Series A checklist

Is the overall architecture appropriate for the team size and user scale expected post-Series A?

A monolith that worked for 5 engineers often becomes a bottleneck at 20. The question is whether the current architecture anticipates this.

Is the codebase modular enough for different teams to work on different areas without constant coordination overhead?

Codebase modularity is a team-scaling problem as much as a technical one.

Is test coverage above 40% for business-critical paths? Are failure paths and edge cases covered?

Below 40% on critical paths at Series A means the team is shipping with significant blind spots.

Is there evidence of a regular code review process with substantive technical feedback?

Code review quality is a leading indicator of team technical standards over time.

Is technical debt tracked, estimated, and included in sprint planning rather than deferred indefinitely?

Untracked debt does not disappear. It compounds silently until it forces an expensive rewrite.

Are engineering standards documented and enforced via tooling such as linters, formatters, and CI quality gates?

Standards that only exist in people's heads break down past 10 engineers.

Can a new engineer run the full stack locally and ship their first change within their first week?

Poor developer experience adds a hidden productivity tax to every future hire.

Are external APIs versioned, with a deprecation lifecycle and backward compatibility guarantees?

Unversioned APIs create breaking-change crises as the customer base grows and integrations multiply.

Is there a formal security review step in the development process for new features?

Ad-hoc security at Series A creates gaps that appear in enterprise security questionnaires.

Is the application compliant with applicable data protection regulations such as GDPR or CCPA?

Non-compliance discovered during enterprise due diligence routinely kills deals.

Is role-based access control implemented with least-privilege principles across all user roles?

Overly broad permissions are a security risk and a compliance gap that enterprise customers specifically audit.

Are all secrets managed with a dedicated secrets manager rather than environment variables alone?

Environment-variable secrets are acceptable at seed. At Series A they introduce operational risk at scale.

Is there a documented process for triaging and patching critical CVEs within 72 hours of disclosure?

Enterprise customers will ask for SLAs on security patching during procurement.

Are inter-service communications authenticated, even on internal networks?

Trusting internal network traffic is the assumption lateral movement attacks rely on.

Is there a security incident response plan with defined roles and communication procedures?

A security incident without a response plan at Series A scale is an existential risk.

Have formal penetration tests been conducted in the last 12 months?

Enterprise customers increasingly require evidence of penetration testing as part of procurement.

Has the team conducted load testing? What are the measured bottlenecks at 10x current load?

Untested scale assumptions are one of the most common causes of post-funding growth crises.

Can the application scale horizontally? Are there any stateful components that prevent this?

Horizontal scaling is the most cost-effective path to handling growth. Stateful components block it.

Is the database designed for 10 to 100x current data volume? Are there partitioning or archiving strategies?

Databases that work at seed scale frequently fail at Series A growth rates.

Is there a defined caching strategy? Are cache invalidation patterns well-understood and tested?

Poorly designed caching creates hard-to-reproduce inconsistency bugs that emerge at scale.

Is infrastructure defined as code, with all environments reproducible from source?

Manual infrastructure cannot be reliably audited or replicated at Series A scale.

Are background workers using a robust queue system with retry logic, dead-letter queues, and monitoring?

Unreliable background processing is invisible during normal operation and catastrophic during incidents.

Is the application architected for multi-AZ or multi-region deployment?

Single-AZ deployments are a business continuity risk for any company with revenue-critical uptime.

Are database reads separated from writes using read replicas or CQRS patterns where appropriate?

Read/write separation is frequently the first scalability lever needed after Series A closes.

Are there auto-scaling policies in place for compute and queue workers?

Manual scaling during traffic spikes requires on-call intervention for what should be automatic.

Is there a cloud cost management strategy? Are there cost anomaly alerts?

Unmanaged cloud costs are a common source of surprise at Series A growth rates.

What is the deployment frequency and change failure rate? Are these metrics tracked?

DORA metrics at Series A should show multiple deployments per week. If they are not tracked, they cannot be improved.

Is there full observability: application metrics, distributed tracing, and structured logs?

Without all three, diagnosing production issues becomes guesswork as architectural complexity grows.

Are SLOs defined for critical services? Are they currently being met?

Undefined SLOs mean there are no standards to hold the team to during reliability incidents.

Is there an on-call rotation and a documented incident management process?

A solo on-call founder is not a sustainable incident response strategy post-Series A.

Are production database migrations zero-downtime?

Migrations that require downtime limit deployment frequency and create risk windows that grow with data volume.

Is there a formal post-mortem process for production incidents, with action items tracked to completion?

Post-mortems without follow-through provide no safety improvement.

Are deployments canary-released or feature-flagged to reduce blast radius?

All-or-nothing deployments multiply the risk of every change as the user base grows.

Is there synthetic monitoring of critical user journeys, not just infrastructure health checks?

Infrastructure uptime monitoring tells you the server is running. Synthetic monitoring tells you the product is working.

What is the mean time to recover (MTTR) for production incidents? Is it improving over time?

MTTR that is not tracked cannot be improved. High MTTR is a direct cost to customer trust.