Use Cases
Three Ways to Roll Out Enterprise AI
Same scenario, three radically different approaches. A 500-employee company wants to deploy AI across Engineering, Customer Support, and Finance using AWS + Azure + GCP. Here's what each path actually costs. In time, money, and risk.
The Scenario
A 500-employee company wants to deploy AI across 3 departments. Engineering, Customer Support, and Finance, using AWS + Azure + GCP.
Three Approaches, One Goal
The Consulting Route
Timeline: 6-12 months
Year 1 cost: $500K-$2M
Ongoing: $100K-$200K/yr maintaining custom integrations
Token usage: ~2M tokens/month once live (single-vendor, unoptimized)
Models: GPT-4o for everything (Azure-only, vendor lock-in from consultant's recommendation)
Monthly AI spend: ~$30/month at 2M tokens… but they don't get here for 6-12 months
Total Year 1: $500K-$2M+ with AI not live until month 8-12
- e.g., Deloitte, Accenture
- Consulting engagement + internal resources
- Consultant picks one model, one cloud. Strategy is stale by delivery
- Models change quarterly; their recommendation is a point-in-time snapshot
- Compliance recommendations delivered as PDFs, not automation
The Patchwork Route
Timeline: 4-6 months
Year 1 cost: $200K-$400K
Ongoing: $8K-$15K/yr tooling + 2-3 platform engineers at $150K+ each
Token usage: ~10M tokens/month (multi-cloud, no cost-optimized routing)
Models: GPT-4o (60%), Claude 3.5 Sonnet (25%), Gemini Flash (15%). No intelligent per-task routing
Monthly AI spend: $200-$400/month at 10M tokens on premium models
Total Year 1: $250K-$450K with 2-3 FTE tied up in platform maintenance
- DIY with point solutions: Portkey ($499/mo routing) + Helicone ($150/mo observability)
- Custom compliance scripts + LangChain/CrewAI for agents
- Separate RAG pipeline per cloud provider
- Every new department = new integration project
- Team spends more time maintaining the stack than building features
Bonito
Timeline: Same day → 1 week
Year 1 cost: ~$17K
Ongoing: $499/mo Pro + 3 agents at $349/mo each
Token usage: ~10M tokens/month (same workload, smart routing)
Models: Nova Lite (60%), GPT-4o Mini (20%), Gemini 2.5 Flash (15%), GPT-4o (5%), auto-routed per task
Monthly AI spend: $60-$80/month at 10M tokens with smart routing
Total Year 1: ~$18K all-in
- Same-day connection to all 3 clouds, 1 week to all departments live
- Smart routing sends each task to the optimal model automatically
- 60% of requests go to Nova Lite for classification and drafts
- Only 5% of requests need GPT-4o, complex analysis only
- Built-in governance, compliance, and cost attribution from day one
Side-by-Side Comparison
Consulting | Patchwork | Bonito | |
|---|---|---|---|
| Time to first AI in production | 6-12 months | 4-6 months | Same day |
| Year 1 all-in cost | $500K-$2M | $250K-$450K | ~$18K |
| Monthly AI inference spend | ~$30 (single model) | $200-$400 (unoptimized) | $60-$80 (smart routing) |
| Monthly token volume | ~2M (single use case) | ~10M (multi-team) | ~10M (multi-team) |
| Models in use | 1 (consultant's pick) | 3-4 (manual selection) | 4-6 (auto-routed per task) |
| Unified governance | ❌ PDF recommendations | ❌ Manual scripts | ✅ Built-in real-time |
| Agent governance | ❌ | ❌ DIY | ✅ Default-deny, budget caps |
| Compliance automation | ❌ One-time audit | ⚠️ Custom scripts | ✅ SOC-2/HIPAA/GDPR checks |
| Cost attribution | ❌ | ⚠️ Partial (per-tool) | ✅ Per-key, per-team, per-request |
| New department rollout | New engagement ($$$) | New integration (weeks) | Add an agent (minutes) |
| Vendor count | 1 expensive one | 5+ | 1 |
| Engineers required | 0 (outsourced) then 2-3 | 2-3 FTE | 0 (self-serve) |
Risk Deep-Dive
Every approach has trade-offs. Here's an honest look at the risks.
The Consulting Route
- Model obsolescence. GPT-4o today might not be optimal in 6 months, but the consultant's strategy is locked in
- Vendor lock-in. The consultant picked one cloud, one model vendor. Switching costs are enormous.
- No operational layer. You get a strategy deck and architecture diagrams, not running infrastructure
- Knowledge walkout. When the consultant engagement ends, institutional knowledge leaves with them
- Compliance is a snapshot, one-time audit recommendations in a PDF, not continuous automated checks
The Patchwork Route
- Integration fragility. One vendor pushes an update and breaks the chain. You're now debugging 5 vendor APIs.
- Security gaps between tools. Portkey handles routing, Helicone handles logging, but neither handles the gaps between them
- No unified audit trail, compliance has to stitch together logs from 5+ tools to answer 'who accessed what, when'
- Agent sprawl without governance. LangChain/CrewAI agents run without default-deny or budget caps
- Engineering team becomes the 'AI platform team'. They spend more time maintaining integrations than building product features
Bonito
- Newer product. Less battle-tested than established consulting firms or Portkey's 3+ year track record
- Smaller provider catalog: 3 cloud providers (AWS, Azure, GCP) vs Portkey's 60+. If you need a niche provider, it may not be supported yet.
- Single vendor dependency. Mitigated by OpenAI-compatible API format (portable) and standard IaC (Terraform) for infrastructure definitions
- No SOC-2 Type II yet. In progress, but not complete. May be a blocker for some enterprise procurement processes.
The Bottom Line
$500K-$2M
Consulting Route
6-12 months to first AI in production
$250K-$450K
Patchwork Route
4-6 months, 2-3 FTE dedicated
~$18K
Bonito
Same day to connected, 1 week to all departments
Sound like your team?
If you're running AI workloads across multiple cloud providers and want unified control without the infrastructure overhead, Bonito was built for you.