An AI tutor built around one organization's curriculum, or a tutoring platform pointed at the curriculum your district actually teaches
On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.
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Khanmigo is an AI tutor built on top of Khan Academy's curriculum, and that coupling is the point. The tutor knows the lesson the student is on, the mastery model behind it, and the pedagogy the content was written for.
If your district teaches Khan Academy's curriculum, that integration is difficult to beat and this comparison is close.
If your district teaches its own scope and sequence, adopted textbooks, or state-specific standards, the coupling inverts. A tutor that knows someone else's curriculum well knows yours not at all, and cannot be pointed at it.
A self-hosted platform starts from the opposite end: the district supplies the curriculum, the standards, and the assessment context, and the AI tutors against those. This page compares the two on curriculum fit, student-data location, and what a district can change.
by ibl.ai
Owned agentic AI platformby Khan Academy
Hosted AI tutor tied to one curriculum| Criteria | Self-Hosted AI | Khanmigo |
|---|---|---|
| Out-of-the-Box Readiness | Production agents for tutoring, guided practice, writing coaching, and teacher lesson support once deployed, configured to how your organization actually works. | Immediately useful — tutoring tightly coupled to Khan Academy's own curriculum and mastery model, from a mission-driven nonprofit. |
| Integration With Your Systems | Deep integration with Clever, ClassLink, Google Classroom, and PowerSchool over APIs and MCP, running inside your own network. | Connects to common systems, bounded by the connectors the vendor has built. |
| Extensibility | Build and own workflows the vendor has not thought of, because you hold the code. | Configurable within the product; capabilities outside it require the vendor to build them. |
| Any-LLM & Model Control | Run any open or commercial model, route by cost, latency, and capability, and switch anytime. | Runs on the models Khan Academy has selected. |
| Criteria | Self-Hosted AI | Khanmigo |
|---|---|---|
| Self-Hosting / On-Prem / Air-Gapped | Runs on your servers, your private cloud, or fully air-gapped with zero external calls. | Runs in Khan Academy's cloud; it cannot be self-hosted or air-gapped. |
| Where the Data Lives | student learning interactions and minors' data never leaves your environment, and every interaction is logged for audit. | Processed and retained on the vendor's infrastructure under your agreement. |
| Source Code Ownership | You hold the full source and can audit, fork, and extend every layer. | You rent access; the platform and its roadmap belong to the vendor. |
| Fit With FERPA and COPPA | Data stays inside your perimeter, which is the simplest posture to evidence under FERPA and COPPA. | Vendor compliance coverage under shared-responsibility terms. |
| Criteria | Self-Hosted AI | Khanmigo |
|---|---|---|
| Cost at Scale | Flat license plus compute you own — extending access across K-12 districts does not multiply the bill. | per-student district licensing, so cost grows with the size of the organization rather than the work done. |
| Time-to-Value | Requires deployment and integration, or a partner who does both for you. | Usable almost immediately with no infrastructure work. |
| Support & Maintenance | Self-managed, or fully supported with forward-deployed engineers. | Fully managed by Khan Academy. |
| What You Keep If the Relationship Ends | A working platform and all your data, still running on your own infrastructure. | Whatever the contract allows you to export. |
A district-owned platform ingests the district's adopted materials, pacing guides, and state standards, so the tutor is aligned to what students are actually assessed on.
Khanmigo's alignment to Khan Academy's own content is genuinely deep — better than a general assistant prompted to be a tutor.
Districts teaching Khan's curriculum should weigh that integration heavily. Districts teaching their own should recognize that curriculum alignment is the feature, and it is not transferable.
Self-hosting keeps tutoring transcripts in district systems, where they can inform intervention, feed local analytics, and stay under district retention policy.
In a hosted tutor, those interactions live with the provider, and the district's view of them is whatever the product reports.
Districts serious about using tutoring data for MTSS and intervention need that data in their own systems, not summarized back to them.
An owned platform can run whichever model performs best for a subject and grade band, and change it as models improve or costs fall.
A hosted tutor uses the models its provider selected, and a district inherits both the capability and the cost profile of that choice.
Over a multi-year deployment, the ability to change models is worth more than any single model's current advantage.
A tutor aligned to another organization's content cannot be pointed at your scope and sequence, which is the alignment that matters for your assessments.
When the curriculum is already Khan's, the tutor's coupling to that content is a real and hard-to-replicate advantage.
MTSS and early-warning workflows need the underlying interaction data in district systems, not reported back through a vendor dashboard.
Self-hosting keeps minors' tutoring interactions inside district-controlled systems rather than transmitting them to a provider.
Timeline: Four to ten weeks depending on integration count and review requirements
Timeline: Days to a few weeks
ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.
ibl.ai tutors against your curriculum rather than someone else's. The district supplies adopted materials, pacing guides, and state standards, and the platform indexes them so tutoring aligns to what students are actually assessed on. Because it runs inside district infrastructure, tutoring transcripts stay in district systems where they can feed MTSS and intervention workflows directly. Agentic OS connects to Clever, ClassLink, Google Classroom, and PowerSchool, applies district-configured guardrails before any model responds to a student, and can run whichever model performs best per subject. You own all the code and the data, run any model, and can deploy on any cloud, on-premise, or air-gapped.
Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours.
Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform.
Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.
Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity.
1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.
ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.