Baseline Rates by Role (2026)

These are what senior firms in India charge. Rates vary by experience, location (Bangalore vs Coimbatore), and specialization.

Why the range? Senior devs with 8+ years in AI cost more. Bangalore tech hubs charge 15–20% premium over tier-2 cities. Startups pay less; established firms charge more.

Cost by Project Type

MVP (AI Feature)

Scope: Single AI feature integrated into existing app (e.g., recommendation engine, document processing)

Timeline: 4–8 weeks

Team: 1 senior backend + 1 ML engineer + QA

Cost Range: $12k–25k

Standalone AI Product

Scope: New product built from scratch (SaaS, internal tool, bot)

Timeline: 8–16 weeks

Team: 2–3 backend, 1 ML, 1 frontend, 1 DevOps, 1 PM

Cost Range: $35k–75k

Enterprise AI Implementation

Scope: Custom workflow automation, complex integrations, compliance (HIPAA, SOC 2)

Timeline: 16–24+ weeks

Team: 3–5 engineers, specialist roles, dedicated PM

Cost Range: $80k–200k+

Team Composition Impact

The same project costs wildly different amounts based on who builds it:

Reality: Option A saves $20k upfront but usually costs $15k more fixing it later. Option C costs more but ships working software.

What Increases Cost (Beyond Base Rates)

• Compliance (HIPAA, SOC 2, GDPR) +40–60% (audit, encrypted storage, legal review)
• Real-time requirements +25% (WebSocket architecture, lower latency)
• Custom ML models +80–150% (training data, validation, iteration)
• Legacy system integration +30–50% (undocumented APIs, fragile systems)
• Multi-platform (web + mobile + iOS) +40% (more QA, native concerns)
• High-volume data processing +50% (optimization, data pipelines, scaling)
• Geographic dispersion (multiple teams) +20–30% (coordination overhead, time zones)

Hidden Cost Factors Nobody Mentions

1. Discovery Phase (Often Unpaid)

Good teams spend 3–5 days scoping ($1.5k–3k). You can pay nothing and ship the wrong thing, or pay for clarity. We include discovery in our project cost.

2. Post-Launch Support

Month 1 after launch: expect 20–30% of dev hours spent on bugs, edge cases, and production tuning. This is normal. Budget for it.

3. API & Infrastructure Costs

A chatbot needs LLM API calls ($500–2k/month). Vector database (Pinecone, Weaviate) runs $200–500/month. These are passed through, not included in dev costs.

4. Testing & QA

If not budgeted explicitly, it gets sacrificed. Proper QA adds 15–25% to timeline. Cheap teams skip it; good teams include it.

5. Technical Debt Velocity

A team that cuts corners can deliver 20% faster but leaves behind bugs. Month 6, you're debugging instead of shipping features. Real costs are hidden in maintenance.

Fixed Price vs Time & Materials

Fixed Price: "We'll build X for $50k. Scope is defined upfront. You pay that, nothing more."

✓ Budget certainty, aligned incentives

✗ Less flexibility if requirements shift

Time & Materials: "We'll spend up to 400 hours at $55/hr. You only pay for what we use."

✓ Flexible, good for exploratory work

✗ Incentivizes slow work (more hours = more $ for vendor)

Our take: Fixed price after a discovery sprint. You know exactly what you're getting and what it costs before we write code.

India vs Global: Cost Breakdown

The trade-off: India has a 2.5–3× cost advantage over the US, but you're paying for knowledge work. A senior firm will build faster than a junior freelancer. Don't confuse low hourly rate with low final cost.

What We Charge & Why

At ZAS Digital, we build AI products for $25k–80k depending on scope. Here's why:

• We're senior engineers, not junior contractors ($45–60/hr effective rate)
• We do discovery upfront to avoid costly scope creep
• We include QA, DevOps, and post-launch support
• We're based in India (cost advantage) but operate like a US firm (quality standard)
• Fixed price = you know your budget before we start

We're not the cheapest. We're not the most expensive. We're the right balance of quality, speed, and price certainty.