Every CFO I've spoken with in the past year has asked the same question about AI infrastructure: "What's the real cost?" The answer is more complicated than most vendors want you to believe, and more expensive than most engineering teams admit.
After analyzing AI gateway implementations across 40+ enterprises, I've identified a pattern that consistently surprises leadership: the platforms that appear cheapest upfront often become the most expensive over 24 months. The reason isn't pricing manipulation. It's the hidden costs of incomplete solutions.
The Three Paths to AI Gateway Infrastructure
When enterprises need to route requests across multiple AI models, they typically consider three approaches:
Path 1: API Aggregators (OpenRouter, simple proxies) Upfront cost: Low Ongoing cost: Variable Hidden cost: High
Path 2: Build/Customize Open Source (LiteLLM, custom solutions) Upfront cost: Medium-High Ongoing cost: High Hidden cost: Medium
Path 3: Complete Enterprise Platforms (Full-featured gateways) Upfront cost: Medium Ongoing cost: Predictable Hidden cost: Low
Let me break down the actual financial implications of each path.
Path 1: The Aggregator Illusion
API aggregators like OpenRouter offer a compelling initial value proposition. Unified access to dozens of models, simple pricing, minimal setup. For a startup processing 100,000 requests monthly, the math looks straightforward:
Visible Costs (Monthly)
- API fees: $3,000-5,000 (depending on model mix)
- Platform fees: $0-100
- Total visible: ~$5,000/month
But here's what the spreadsheet doesn't show:
Hidden Cost 1: Manual Model Management Without intelligent routing, engineers manually select models per use case. When a new model launches (like Claude 3.5 Sonnet) or pricing changes, someone needs to evaluate, test, and update model selections across the codebase.
Time cost: 2-4 engineer hours/month Dollar cost: $300-600/month
Hidden Cost 2: Outage Response Aggregators don't provide automatic failover. When Anthropic experiences downtime (which happens 2-3 times monthly on average), engineers scramble to implement fallbacks or features fail.
Time cost: 4-8 engineer hours/month (average) Dollar cost: $600-1,200/month
Hidden Cost 3: Cost Attribution Finance needs to understand AI spend by team, feature, and project. Aggregators provide total usage, not granular attribution. Building cost allocation requires custom logging and analysis.
Time cost: 8-16 hours initial setup, 2-4 hours/month maintenance Dollar cost: $1,500 initial + $300-600/month
Hidden Cost 4: Compliance Requirements Enterprise customers require SOC 2 compliance, audit logging, and data residency controls. Basic aggregators don't provide these. You either decline enterprise deals or build custom compliance layers.
Opportunity cost: 1-3 enterprise deals/year Dollar cost: $50,000-300,000/year in lost revenue
Hidden Cost 5: No Caching Without semantic caching, every request incurs full cost. For applications with repetitive queries (support bots, FAQ systems, documentation assistants), 30-60% of requests could be served from cache.
Overspend: 30-60% of API costs Dollar cost: $1,500-3,000/month
Actual Total Cost (Monthly): $8,500-15,000 vs. Visible Cost: $5,000
The aggregator that looked 50% cheaper than enterprise alternatives is actually 50-100% more expensive.
Path 2: The Build Trap
Engineering teams often propose building custom gateway infrastructure or deploying open-source solutions like LiteLLM. The logic: "We'll customize it exactly to our needs and own the roadmap."
The initial estimate typically looks like this:
Visible Costs (Year 1)
- Infrastructure (cloud compute, storage): $24,000
- Engineering time (3-month build): $150,000
- Open-source licensing: $0
- Total visible: ~$175,000
Here's the reality:
Hidden Cost 1: Scope Expansion The "3-month build" becomes 6-9 months as requirements emerge. Intelligent routing alone requires building task classification, latency prediction, and cost optimization. Most teams underestimate complexity by 2-3x.
Actual engineering cost: $300,000-450,000
Hidden Cost 2: Ongoing Maintenance Every API provider changes their API periodically. New models require integration. Security patches need deployment. Based on industry data, gateway maintenance requires 0.3-0.5 FTE.
Annual maintenance cost: $45,000-75,000
Hidden Cost 3: Feature Debt Your custom solution works for today's requirements. Next quarter, the business needs team-level cost attribution. Six months later, compliance requires enhanced logging. A year in, performance requires semantic caching.
Each feature is another build cycle. Most custom gateways accumulate 6-12 months of feature debt within two years.
Deferred feature cost: $100,000-200,000/year
Hidden Cost 4: Key Person Risk The engineer who built the gateway becomes irreplaceable. Their departure triggers either panic hiring or system rewrites. I've seen this scenario play out at four different companies.
Risk-adjusted cost: $50,000-100,000
Hidden Cost 5: Opportunity Cost Engineering time spent on infrastructure isn't spent on product differentiation. For a growth-stage company, gateway infrastructure competes with revenue-generating features.
