January 10, 2026
The Inference Cost Death Spiral
I keep having the same conversation with CFOs.
It starts with excitement. They’ve seen the demos. AI is going to transform everything - customer service, document processing, decision support, coding. The ROI projections look incredible.
Then I show them the inference cost projections. The room gets quiet.
We’re in the early innings of AI deployment. Most enterprises are running pilots, handling hundreds or thousands of requests per day. The costs look manageable. But pilots aren’t production. And nobody’s doing the math on what happens when you actually scale.
The Hidden Multiplication
Here’s what most people miss: AI costs don’t scale linearly with usage. They compound.
A single user interaction in a modern AI-enabled application isn’t one model call. It’s five, ten, maybe fifteen.
Anatomy of a "Simple" AI Interaction
User: "Summarize yesterday's sales and flag any anomalies"
That's 12 model calls for one user question.
Now multiply by users, interactions per user, and growth.
And that’s just one application. The modern enterprise is deploying AI across dozens of use cases. Customer service. Internal knowledge bases. Document processing. Code assistance. Email drafting. Meeting summaries.
Each application has its own cascade of model calls. Each user interaction triggers multiple of them. The numbers add up fast.
The Real Math
Let’s run the actual numbers for a mid-size enterprise.
Starting assumptions:
- 5,000 employees
- 3 AI-enabled applications deployed
- Average 20 interactions per employee per day (conservative)
- Average 8 model calls per interaction
- Blended cost of $0.004 per call (mix of small and large models)
Inference Cost Projection
DAILY
5,000 x 20 x 8 x $0.004
$3,200/day
MONTHLY
$3,200 x 22 working days
$70,400/month
ANNUAL
$844,800/year
But this assumes:
- No growth in usage
- No additional applications
- No customer-facing AI
- No agent loops
- No failed retries
$845K per year for a mid-size company running three internal AI tools. That’s not transformative AI - that’s basic productivity tooling.
Now layer in realistic growth.
The Growth Multipliers
Usage growth: When AI tools actually work, people use them more. We consistently see 3-5x usage growth in the first year after deployment. That $845K becomes $2.5M-$4.2M.
Application proliferation: Success breeds expansion. One AI application becomes five becomes fifteen. Each with its own inference costs.
Customer-facing AI: Internal tools are the easy case. Customer-facing applications - chatbots, recommendation engines, personalization - run at a completely different scale. A consumer-facing AI feature can generate millions of interactions per day.
Agent loops: The hot new architecture is AI agents - systems that reason, plan, and take actions autonomously. Agents don’t make one model call per interaction. They make dozens, sometimes hundreds, as they loop through planning and execution cycles.
Cost Growth Trajectory
This assumes conservative growth rates. Actual customer-facing deployments can grow much faster.
This isn’t a scare scenario. This is what the trajectory looks like when AI actually succeeds. The “problem” is that it works.
The Hidden Costs
The raw inference cost is just the beginning. There’s a constellation of hidden costs that multiply the base number:
Failed requests and retries. Models fail. Rate limits hit. Timeouts happen. Every retry is another inference call. In production, we typically see 10-15% overhead from retries alone.
Evaluation and monitoring. You can’t deploy AI responsibly without evaluation. That means running test suites, monitoring outputs, checking for drift. Each evaluation is more inference.
Development and testing. Before code goes to production, developers are testing prompts, debugging outputs, running experiments. Development environments can consume as much inference as production.
Safety and compliance. Content filtering, bias checking, compliance verification - each adds another model call to the chain. For regulated industries, this isn’t optional.
Hidden Cost Multipliers
Reality: Your true inference cost is roughly 2x your naive calculation.
The Vendor Lock-In Trap
Here’s where it gets strategic.
Most enterprises are building on one or two foundation model providers. The integrations are deep - prompts tuned for specific models, evaluation sets built on model outputs, retrieval systems optimized for particular embedding spaces.
Switching costs are enormous. Migrating from one provider to another means re-tuning prompts, rebuilding evaluation sets, retraining users, and accepting a period of degraded performance.
The providers know this. Today’s pricing is penetration pricing. Once you’re locked in, once your applications depend on their models, once your teams are trained on their tools - that’s when pricing power kicks in.
