Short answer: Enterprise 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 AI vendor raised prices 40% at renewal. Your mobile app is architecturally dependent on their API, and you have 6 weeks until the current contract expires. Your procurement team wants a path off the dependency.
Vendor lock-in in AI is a structural problem, not a negotiating one. Price increases at renewal are a symptom of an architecture built around a single proprietary endpoint.
The Four Decisions That Determine Whether This Works
Feature portability assessment. Not all features migrate with equal effort. Cloud AI features that use proprietary APIs — specific embeddings, fine-tuned model endpoints, multimodal capabilities not in open models — have higher migration cost than those using standard LLM completions. The audit of which features are portable in 6 weeks is the first deliverable, and it determines whether an exit is possible on your timeline.
Open model selection. The open-source model landscape in 2026 has capable options at 1B-7B parameters for most mobile AI tasks. Selecting the right model for your specific tasks requires benchmarking against your actual inputs, not general leaderboard performance. A model that ranks well on MMLU may underperform on your specific domain vocabulary.
Model hosting options. On-device eliminates the vendor relationship entirely. On-premise server hosting eliminates the vendor but keeps your infrastructure team in the loop. Both are viable exit paths with different cost structures and maintenance obligations. The right choice depends on your device distribution and your internal IT capacity.
Contract risk during migration. If you're 6 weeks from renewal and the migration takes 6 weeks, you need a short-term bridge. The project plan has to account for the contract timeline, not just the technical timeline. Ignoring this means signing a renewal you're trying to exit, or shipping an incomplete migration.
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 enterprise 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 enterprise?
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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