Cost ranges in plain numbers
Honest ranges based on our portfolio (USD, indicative — your specific engagement varies):
- Claude Readiness Assessment: $20K–$50K, 2–4 weeks. Audit, eval data, recommendation, build proposal.
- Single-workload Claude API integration: $80K–$250K, 6–12 weeks. Production-grade integration with eval harness, cost telemetry, hardening.
- MCP server engineering: $100K–$300K, 8–14 weeks. Custom MCP server with tenancy, audit, schema evolution discipline.
- RAG architecture deployment: $120K–$400K, 8–16 weeks. Production RAG with chunking, hybrid search, re-ranking, eval CI.
- Agentic AI deployment: $150K–$600K, 12–24 weeks. Production agent with state durability, escalation, observability, shadow-mode validation.
- Claude Code enterprise rollout: $80K–$500K depending on org size, 11 weeks pilot + 3–9 months scale-out.
- Multi-region Claude implementation programme: $400K–$2M+, 16–40 weeks.
- Managed Claude services retainer: $15K–$200K+ monthly, depending on tier.
What drives cost
Top cost drivers in order of impact:
- Team size — number of engineers and weeks engaged. Single biggest variable.
- Engagement depth — single integration vs full programme; build-only vs build-plus-operate.
- Regulatory complexity — HIPAA, PCI DSS, GxP, MiFID II add validation cycles, security review, and compliance documentation that scale calendar and effort.
- Workload novelty — well-understood patterns (Claude API integration into web app) cost less than novel patterns (multi-agent orchestration in regulated industry).
- Multi-region or multi-tenancy — significantly increases architecture complexity.
- Anthropic procurement — direct API simplest, hyperscaler routing slightly more complex, BAA / DPA negotiation adds calendar.
- Knowledge transfer scope — engagements with internal-team training and CoE handover cost more than pure delivery.
What's NOT included in NINtec's quoted price
We quote engineering services. Two costs you bear separately:
- Anthropic API consumption — per-token pricing depending on model tier and volume. We model this in Discovery; for typical enterprise workloads it is a small fraction of engineering cost. Some clients route this through us; many contract directly with Anthropic.
- Hyperscaler infrastructure — if deploying via AWS Bedrock, GCP Vertex AI, or Azure, the underlying compute and networking costs are billed by the hyperscaler. Same modelling principle.
Most engagements include cost dashboards and a clear breakdown so the total cost of ownership is visible.
Pricing model options
Three pricing models, chosen per engagement:
- Fixed-price milestone — for well-scoped engagements (Readiness Assessment, single MCP server, single-workload integration). Risk borne by NINtec; price is predictable.
- Time-and-materials with monthly cap — for ambiguous-scope engagements (full agentic-system development, programmes that evolve). Risk shared; price floats but caps protect against runaway.
- Retainer — for managed services, fractional advisory, extended-team augmentation. Predictable monthly billing; resource allocation flexible within tier.
Most engagements start as fixed-price (Readiness Assessment) and move into either time-and-materials (build) or retainer (operate).
How to budget realistically
If you are budgeting for a Claude programme:
- Start with $30K–$50K for a Readiness Assessment that produces a defensible build proposal
- Add $150K–$400K per single-workload production deployment
- Budget $30K–$80K monthly for the first 6–12 months of managed operations after launch
- For regulated industries, increase by 30–50% for compliance and validation overhead
- Budget separately for Anthropic API consumption (typically 10–25% of engineering cost on a steady-state basis)
- For Claude Code rollouts, budget per-engineer licensing cost (Anthropic enterprise) plus engagement cost
NINtec's Discovery phase produces a precise budget for your specific scope.
ROI framing
Claude development cost is rarely the right framing — engagements deliver value through engineering productivity uplift, automated workflows, or new product capability. Common ROI sources:
- Engineering productivity — 30–60% velocity uplift on Claude Code-enabled teams
- Customer service deflection — 20–50% routine-ticket reduction with Claude-powered first-line
- Process automation — agentic workflows that reduce hours-per-shift on routine triage
- Product differentiation — new AI features that drive customer acquisition or retention
We model ROI in Discovery so the engagement decision is grounded in business outcome, not just cost.