5 articles tagués avec « enterprise »

Most enterprise identity architectures did not start as what they are today. They started in 1999 with Active Directory, accumulated LDAP integrations over the following decade, and are now partway through a migration toward cloud-based identity via Microsoft Entra ID — carrying the weight of every layer that came before.

We built our platform from scratch. We never had an on-premises domain. We never configured LDAP. We skipped directly to the protocol stack that enterprises are spending years and significant money trying to reach. This post explains what we chose, why, and how the resulting identity architecture compares to the enterprise standard.

Most enterprise AI deployments make the same architectural mistake early on: they give every team a direct API key to OpenAI or Anthropic and call it done. The result is predictable — no cost visibility, no access control, no audit trail, and sensitive data being sent to cloud APIs without any guardrails.

A proper enterprise AI gateway changes the shape of the problem. Instead of many teams talking to many APIs, you have one endpoint that handles routing, rate limiting, PII scrubbing, caching, and observability. Teams consume it the same way regardless of whether the model is running on your own hardware or on a cloud provider's GPU fleet.

This post covers the full design of such a gateway, built on Kubernetes with LiteLLM as the proxy layer, Ollama and vLLM for local inference, and Presidio for PII protection — with real configuration that is running in production.

Most write-ups about vLLM versus Ollama assume you have a GPU. The benchmarks show impressive VRAM utilisation. The architecture diagrams include CUDA drivers. The recommendation — vLLM wins — comes with an implicit asterisk: assuming you have the hardware for it.

We did not. Our inference cluster is four ThinkPad laptops, each with an i7-8565U (4 cores, 8 threads, up to 4.6 GHz turbo), 16 GB of RAM, and no GPU of any kind. We ran Ollama first. Then we replaced it with vLLM. This is the honest account of that migration: what broke, what improved, and what the tradeoffs actually look like when you run LLM inference on commodity x86 CPUs.

Every article about enterprise AI ends the same way. Use Amazon Bedrock. Use Azure OpenAI Service. Use Google Vertex AI. These platforms offer enterprise-grade compliance, data privacy, and zero-training guarantees.

The advice is correct — for organisations that can use American cloud infrastructure.

A large and growing number of organisations cannot. Some because of cost. Many because of regulation. A few because their data literally cannot cross a border under the law that governs their sector.

This post is for those organisations. It covers two separate problems — data sovereignty and cost — and the concrete options available for each.

Ollama is how most teams first run a large language model locally. You install it in five minutes, run ollama pull mistral, and you have a working API. It feels like magic.

Then you try to serve ten users at once. Or a hundred. Or you need to audit every request for compliance. Or your legal team asks where the data goes. That is when you realise Ollama was built for something else entirely.