Check-out Our Cost Calculators

Get precise estimates for MVP development, features, and timeline.

• Platform-specific breakdown
• Feature complexity tiers
• Timeline and phase plan

See side-by-side totals to choose the most cost-effective model

• Salaries vs partner pricing
• Productivity adjustments applied
• Annualized TCO scenarios

Forecast payback period and long-term gains from your project

• Inputs: costs, uplift, savings
• Outputs: ROI, payback, NPV
• Sensitivity: best/base/worst

Estimate end-to-end costs from MVP to scalable multi-tenant platform.

• Core modules and compliance
• Cloud, CI/CD, security
• Scale: multi-region, SSO

Get an instant estimate for team size, skills, and monthly run-rate.

• Role-wise cost breakdown
• Seniority and model factors
• Monthly total with overheads

Estimate end-to-end engineering costs from discovery through launch and scaling.

• Feature scope and complexity
• Team composition and velocity
• Environment and DevOps costs

Calculate data quality, governance, and pipeline management investments accurately.

• Ingestion and ETL volume
• Governance and catalog scope
• Storage, retention, compliance

Forecast design, build, and performance optimization for data warehouses.

• Warehouse size and workload
• ELT complexity and scheduling
• Performance, partitioning, tuning

Estimate dashboards, predictive models, and company-wide analytics adoption costs.

• Scope: reports, dashboards, KPIs
• Data sources and refresh cadence
• Team mix and tool costs

Estimate setup, licensing, and rollout for a scalable helpdesk.

• Agents, channels, SLAs
• Automation and self-service scope
• Integrations and reporting needs

Scope strategic assessments, roadmaps, and execution support for initiatives.

• Discovery and assessment depth
• Advisory vs. execution mix
• Duration and deliverable count

Price test strategy, coverage, and automation enablement across platforms.

• Test types and depth
• Automation framework setup
• Environments and tooling

Estimate penetration testing scope, depth, and remediation validation cycles.

• App count and complexity
• Scope: web, mobile, APIs
• Reporting and retest rounds

Model licensing, data ingestion, and ongoing monitoring operations costs.

• EPS/data volume tiers
• Rule sets and use cases
• Deployment and monitoring effort

Forecast manual and automated testing efforts across releases and environments.

• Test cases and coverage
• Automation pipeline setup
• Cycles, environments, devices