SaaS Development Company for US & EU B2B Products

SaaS work is fixed-scope and all-in, quoted in USD. Support and ongoing evolution of a live SaaS runs from $1,400 per month per named seat; a paying-customer-ready SaaS MVP from $8,100; a full turnkey B2B SaaS with enterprise auth, tenant isolation and data residency from $20,700; and a high-load platform engineered past its first 1000 tenants from $40,200. The exact number depends on the tenancy model, integration count (billing, tax, SSO/SCIM), regulated scope and the number of pinned data-residency regions. You see the line-item budget at the end of discovery and sign off before any code is written — no hidden fees, no recruitment markup, no tool surcharges, and cloud fees run on your own accounts.

Yes. We design the multi-tenancy model on day one — typically row-level isolation with a tenant_id column on every domain table for new B2B SaaS, or schema-per-tenant for products with strict data-segregation requirements. We layer in tenant provisioning, clean tenant offboarding with deterministic data deletion, per-tenant feature flags, a billing backbone, an admin console, and SSO/RBAC scaffolding. Most greenfield SaaS MVPs ship a paying-customer-ready v1 in 12–16 weeks.

Often. We start with a tenancy audit of the existing codebase and database, then plan the migration in phases: introduce tenant_id, dual-write or backfill data, gate access at the ORM and API layers, refactor background jobs, and finally retire the legacy single-tenant deployment per customer. We run customer-zero migrations on a dark-launched copy of production so the first real tenant cuts over with measurable risk, not a leap of faith.

Enterprise readiness is a concrete checklist: SAML 2.0 and OIDC SSO with at least Okta, Azure AD, and Google Workspace tested; SCIM 2.0 user and group provisioning; role-based access control with at least three default roles plus custom roles; an immutable audit log covering authentication, configuration, and data access; data export on demand in a machine-readable format; and a clearly documented sub-processor list. We ship those capabilities as part of the platform foundation, not as a paid add-on.

We integrate Stripe Billing or Chargebee for subscription lifecycle, proration, usage-based metering, and invoice generation. Tax is handled through Stripe Tax or Avalara so you do not maintain VAT and sales-tax tables yourself. Dunning is automated with smart retries, email and in-app reminders, and a clear grace-period policy that does not break paying customers. Revenue recognition is exported to your finance stack — typically NetSuite, QuickBooks, or Xero — through scheduled jobs or a thin reconciliation service.

We build with compliance in mind from the foundation phase. GDPR-aligned engineering covers lawful basis, data residency, DSARs, and sub-processor transparency. We are SOC 2 Type II in progress and aligned to the trust services criteria for security and availability, which directly maps to your own SOC 2 scope. HIPAA-capable delivery is available for PHI workloads with BAAs at the infrastructure layer. CCPA notice obligations and PCI DSS scoping for payment flows are addressed when the SaaS handles US consumer or cardholder data.

Yes. After launch the same squad transitions into a run mode with SLO-driven SRE, error budgets, an on-call rotation, and a defined incident response process with public status communication. Observability is built on Datadog, Sentry, and OpenTelemetry instrumentation we install during the build phase, so production issues are diagnosed in minutes not hours. We also run growth experiments, customer-zero migrations for new features, and quarterly architecture reviews to keep the platform healthy past 1000 tenants.

The right tenancy model is determined by three factors: data-segregation requirements, scale, and operational cost. Row-level isolation (a tenant_id column on every domain table, enforced at the ORM and API layer) is the right default for most B2B SaaS: it ships fastest, scales to tens of thousands of tenants on a single database cluster, and supports per-tenant feature flags and usage metering without provisioning infrastructure per customer. Schema-per-tenant suits products where tenant data is large, cross-tenant joins are never needed, and the customer base is small enough that provisioning a schema per signup is operationally manageable — typically under 2,000 tenants. Silo (separate database or infrastructure per tenant) is justified only when the contract requires it — regulated industries, sovereign-cloud procurement, or enterprise accounts willing to pay the premium for guaranteed non-shared infrastructure. We recommend the model in discovery after reviewing your compliance scope, expected tenant count, and pricing model; most products start with row-level and add a silo option for enterprise tiers after proving the product.

We wire product analytics at the infrastructure layer rather than through a third-party pixel dropped on every page — a pattern that survives ad-blockers, gives you tenant-scoped event streams, and keeps you in control of the data. Events are emitted from the backend on state transitions (activation, first-value moment, feature adoption, churn signals) and from the frontend on intentional user actions. The event schema is designed during the build phase against the metrics your product team needs — typically activation rate, time-to-first-value, feature adoption by plan tier, and cohort retention — so you are not reverse-engineering meaning from raw click events after launch. For GDPR and CCPA compliance, telemetry is tenant-scoped and stripped of PII at the collection point; aggregates flow to your BI layer (Metabase, Looker, or Amplitude) and per-tenant dashboards are available in your admin console. We also instrument SLO-relevant events — API p95 latency, job queue depth, error rate per tenant — so the observability stack covers both product health and operational health from a single pipeline.

Yes, and we have. Technical due diligence for a SaaS acquisition or Series B covers five areas: code quality and test coverage, architecture and scalability evidence, security posture, compliance documentation, and operational runbooks. We build with all five in mind from the foundation phase: automated test suites on critical paths (auth, billing, tenancy boundaries) with a coverage target agreed in discovery; architecture decision records and a C4 model maintained throughout the build; a threat model reviewed at the hardening phase; GDPR and SOC 2 documentation produced as a project deliverable, not assembled under investor pressure; and operational runbooks for every critical flow written by the engineers who built them. If you are already operating a SaaS and approaching a raise or exit, we run a pre-diligence audit — code review, architecture assessment, and compliance gap analysis — and produce a remediation plan sized against the diligence timeline.

Feature flags are a first-class architectural concern, not a workaround. We implement a flag system at the platform level — flags stored in the database, evaluated server-side on every request, scoped to tenant, plan tier, or individual user — so you can enable a feature for your beta tenants, roll it to 5% of all tenants, and gate it behind a plan upgrade without a code deploy. We integrate with LaunchDarkly or Unleash where a managed solution is preferred, or ship a lightweight internal implementation for teams that need auditability without a third-party sub-processor. Progressive rollouts (canary deploys, traffic percentages, automated rollback on error-rate threshold) are wired into the CI/CD pipeline from the first production deploy — the same discipline that lets us ship to paying customers in sprint two without a feature-freeze gate before launch.