Build custom AI you actually own: open-source models plus multi-cloud proxies, no lock-in, controlled cost.

Most "AI platforms" quietly make one decision for you: the exit is expensive. Your prompts, your fine-tunes, your embeddings, and your integration surface all live inside someone else's product. Switching later means rewriting, re-training, and renegotiating from a weak position. That is vendor lock-in, and it usually shows up as a surprise on your invoice or a feature you cannot get.

At DYDD we build custom AI a different way. We do not sell a platform, and we never compete with the clients we build for. You end up owning the solution, running it where you choose, and keeping the option to change providers without tearing everything down.

What vendor lock-in actually costs

Lock-in is rarely one big bill. It is a series of smaller constraints that compound:

• Pricing power moves to the vendor. When switching is hard, price increases are easy to pass on to you.
• Your roadmap depends on theirs. Need a capability they have not shipped? You wait.
• Data gravity grows. The longer your data lives in a proprietary format, the harder and riskier the exit becomes.
• Your provider may become your competitor. Closed AI products often expand into the very services their clients sell.

None of this means cloud AI is bad. Managed models from AWS, Azure, and others are genuinely excellent. The problem is architecture that assumes you will never want to leave.

Our approach: open source at the core, cloud where it pays off

DYDD combines two things deliberately.

First, open-source models and tooling at the core of your system: open-weight LLMs, embedding models, vector stores, and orchestration frameworks. These have no license that can be revoked, run on infrastructure you control, and keep your data in formats you own.

Second, a set of cloud AI service proxies in front of providers like AWS, Azure, and Hugging Face. Instead of calling a vendor SDK directly from your business logic, your application talks to a thin internal interface. That interface decides which provider handles a given request. The provider becomes an implementation detail, not a foundation.

The result is a system where the expensive, hard-to-move parts are yours, and the interchangeable parts stay interchangeable.

Why a proxy layer changes the economics

A provider-agnostic interface gives you three levers most teams never get:

• Cost control. Route cheap, high-volume tasks to an open-source model on your own GPUs, and reserve a premium hosted model for the requests that truly need it.
• Reliability. If one provider has an outage or a rate-limit wall, fail over to another without a deploy.
• Negotiation. When you can leave in an afternoon, pricing conversations go very differently.

A concrete example: swapping a model without re-architecting

Say your product summarizes long support tickets. On day one you use a hosted model on Azure OpenAI because it is fast to start and quality is high. Your code never imports the Azure SDK directly; it calls an internal SummarizationProvider with a stable contract, summarize(text, options).

Three months later, volume has grown and cost matters. You benchmark an open-weight model self-hosted behind a Hugging Face Text Generation Inference endpoint. Quality is close enough for most tickets, and the per-request cost drops sharply.

Because every call went through the proxy layer, the change is a configuration decision, not a rewrite:

• Add a second implementation of SummarizationProvider that points at the self-hosted endpoint.
• Route 90% of traffic to the open-source model and keep the hosted model for edge cases flagged as complex.
• Prompts, evaluation suites, and business logic stay untouched because the contract never changed.

No feature freeze, no migration project, no re-architecture. The same pattern lets you move embeddings between a hosted API and a local model, or shift a whole workload from one cloud to another as pricing and availability change.

You own it, and we do not compete with you

Two principles keep this honest.

You own the solution. The code, the model choices, the prompts, the data pipelines, the deployment scripts. If DYDD disappeared tomorrow, your team could keep running and evolving the system. That is the opposite of renting access to a black box.

We are a services company, not a product company. We do not sell a platform you plug into and cannot leave, and we have no product that competes with what you sell. Our incentive is a system that works so well you recommend us, not one that traps you into a subscription. Honest engineering, not a moat around your data.

The trade-offs, stated plainly

This approach is not free of effort. A proxy layer is a bit more upfront design than calling one vendor SDK, and self-hosting open-source models means owning some infrastructure and evaluation work. For small experiments, a single managed provider may be the right call, and we will tell you so.

The payoff shows up as you scale: predictable cost, the freedom to adopt the best model of the moment, and no quiet dependency deciding your future for you.

Talk to us

If you are building AI into your product and want to own the result instead of renting it, we would like to hear what you are working on. Start a conversation with our assistant in the chat on this site, or reach us on WhatsApp through the contact page. We will give you a straight technical answer, not a sales pitch.