We’ve already seen this movie with cloud infrastructure. The AI inference market will follow the same playbook.
flowchart TD
subgraph Phase1["PHASE 1: ADOPTION"]
A1["Competitive pricing"]
A2["Free tier for experimentation"]
A3["Easy integration"]
A4["'Start building today!'"]
end
subgraph Phase2["PHASE 2: INTEGRATION"]
B1["Prompts tuned for specific model"]
B2["Evaluation sets built on model outputs"]
B3["Team expertise concentrated"]
B4["Retrieval optimized for embeddings"]
end
subgraph Phase3["PHASE 3: DEPENDENCY"]
C1["Switching cost > short-term savings"]
C2["Applications deeply coupled"]
C3["Institutional knowledge tied to platform"]
C4["'We can't migrate without major rework'"]
end
subgraph Phase4["PHASE 4: EXTRACTION"]
D1["Price increases announced"]
D2["Volume discounts require multi-year commits"]
D3["'Strategic partnership' discussions"]
D4["Limited negotiating leverage"]
end
Phase1 --> Phase2
Phase2 --> Phase3
Phase3 --> Phase4
style Phase1 fill:#22c55e20,stroke:#22c55e
style Phase2 fill:#eab30820,stroke:#eab308
style Phase3 fill:#f9731620,stroke:#f97316
style Phase4 fill:#ef444420,stroke:#ef4444
What Smart Enterprises Are Doing
The enterprises that will win the AI cost game are making strategic moves now, before lock-in becomes irreversible.
Model-Agnostic Architecture
Build abstraction layers. Your applications should talk to an AI gateway, not directly to model APIs. When pricing changes or better models emerge, you can swap backends without rewriting applications.
This isn’t just about cost - it’s about optionality. The model landscape is evolving fast. Today’s best model might be obsolete in eighteen months. Architecture that accommodates change is architecture that survives.
Tiered Model Strategy
Not every task needs GPT-5 or Claude 4.5 Opus. Most interactions can be handled by smaller, cheaper models. Reserve the expensive models for tasks that actually require their capabilities.
We see enterprises cut inference costs by 60-70% just by implementing intelligent routing - using small models for simple tasks and escalating to larger models only when needed.
flowchart TD
Request["INCOMING REQUEST"] --> Router["ROUTER<br/>(Tiny LLM)"]
Router --> T1["TIER 1: Small<br/>$0.0005/call"]
Router --> T2["TIER 2: Medium<br/>$0.003/call"]
Router --> T3["TIER 3: Large<br/>$0.03/call"]
subgraph Tier1Tasks["Tier 1 Tasks"]
T1A["FAQ"]
T1B["Classify"]
T1C["Extract"]
T1D["Format"]
end
subgraph Tier2Tasks["Tier 2 Tasks"]
T2A["Summary"]
T2B["Draft"]
T2C["Code"]
T2D["Analysis"]
end
subgraph Tier3Tasks["Tier 3 Tasks"]
T3A["Complex reasoning"]
T3B["Creative"]
T3C["Multi-step"]
end
T1 --> Tier1Tasks
T2 --> Tier2Tasks
T3 --> Tier3Tasks
style T1 fill:#22c55e,stroke:#16a34a,color:#000
style T2 fill:#eab308,stroke:#ca8a04,color:#000
style T3 fill:#ef4444,stroke:#dc2626,color:#fff
Cost Impact:
Before routing: 100% requests x $0.03 = $0.03 average
After routing: 70% x $0.0005 + 25% x $0.003 + 5% x $0.03 = $0.0026 average
Result: ~90% cost reduction with minimal quality impact
Aggressive Caching
Many AI interactions are repetitive. The same questions get asked, the same documents get processed, the same patterns recur.
Semantic caching - recognizing when a new request is similar enough to a cached response - can eliminate huge numbers of inference calls. We’ve seen caching hit rates of 30-50% in production systems.
Open Model Investment
The open-source model ecosystem is maturing fast. Models like Llama 4, Mistral, and their derivatives are approaching frontier performance for many tasks.
Building capability to run open models - whether self-hosted or through inference providers - gives you pricing leverage and reduces dependency on any single vendor.
Cost Observability
You can’t optimize what you don’t measure. Implement detailed cost tracking at the application, feature, and user level. Know exactly where your inference spend is going.
The patterns will surprise you. Often, a small number of users or features account for a disproportionate share of costs. Targeted optimization on those hotspots can yield dramatic savings.
The Rotavision Approach
Our Sankalp platform includes an AI gateway that implements model-agnostic routing, intelligent tiering, and semantic caching. We built it because we saw our customers heading into the inference cost death spiral and wanted to give them an off-ramp.
Guardian provides the observability layer - tracking costs per request, identifying optimization opportunities, and flagging runaway spend before it becomes a problem.
The Uncomfortable Truth
AI is transformative. The demos are real. The capabilities are genuine. The productivity gains are achievable.
But the economics are brutal if you don’t manage them proactively. The inference cost death spiral is real, and most enterprises are sleepwalking into it.
The winners will be organizations that treat AI inference as a strategic resource - measured, optimized, and managed with the same rigor they apply to compute and storage.
The losers will be organizations that scale AI without watching the meter, then wonder why their cloud bills exploded.
The time to build cost discipline is now, while you’re still small enough for the numbers to be manageable. Don’t wait until $35M per year to start optimizing.
AI transformation is real. So are the costs. Plan accordingly.
Want to get ahead of the inference cost curve? Sankalp provides model routing, caching, and cost observability for enterprise AI deployments. Let’s discuss your architecture before costs spiral.
