Developing a multi-tenant Software-as-a-Service (SaaS) application requires balancing data security, query isolation, and cost-efficiency. With our latest venture, HireIQ, an AI-powered recruitment platform, we had to build an architecture capable of processing thousands of candidate resumes simultaneously while ensuring each HR agency's data remained private.
1. Choosing a Multi-Tenancy Strategy
For HireIQ, we analyzed three multi-tenant models: database-per-tenant, schema-per-tenant, and single shared database with tenant column isolation. Because recruitment companies require dynamic scale and budget predictability, we opted for the shared database with column isolation model. We implemented database query scoping to ensure tenant_id filters are automatically applied to every query:
protected static function booted()
{
static::addGlobalScope(new TenantScope);
}
This prevents any developer slip-up from leaking candidate info to another company, maintaining isolation at the database query abstraction layer.
2. Handling AI Pipelines Asynchronously
Resumes are uploaded in bulk. Processing them with AI models synchronously would block web server processes and lead to request timeouts. HireIQ utilizes an asynchronous pipeline where uploads are written directly to AWS S3. A serverless event triggers an extraction microservice that parses PDF texts, sends chunks to vector databases, and performs semantic matching in the background, keeping the user interface fast and highly responsive.