Build vs Buy: When to Use Third-Party Services

Problem

Your startup is building a developer productivity platform. For each major capability, you need to decide whether to build it in-house or use a third-party service. Each decision has cost, control, time, and risk implications.

Capabilities to Evaluate

1. Authentication: User registration, login, SSO, MFA, password reset

- Build: Custom auth with bcrypt, JWT, TOTP library - Buy: Auth0, Clerk, Supabase Auth

1. Email Sending: Transactional emails, marketing campaigns, templates

- Build: Self-hosted email server (Postfix + DKIM) - Buy: SendGrid, Postmark, AWS SES

1. Full-Text Search: Search across documents, code snippets, and user content

- Build: Self-hosted Elasticsearch/OpenSearch cluster - Buy: Algolia, Typesense Cloud, Meilisearch Cloud

1. Payment Processing: Subscriptions, usage-based billing, invoices

- Build: Custom payment integration with bank APIs - Buy: Stripe, Paddle, LemonSqueezy

1. File Storage & CDN: User uploads, generated assets, global delivery

- Build: Self-hosted MinIO with custom CDN - Buy: AWS S3 + CloudFront, Cloudflare R2

1. Monitoring & Observability: Logs, metrics, traces, alerting

- Build: Self-hosted Grafana + Prometheus + Loki + Tempo - Buy: Datadog, New Relic, Sentry

Decision Framework

For each capability, evaluate:

1. Core vs Context: Is this a core differentiator for your product, or operational context?
2. Engineering Cost: How many engineer-months to build and maintain?
3. Vendor Cost: Monthly cost at current scale AND projected 10x scale.
4. Vendor Lock-in Risk: How hard is it to switch providers later?
5. Control: Do you need to customize behavior beyond what the vendor supports?
6. Compliance: Are there data residency or security requirements that affect the decision?
7. Time-to-Market: How does each option affect your launch timeline?

Your Task

1. Evaluate each capability using the framework.
2. Recommend build or buy for each with detailed justification.
3. Identify the total engineering cost (in months) of building everything vs. buying everything.
4. Design an abstraction layer that reduces lock-in for "buy" decisions.
5. Create a decision matrix that future capabilities can be evaluated against.

Constraints

• 6-person engineering team, launching in 5 months.
• Annual recurring revenue (ARR): $0 (pre-revenue). Runway: 18 months.
• Expected users at launch: 500. Target at month 12: 10,000.
• Located in the US, some customers will be in EU (GDPR applies).