# Self-Hosted AI vs Cloud AI Hosting

> Source: https://ibl.ai/resources/comparisons/self-hosted-ai-vs-cloud-ai-hosting
> Last updated: 2026-10-20


*The five ways to host an AI application in 2026 — managed model APIs, inference API providers, runtimes in your own cloud account, on-premise and air-gapped — and what you operate versus what the provider operates in each*

**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.**

## What's the difference between Self-Hosted AI (ibl.ai) and Managed Cloud AI Hosting?

Self-hosted AI means the model runtime and the application around it run on infrastructure you control. Managed cloud AI hosting means a provider operates one or both. On ibl.ai you own all the code and the data, and the platform runs on any of the hosting options below.

The options, from most to least managed: managed model APIs (Amazon Bedrock, Azure OpenAI in Microsoft Foundry, Google Vertex AI, the OpenAI API); inference API providers for open-weight models (Together AI, Fireworks, Groq, DeepInfra, Baseten).

Then: open-source runtimes in your own cloud account or VPC (vLLM and SGLang, Apache-2.0; Ollama and llama.cpp, MIT); on-premise GPU servers; and fully air-gapped networks. Hugging Face TGI was archived in March 2026 and now points users to vLLM, SGLang and llama.cpp.

The trade is operations for control. Managed hosting removes GPU work and bills per token; self-hosting takes the provider out of the data path and is the only air-gapped route. ibl.ai is model-agnostic, has no per-seat pricing, and is family-owned and operated from New York, NY.

## How do self-hosted AI and cloud AI hosting compare, layer by layer?

### What You Operate vs What the Provider Operates

| Criteria | Self-Hosted AI (ibl.ai) | Managed Cloud AI Hosting |
|----------|--------------------|--------------------|
| GPUs and model serving | You run vLLM, SGLang, NVIDIA NIM or Ollama on your GPUs — or the platform calls a managed API for that workload. | The provider runs capacity, scaling, model upgrades and failover. |
| Agents, memory, RAG and logs | On ibl.ai, in the database you operate: conversations, memories, datasets and eval traces. | Your app's own store plus provider-side stateful features — OpenAI's Responses API keeps state 30 days by default. |
| Where prompts and outputs travel | Inside your network for self-hosted models; to a provider you choose, per workload, when not. | To the provider's region; Azure Global and DataZone deployments may process in other geographies in the zone. |
| Air-gapped operation | Supported: the full platform with local models and zero external API calls. | Not possible — a managed endpoint needs network reachability. |

### Cost Shape

| Criteria | Self-Hosted AI (ibl.ai) | Managed Cloud AI Hosting |
|----------|--------------------|--------------------|
| Fixed cost to start | GPU servers or reserved cloud instances, paid whether or not they are busy. | Near zero: on-demand per-token pricing with no commitment. |
| Cost at sustained high volume | Flat GPU cost amortised over every token once utilisation is high. | Scales linearly with tokens; Bedrock batch is 50% lower and provisioned throughput is hourly, with 1- or 6-month terms. |
| Seat licensing | ibl.ai has no per-seat pricing; usage against caps you set. | Model APIs are per token, not per seat — the per-seat bill comes from SaaS apps layered on top. |

### Capability

| Criteria | Self-Hosted AI (ibl.ai) | Managed Cloud AI Hosting |
|----------|--------------------|--------------------|
| Frontier proprietary models | Reached through the same managed APIs, routed from your platform; closed weights cannot be self-hosted. | The only place the newest closed models run, often on launch day. |
| Elasticity and time to first deployment | Capacity is the GPUs you provisioned; infra-cli automates the platform install. | An API key and minutes; bursts absorbed by the provider. |
| Model and provider portability | Switch any agent between self-hosted and managed models without rewriting the application. | Bound to one provider's catalog, SDK and stateful features unless you add an abstraction. |

## Which AI hosting option fits which workload?

### What are the main AI hosting options in 2026?

**Self-Hosted AI (ibl.ai):** Self-hosted options: open-source runtimes such as vLLM, SGLang, Ollama and llama.cpp in your cloud account or VPC, on-premise GPU servers, and air-gapped networks with no outbound path.

