Your own instance or a shared one with logical partitions — what tenancy changes for isolation, customization, and the evidence you can hand an auditor
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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Multi-tenant means many customers share the same running system, separated by logic — tenant identifiers, row-level rules, and access controls enforced in software. Single-tenant means your deployment is its own instance, with separation enforced by infrastructure rather than by code paths.
Multi-tenancy is why SaaS is cheap and why features arrive continuously. The provider operates one system instead of a thousand, and every improvement reaches everyone at once. For most software this is straightforwardly the better design.
AI raises the stakes because the shared surface now includes embeddings, vector indexes, caches, and model context — places where a permissions bug does not merely expose a record but can surface it inside a generated answer to another customer.
This page covers what tenancy actually changes, where multi-tenant is entirely appropriate, and when the isolation has to be physical.
by ibl.ai on your infrastructure
Dedicated instance you controlby Managed AI SaaS providers
Shared infrastructure with logical isolation| Criteria | Single-Tenant / Owned | Multi-Tenant SaaS |
|---|---|---|
| Nature of the Boundary | Infrastructure-level: a separate deployment, database, and index that no other customer touches. | Logical: identifiers and access rules enforced in application code shared by every tenant. |
| Blast Radius of a Bug | A defect exposes your data to you. There is no adjacent tenant to leak into. | A filtering defect is a cross-customer disclosure — the recurring failure mode in shared AI systems. |
| Embedding and Index Separation | Vector stores and caches hold only your content, so retrieval cannot reach anyone else's material. | Shared indexes rely on per-query filtering, which must be correct on every path, every time. |
| Compliance Evidence | Dedicated infrastructure is simple to evidence: an auditor can see the boundary. | Isolation is demonstrated through vendor attestations and control testing rather than architecture. |
| Criteria | Single-Tenant / Owned | Multi-Tenant SaaS |
|---|---|---|
| Noisy-Neighbor Exposure | Your capacity is yours; another customer's batch job cannot slow your users down. | Shared inference capacity means someone else's spike can become your latency. |
| Model and Pipeline Customization | Choose models, tune retrieval, and modify the pipeline for your data without affecting anyone. | Configuration within the product's bounds; the pipeline is shared and cannot diverge per tenant. |
| Upgrade Timing | You decide when to take a release, so a model change never surprises a validated workflow. | The provider upgrades everyone together; behavior can shift under a prompt you already validated. |
| Data Residency Control | The instance runs where you put it, including on-premise and air-gapped networks. | Region selection where offered, but the environment is the provider's. |
| Criteria | Single-Tenant / Owned | Multi-Tenant SaaS |
|---|---|---|
| Entry Cost | A dedicated instance carries its own baseline cost regardless of how lightly it is used. | Shared infrastructure amortizes across customers, which is why it starts so cheap. |
| Cost at Large Scale | A dedicated deployment serves the whole organization at flat cost once utilization is high. | Per-seat pricing on shared infrastructure keeps scaling with headcount indefinitely. |
| Operational Burden | Yours to run, or run for you by forward-deployed engineers. | None. The provider operates, patches, and scales the platform. |
| Speed of Feature Delivery | Releases arrive on your schedule rather than continuously — deliberate, but slower. | Continuous delivery to every tenant at once is multi-tenancy's real advantage. |
In a dedicated deployment the index contains only your material, so a retrieval bug cannot surface another organization's content — the failure mode simply does not exist.
Shared vector stores depend on per-query filtering being correct on every code path. When it is not, the leak arrives as a fluent, confident answer rather than an obvious error.
For regulated content the question is not whether the provider's filtering is good, but whether you can accept that a software defect is the only thing standing between tenants.
Single-tenancy costs money and operational attention. Running a dedicated instance for a small team with ordinary data is expensive discipline with little payoff.
Mature multi-tenant providers invest heavily in isolation testing, and continuous delivery means every customer gets improvements immediately.
Choose by data class and scale, not by instinct. Multi-tenant is right for most organizations most of the time.
ibl.ai runs as your own deployment on your own infrastructure, so isolation is not a tier you purchase — it is a property of where the software runs.
A vendor's dedicated tier still runs in the vendor's environment, on their schedule, under their operational control.
If the reason for wanting single-tenancy is control, a self-hosted deployment delivers it completely rather than partially.
Infrastructure-level separation is far simpler to evidence to an auditor than a vendor's attestation about logical isolation.
Shared infrastructure is cheaper, requires no operations, and delivers features continuously — the right trade at that scale.
A shared pipeline cannot diverge per tenant, so meaningful customization requires your own instance.
Controlling upgrade timing keeps a model change from silently altering output in a workflow you already validated.
Timeline: Three to eight weeks depending on data volume and connector count
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 is single-tenant by construction: the platform runs as your deployment, on your infrastructure, with indexes, embeddings, caches, and logs that contain only your organization's material. That removes the cross-tenant failure mode entirely rather than mitigating it, and it puts upgrade timing in your hands so a model change never alters a validated workflow without your say-so. Agentic OS still gives you model-agnostic routing, permissions-aware retrieval, guardrails, and audit logging — you own all the code and the data, deploy on any cloud, on-premise, or air-gapped, and pay a flat license rather than per seat.
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.