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NuGenIT

Solution

We are locked into one model provider

A price change, a deprecation or an outage upstream would be a serious problem for us.

Most teams begin with one hosted model provider because it was the fastest way to ship. That is a reasonable decision, and it quietly becomes a dependency: provider-specific calls spread through the codebase, and there is no tested path to anywhere else.

The exposure is commercial as much as technical. Model pricing changes, models get deprecated on the vendor’s schedule rather than yours, and rate limits arrive at the worst moment. Portability is not about leaving — it is about being able to, which is also what makes a commercial conversation possible.

What is usually going on

Provider-specific calls throughout the application

Without an abstraction layer, changing provider means touching every call site, so nobody ever does.

No evaluation set to compare against

Switching model safely requires knowing whether quality held. Without a repeatable evaluation, any change is a leap of faith.

Prompts tuned to one model’s behaviour

Prompts drift towards the quirks of whatever they were developed against. Portability needs this acknowledged and tested, not assumed.

No fallback when the provider degrades

An upstream incident becomes your incident. A tested secondary path turns an outage into degraded service.

What we look at, in order

  1. 01 Assess how deeply provider-specific behaviour is embedded in the application
  2. 02 Introduce a gateway so routing, keys, limits and logging live in one place
  3. 03 Build an evaluation set that makes a model change measurable rather than a gamble
  4. 04 Design and actually test a fallback path
  5. 05 Decide what to keep hosted and what, if anything, is worth self-hosting

What you receive

  • Dependency assessment with the concrete exposure named
  • Gateway and routing architecture
  • Fallback design with a tested failure path
  • Hosted versus self-hosted analysis for your traffic profile

Delivered as a written assessment. More on how we work.

Products relevant to this

Not a shortlist for you specifically — that depends on constraints this page cannot know. These are the options an engineering team addressing this problem will encounter first.

LiteLLM

L4 · Inference gateway

Desk Research

A gateway presenting one consistent interface across many hosted model providers and your own self-hosted models.

Fit
Applications calling more than one model provider, or planning to.
Catch
Adds a network hop, and sits on the critical path of every request.
Read the notes

vLLM

L4 · Inference serving engine

Desk Research

An open-source engine for serving large language models, with memory handling built for high concurrent throughput.

Fit
Teams self-hosting an open-weight model for production inference.
Catch
Serves models well; does not manage your fleet, routing or tenancy.
Read the notes

AWS, Google Cloud and Azure

L3 · Hyperscale cloud

Desk Research

The three large general-purpose clouds, each offering accelerated compute alongside everything else you already run.

Fit
Estates where the data, identity and compliance posture already live.
Catch
Generally the highest hourly rate, and quota is often the real limit.
Read the notes

Other problems

Our inference costs rise faster than our usage We are weighing model APIs against running our own It works in the pilot but not in production We cannot see where our AI spend actually goes We are paying for accelerators that sit idle Our data pipeline is the bottleneck, not the compute We need to run AI on infrastructure we control We are about to commit, and it is hard to undo

Tell us what you are trying to solve.

Describe your current setup and the problem in your own words. We reply within 2 working days with an honest assessment of whether we can help.

Request an assessment hello@nugenit.in