**Managed Cloud AI Hosting:** Managed options: hyperscaler model APIs (Amazon Bedrock, Azure OpenAI, Google Vertex AI), the model labs' own APIs, and inference API providers serving open-weight models.

**Verdict:** Most enterprises end up using several. The useful decision is per workload, not one hosting model for the whole organization.

### Is self-hosting or cloud AI hosting cheaper?

**Self-Hosted AI (ibl.ai):** Self-hosting has a fixed GPU cost that wins once utilisation is high and steady, because each extra token is close to free.

**Managed Cloud AI Hosting:** Managed hosting has no fixed cost and wins for spiky or low volume; Bedrock adds a 50% batch discount and hourly provisioned throughput for steadier loads.

**Verdict:** Compare the monthly token bill against the cost of the GPUs that would serve it at your real utilisation. Below that break-even, rent; above it, own.

### Can you mix self-hosted and cloud AI hosting?

**Self-Hosted AI (ibl.ai):** On ibl.ai the platform — agents, memory, data, audit — stays on your infrastructure while each agent's model can be a local runtime or a managed API.

**Managed Cloud AI Hosting:** Hyperscalers support hybrid patterns inside their own cloud, such as VPC endpoints, but the model endpoint remains theirs.

**Verdict:** Hybrid is the common answer: own the application layer and the data, and pick the model host per workload and per sensitivity.

## Should my organization self-host AI or use cloud AI hosting?

### Prototypes, pilots and spiky workloads — Managed Cloud AI Hosting

No fixed cost and instant capacity make managed model APIs the right first step.

### Steady, high-volume workloads on open-weight models — Self-Hosted AI (ibl.ai)

Once GPUs stay busy, a flat hardware cost undercuts per-token pricing, and the data never leaves your network.

### Defense, government and air-gapped environments — Self-Hosted AI (ibl.ai)

No managed endpoint can serve a network with no outbound path; only self-hosted models can.

### Most enterprises running many AI workloads — Either

Own the platform and the data, then host each model wherever its cost, capability and sensitivity point.

## How do I move from cloud AI hosting to self-hosted AI on ibl.ai?

### Managed cloud AI → self-hosted on ibl.ai (medium difficulty)

Timeline: Two to six weeks for the platform; model moves happen workload by workload

- Inventory: list each workload's model, monthly tokens and data sensitivity; move open-weight-eligible, high-volume or sensitive ones first.
- Platform: provision ibl.ai with infra-cli in your own AWS or GCP account, or bootstrap existing servers on any cloud or bare metal.
- Models: keep managed providers as organization LLM keys for frontier workloads, and serve open-weight models locally via NVIDIA NIM, Ollama or vLLM.
- Agents and RAG: recreate prompts as agents and re-ingest documents as datasets, so retrieval indexes live on your infrastructure.
- Eval: run the same benchmark against the managed and self-hosted model before switching, with LLM-as-Judge scoring.
- Air-gapped: for networks with no outbound path, deploy with local model weights only and no external providers configured.

Docs and skills for this move:

- [Deploy with infra-cli (AWS)](/developer/infra-cli)
- [Deploy on Google Cloud](/developer/infra-cli/gcp)
- [Infrastructure architecture](/developer/infra-cli/architecture)
- [Organization LLM keys](/docs/os/organization-settings/integrations-llms)
- [Agent LLM configuration](/docs/os/agent-settings/llm-configuration)
- [Skill: iblai-api-infrastructure](/developer/skills/iblai-api-infrastructure)
- [Evaluations](/developer/platform/agents/evaluations)
- [Air-gapped AI with local models](/service/air-gapped-ai)

### Self-hosted → managed cloud AI (low difficulty)

Timeline: Hours per workload

- Add the provider's key in the organization's LLM settings and switch the agent's model — the application layer does not move.
- Check region, retention and abuse-monitoring terms for the chosen endpoint before routing sensitive data.
- Reasonable for frontier-model workloads or when GPU utilisation is too low to justify the hardware.

