Short answer: High-Volume Mobile companies paying per-query cloud AI fees can eliminate that variable cost by moving inference on-device — the model runs on the user's hardware, not yours. Wednesday scopes and ships this in 4–6 weeks.
Your cloud AI invoice varies by 40% month to month depending on user behavior. Your finance team can't budget a line item that swings $30K based on how often users trigger the search feature.
Cloud AI billing is variable by design. The only structural fix is removing the per-query charge — which means moving inference off the cloud.
The Four Decisions That Determine Whether This Works
Which features drive the variance. Usage-based AI billing spikes when specific features are triggered. A viral moment, a new onboarding flow, or a seasonal event can 3x the monthly bill. Identifying which features have the most variable call volume tells you which ones to migrate first to make the bill predictable. Migrating stable-volume features first saves money but doesn't solve the forecasting problem.
On-device cost structure. On-device AI has two costs: a one-time development cost and an ongoing compute cost that is zero per query. The migration pays back the development cost at a break-even point that depends on your current monthly API spend. For most teams at $10K+/month in AI API spend, the break-even is under 12 months. Your finance team needs this framing to approve the project.
Hybrid model for tail cases. Some AI tasks can't run on-device for all users. A hybrid architecture runs on-device for 80-90% of queries and falls back to cloud for edge cases and unsupported devices. The hybrid architecture caps the cloud bill at a predictable maximum instead of letting it float. The cap is what finance needs to put a number in the budget.
Finance reporting. Your finance team needs a way to track on-device AI compute cost in terms they can report. The project has to include an instrumentation layer that tracks model invocations and device-side compute time, even if those don't generate invoices. Without this, finance can't confirm the savings and you can't defend the project at the next board review.
Most teams spend 4-6 months discovering these decisions by building the wrong version first. A team that has shipped this before compresses that to 1 week.
On-Device AI vs. Cloud AI: What's the Real Difference?
| Factor | On-Device AI | Cloud AI |
|---|---|---|
| Data transmission | None — data never leaves the device | All inputs sent to external server |
| Compliance | No BAA/DPA required for inference step | Requires BAA (HIPAA) or DPA (GDPR) |
| Latency | Under 100ms on Neural Engine | 300ms–2s (network + server queue) |
| Cost at scale | Fixed — one-time integration | Variable — $0.001–$0.01 per query |
| Offline capability | Full functionality, no connectivity needed | Requires active internet connection |
| Model size | 1B–7B parameters (quantized) | Unlimited (GPT-4, Claude 3, etc.) |
| Data sovereignty | Device-local, no cross-border transfer | Depends on server region and DPA chain |
The right choice depends on your compliance constraints, query volume, and task complexity. Wednesday scopes this in the first week — before any code is written.
Why We Can Say That
We built Off Grid because we hit every one of these problems in production. Off Grid is the fastest-growing on-device AI application in the world, with 50,000+ users running it today.
It's open source, with 1,650+ stars on GitHub and contributors from across the world. It has been cited in peer-reviewed clinical research on offline mobile edge AI.
Every decision named above — model choice, platform, server boundary, compliance posture — we have made before, at scale, for real deployments.
How the Engagement Works
The engagement is four sprints. Each sprint is fixed-price. Each sprint has a named deliverable your team can put on a roadmap.
Discovery (Week 1, $5K): We resolve the four decisions — model, platform, server boundary, compliance posture. Deliverable: a 1-page architecture doc your CTO can take to the board and your Privacy Officer can take to Legal.
Integration (Weeks 2-3, $5K-$10K): We ship the on-device model into your app behind a feature flag. Deliverable: a working build your QA team can test against real workflows.
Optimization (Weeks 4-5, $5K-$10K): We hit the performance and compliance targets from the discovery doc. Deliverable: benchmarks signed off by your team.
Production hardening (Week 6, $5K): Edge cases, OS version coverage, app store and compliance review readiness. Deliverable: shippable build.
4-6 weeks total. $20K-$30K total.
Money back if we don't hit the benchmarks. We have not had to refund.
"They delivered the project within a short period of time and met all our expectations. They've developed a deep sense of caring and curiosity within the team." — Arpit Bansal, Co-Founder & CEO, Cohesyve
Ready to See the Numbers for Your App?
Worth 30 minutes? We'll walk you through what your current inference spend and usage volume mean for the business case, and what a realistic cost reduction target looks like.
You'll leave with enough to run a planning meeting next week. No pitch deck.
If we're not the right team, we'll tell you who is.
Book a call with the Wednesday team
Frequently Asked Questions
Q: How much can a high-volume mobile company save by moving AI on-device?
At 1M queries/month, a $0.002/query cloud API costs $2,000/month. On-device costs $0 per query after integration. At 10M queries/month: $20,000/month saved. Break-even on a $20K–$30K integration is typically 1–3 months.
Q: What's the quality trade-off between on-device and cloud AI?
For structured tasks — classification, extraction, form completion, search ranking — a 2B–7B on-device model performs comparably to cloud. For open-ended generation or broad world knowledge, cloud models have an advantage. The discovery sprint benchmarks your specific tasks against on-device candidates before committing.
Q: How long does a cloud-to-on-device migration take for high-volume mobile?
4–6 weeks. Week 1 identifies which tasks move on-device and defines quality benchmarks the on-device model must meet.
Q: What does a cloud-to-on-device AI migration cost?
$20K–$30K across four fixed-price sprints, money back if benchmarks aren't met. Typically recovered within 1–3 months of reduced API spend.
Q: What happens to AI quality when moving from GPT-4 to on-device?
Structured tasks often match cloud quality with a well-tuned 2B–7B model. Tasks requiring reasoning over long context or broad factual knowledge will show degradation. The discovery sprint benchmarks your specific tasks before any migration is committed.
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