Short answer: Edtech 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 tutoring and feedback features cost $0.05 per student interaction. At 500,000 monthly interactions across your platform, that's $25K per month — and your student base is growing 40% per year.
At that growth rate, your AI cost line exceeds your content licensing cost in 18 months. That's a unit economics problem, not a feature problem.
The Four Decisions That Determine Whether This Works
Migration order by task viability. Tutoring, feedback, and content generation have different on-device viability. Concept explanation and FAQ responses can run on a small model with acceptable quality. Essay feedback requires more nuance and a larger model. Content generation for new practice questions requires the most capability. The migration order should follow viability, not volume — starting with the tasks most suited to on-device gives you early wins without compromising output quality.
Accuracy requirements for learning. An AI tutor that explains a concept incorrectly 5% of the time causes learning harm, not just user frustration. The accuracy floor for educational content is higher than for most other app categories. The model selection and testing protocol has to reflect this before anything ships. Testing on domain-specific educational content — not general benchmarks — is the correct validation method.
Offline learning on mobile. Students in emerging markets study on mobile with intermittent connectivity. An on-device tutoring model that works offline expands your addressable market while cutting costs. These two outcomes from the same architectural decision change the business case and the funding narrative for the project.
Learning data and personalization. On-device AI can personalize to the student's in-session performance without sending learning data to a server. The privacy and data governance story for parents and schools changes when learning behavior data stays on the device. Many school district procurement requirements make this distinction relevant to your sales cycle, not just your engineering team.
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.
React Native vs. Native vs. Hybrid: When to Use Each
| Factor | React Native | Native iOS + Android | Hybrid (WebView) |
|---|---|---|---|
| Code sharing | ~85% shared codebase | 0% — two separate codebases | 95%+ shared |
| Performance | Near-native for most interactions | Best possible | Noticeably slower |
| Development speed | 40–60% faster than native | Slowest | Fastest |
| Platform API access | Full, via native modules | Full | Limited |
| Team required | JavaScript/TypeScript engineers | iOS (Swift) + Android (Kotlin) specialists | Web engineers |
| Best for | Feature-rich apps, marketplaces, rapid iteration | Performance-critical apps, deep OS integration | Simple tools, prototypes |
For most product apps — marketplaces, fintech, edtech, consumer — React Native is the right default. Wednesday has shipped it at 500,000-user scale.
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.
"Wednesday Solutions' ownership is extremely high and works as if this was their project." — Pranay Surana, Director of Product Management, ALLEN Digital
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 edtech 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 edtech?
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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