## FAQ

**Q: What is the difference between self-hosted AI and cloud AI hosting?**

Who operates the model runtime and where the data goes. Cloud AI hosting means a provider serves the model and you call its API; self-hosted AI means the runtime runs on infrastructure you control, so prompts never leave it.

**Q: What are the best self-hosted LLM runtimes?**

vLLM and SGLang (Apache-2.0) for production throughput on GPUs, Ollama and llama.cpp (MIT) for local and edge. Hugging Face TGI was archived in March 2026 and recommends vLLM, SGLang or llama.cpp instead.

**Q: Do managed AI hosts train on my prompts?**

The major enterprise APIs say no. Azure states prompts are not used to train foundation models, and Bedrock runs models in AWS-owned accounts model providers cannot access. Retention and abuse-monitoring terms still apply.

**Q: Can ibl.ai use Amazon Bedrock or Azure OpenAI?**

Yes. ibl.ai is model-agnostic: managed providers are configured as organization LLM keys alongside self-hosted models, and each agent can switch providers without rewriting anything.

**Q: Can AI run fully air-gapped?**

Only self-hosted. ibl.ai runs its full platform on air-gapped Ubuntu servers with NVIDIA GPUs, serving local models via NIM, Ollama or vLLM with zero external API calls.

**Q: How does ibl.ai fit into AI hosting?**

ibl.ai is the application layer you self-host. You own all the code and the data, there is no per-seat pricing, and you deploy anywhere while choosing each model's host per workload.


## Where do these facts come from?

1. Amazon Bedrock runs each model provider's software in AWS-owned model deployment accounts that providers cannot access, so they never see customer prompts or completions. [AWS docs: Amazon Bedrock data protection](https://docs.aws.amazon.com/bedrock/latest/userguide/data-protection.html)
2. Bedrock prices on-demand inference per token, batch inference 50% lower, and provisioned throughput per hour with no-commitment, 1-month or 6-month terms. [Amazon Bedrock pricing](https://aws.amazon.com/bedrock/pricing/)
3. Azure-sold models do not use prompts or completions to train foundation models; Global and DataZone deployments may process data in other geographies within the zone. [Microsoft Learn: Data, privacy and security for Foundry Models sold by Azure](https://learn.microsoft.com/en-us/azure/foundry/responsible-ai/openai/data-privacy)
4. OpenAI's Responses API retains application state for 30 days by default. [OpenAI API docs: Data controls](https://developers.openai.com/api/docs/guides/your-data)
5. vLLM is an Apache-2.0 high-throughput LLM serving engine. [vllm-project/vllm on GitHub](https://github.com/vllm-project/vllm)
6. SGLang is an Apache-2.0 serving framework for large language and multimodal models. [sgl-project/sglang on GitHub](https://github.com/sgl-project/sglang)
7. Ollama is MIT-licensed for running open models locally. [ollama/ollama on GitHub](https://github.com/ollama/ollama)
8. llama.cpp is MIT-licensed LLM inference in C/C++. [ggml-org/llama.cpp on GitHub](https://github.com/ggml-org/llama.cpp)
9. Hugging Face TGI was archived on March 21, 2026, is in maintenance mode, and recommends vLLM, SGLang and llama.cpp or MLX. [huggingface/text-generation-inference on GitHub](https://github.com/huggingface/text-generation-inference)
10. Inference API providers for open-weight models include Together AI, Fireworks, Groq, DeepInfra and Baseten. [AI SDK docs: Providers](https://ai-sdk.dev/providers/ai-sdk-providers)


## Where does ibl.ai fit alongside Managed Cloud AI Hosting?

**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 the part of an AI stack that should never be rented: agents, memory, datasets, evaluation and audit, on infrastructure you control, pointed at whichever model host fits each workload.

Agentic OS runs in your cloud account, on-premise or air-gapped, and our Air-Gapped AI and Amazon Bedrock services cover both ends of the hosting spectrum. You own all the code and the data, whichever GPUs serve the tokens.

- **You own all the code and the data.** Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours.
- **Model-agnostic.** Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform.
- **No per-seat pricing.** Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.
- **Deploy anywhere.** 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.