Opportunity cost: 3-6 months of product velocity
Actual Total Cost (Year 1): $500,000-750,000 vs. Visible Cost: $175,000
Custom builds aren't cheaper. They're just differently expensive, with costs distributed across engineering budgets, technical debt, and opportunity cost.
Path 3: The Complete Solution Math
Enterprise AI gateway platforms typically price based on usage volume with platform fees. Using Swfte Connect as an example:
Visible Costs (Year 1)
- Platform fees: $36,000-120,000 (depending on tier)
- API passthrough: At cost (no markup) or BYOK
- Setup: Included
- Total visible: $36,000-120,000
What's included that you'd otherwise build or buy:
Included: Intelligent Routing Cost-aware, latency-aware, and capability-aware routing automatically optimizes every request. Based on customer data, intelligent routing reduces API costs by 30-45%.
Value: $30,000-100,000/year in cost savings
Included: Automatic Failover When providers experience issues, requests automatically route to configured alternatives. No engineering intervention required.
Value: $15,000-30,000/year in avoided engineering time
Included: Semantic Caching Similar requests return cached responses. For repetitive workloads, this reduces API costs by 30-60%.
Value: $25,000-75,000/year in cost savings
Included: Enterprise Compliance SOC 2 Type II certification, GDPR compliance, and audit logging included. No custom development required.
Value: $75,000-150,000 in implementation costs avoided
Included: Cost Attribution Per-team, per-project, per-feature cost visibility out of the box. Finance can budget accurately without custom tooling.
Value: $10,000-20,000/year in reporting automation
Included: Ongoing Development New model integrations, performance improvements, and feature additions without internal engineering investment.
Value: $100,000+/year in maintenance costs avoided
Net Cost After Value Recognition: Often Negative
For enterprises processing significant AI volume, complete gateway platforms frequently generate positive ROI through cost optimization alone, before considering operational benefits.
The OpenRouter to Enterprise Gateway Migration
Let me share a specific example. A B2B SaaS company (150 employees, $40M ARR) started with OpenRouter for their AI-powered features.
Year 1 with OpenRouter:
- API costs: $180,000
- Engineering time (model management, fallbacks, monitoring): $85,000
- Lost enterprise deal (compliance gap): $200,000 ARR
- Total cost of ownership: $265,000 + opportunity cost
Year 2 with Complete Gateway:
- Platform fees: $72,000
- API costs (with intelligent routing + caching): $95,000
- Engineering time: $15,000 (monitoring, not building)
- Total cost of ownership: $182,000
Annual savings: $83,000 cash + recovered enterprise opportunity
The migration paid for itself in four months. More importantly, engineering redirected 600+ hours annually from infrastructure maintenance to product development.
The Decision Framework
Use this framework to evaluate AI gateway options:
Choose an Aggregator When:
- Processing fewer than 50,000 requests monthly
- No enterprise compliance requirements
- Simple, predictable workloads
- Engineering capacity for custom fallback logic
- Cost optimization isn't critical
Choose Build/Open Source When:
- AI infrastructure is a core competency
- Unique requirements that no platform addresses
- Dedicated platform engineering team (2+ engineers)
- Long-term commitment to maintenance
- Regulatory requirements mandate self-hosted
Choose a Complete Platform When:
- Processing 100,000+ requests monthly
- Enterprise compliance requirements exist
- Multiple teams or products share AI infrastructure
- Engineering should focus on product, not infrastructure
- Cost optimization and reliability matter
The Questions Finance Should Ask
When evaluating AI gateway investments, finance teams should ask:
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What's the fully-loaded engineering cost? Include setup, maintenance, and feature development—not just platform fees.
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What's the cost optimization potential? Intelligent routing and caching can offset 30-60% of platform costs.
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What's the compliance gap cost? Lost enterprise deals and custom compliance builds add up quickly.
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What's the opportunity cost? Engineering time has alternative uses. Infrastructure isn't differentiation.
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What's the 24-month TCO? Year 1 costs differ dramatically from Year 2 costs across all paths.
The Bottom Line
The cheapest AI gateway is rarely the lowest-priced one. It's the one that delivers the most value relative to total cost of ownership.
For most enterprises, that means a complete platform that handles intelligent routing, failover, caching, compliance, and observability natively. The visible costs are higher than an aggregator. The actual costs—including engineering time, missed optimizations, and compliance gaps—are typically 40-60% lower.
The AI gateway market is still educating buyers on total cost of ownership. As the market matures, I expect the conversation to shift from "what's the API markup?" to "what's the operational value?" That shift will favor complete solutions.
Ready to see the ROI of a complete AI gateway? Explore Swfte Connect to learn how enterprises reduce AI costs by 40% while improving reliability. For details on how intelligent routing optimizes costs, see our AI model routing guide. To understand the multi-model strategy behind these savings, read why single-model AI strategies are obsolete. For the technical architecture perspective, explore our guide on AI gateway flexibility and ease.