Spartera vs Building Analytics Yourself
The true cost of DIY analytics infrastructure
Building your own external data acquisition and analytics infrastructure costs 10-20x more than you think. Comprehensive analysis of buy vs build economics.
The true cost of DIY analytics infrastructure
Building your own external data acquisition and analytics infrastructure costs 10-20x more than you think. Comprehensive analysis of buy vs build economics.
Building analytics yourself means: (1) Acquiring raw data (negotiate contracts, legal reviews, ongoing fees), (2) Building infrastructure (data warehouses, ETL pipelines, processing), (3) Creating analytics logic (SQL, ML models, calculations), (4) Maintaining everything forever. Spartera means calling an API. The cost and time difference is staggering.
Quick decision guide to help you choose the right solution
Side-by-side comparison of key features and capabilities
What makes these solutions different
DIY Year 1: $500K-$1.5M (team + infrastructure + data). Spartera Year 1: $10K-$100K (usage only). 5-year TCO: DIY $2M-$6M vs Spartera $50K-$500K. The economics aren't close.
DIY: 3-6 months from decision to first insight (legal, infrastructure, development). Spartera: <1 hour from signup to working analytics. Every month delayed is lost competitive advantage and revenue.
Spartera requires zero dedicated engineers - just API integration. DIY requires 2-4 FTE at $300K-$600K/year, plus opportunity cost of what they could build instead. That's $1.5M-$3M in engineering cost alone over 5 years.
With Spartera, your team builds your product - your competitive advantage. With DIY, your team builds analytics infrastructure - undifferentiated heavy lifting that provides zero competitive edge. Focus on what makes you unique.
Spartera is pure OpEx - pay as you go, scales with usage, no upfront commitment. DIY is massive CapEx - $300K-$1M upfront before any value, then high fixed costs. OpEx preserves cash and provides flexibility.
In fast-moving markets, 6-month delay for DIY infrastructure means competitors using Spartera launch their products first, capture market share, and establish brand. Time to market often matters more than marginal cost savings.
When each solution shines in practice
A Series A SaaS company ($5M raised) needs market data for their product. Building DIY infrastructure ($1M Year 1) would burn 20% of their runway on undifferentiated infrastructure. Spartera ($50K Year 1) preserves cash and lets them focus 100% of engineering on their unique product. No-brainer for startups.
A growth company (100 employees) wants to add competitor intelligence to their dashboard. They have 20 engineers working on core product. Dedicating 2-4 engineers to analytics infrastructure would slow product velocity 10-20%. Spartera costs $100K/year vs $800K/year DIY while preserving full engineering capacity for product.
An enterprise wants to launch a new product line requiring external market data. Executive team needs insights in Q1 to make go/no-go decision. DIY won't deliver insights until Q3 - too late. Spartera delivers insights immediately, enabling faster decision-making and execution.
A company is building an analytics platform that IS their product - they'll resell analytics to customers. The infrastructure IS their competitive advantage and IP. Building it yourself makes sense because analytics is your business, not just a data source. Rare case where DIY is justified.
A company making 500M+ API calls per month with highly custom analytics requirements. At this scale, DIY economics might work - you'd spend $5M+/year on Spartera but could build for $3M/year. However, this is <1% of companies - most will never reach this scale.
A company that already has data warehouse, ETL pipelines, engineering team, and all infrastructure fully built and paid for. Marginal cost of adding analytics is low. But this assumes sunk costs - if you're starting from scratch, Spartera is still better.
Common questions about this comparison