API-heavy apps
• Database: PostgreSQL via Supabase (auth, storage, realtime included)
• AI/LLM: OpenAI GPT-4o or Anthropic Claude 3.5 via their SDKs
• Hosting: Vercel (frontend) + Railway or Fly.io (backend)
• Auth: Supabase Auth or Clerk
• Payments: Stripe
• Observability: Sentry + PostHog

Pre-seed (0 to first 100 users)

At this stage, the only metric is shipping. Optimize for developer velocity, not scalability:

• Use managed services for everything — don't run your own infrastructure
• Choose tools your team already knows — this is not the time to learn new languages
• Supabase over raw PostgreSQL + custom auth (one tool does both)
• Vercel over AWS (zero DevOps overhead)
• Use OpenAI APIs, not self-hosted models — the infra overhead isn't worth it yet

Seed stage (first 1K users to $500K ARR)

Now you have real users and real load. The focus shifts to reliability and team scalability:

• Add a proper CI/CD pipeline (GitHub Actions + automated tests)
• Separate your database from your app hosting (connection pooling via PgBouncer or Supavisor)
• Add background job processing (BullMQ for Node, Celery for Python)
• Set up structured logging and alerting (Datadog or Grafana Cloud)
• Add feature flags (LaunchDarkly or Unleash) for safe rollouts

Series A ($1M+ ARR, 5+ engineers)

The focus shifts to team coordination and platform investments:

• Evaluate whether to migrate off Vercel to AWS/GCP for cost at scale
• Consider Kubernetes if you have 10+ services (but not before)
• Add a data warehouse (Snowflake or BigQuery) for analytics
• Implement SOC 2 compliance tooling if selling enterprise (Drata or Vanta)
• Formalize your AI/LLM infrastructure — evaluation harnesses, prompt versioning, model selection

AI/LLM stack decisions for 2026

Every startup is now an AI company. Here's what we recommend:

• Model selection: Claude 3.5 Sonnet for reasoning-heavy tasks, GPT-4o for multimodal, Gemini 2.0 Flash for cost-sensitive high-volume calls
• RAG stack: OpenAI embeddings + pgvector (in Supabase) for document retrieval — avoid Pinecone unless you have 1M+ vectors
• Agentic workflows: LangChain or LlamaIndex for orchestration, but keep prompts simple first
• Evaluation: Braintrust or Langfuse for LLM observability and prompt regression testing