Running self-hosted LLMs efficiently.
Feniria automatically tunes your models for your GPUs, traffic and latency targets.
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The problem
The LLM API bill keeps growing.
Everyone starts with LLMs through an API. Then agents, coding assistants and internal AI platforms go live, and the bill explodes.
3.2× in one year
Source: Menlo Ventures, State of GenAI in the Enterprise 2025
So teams move to their own models
- To cut costs No per-token bill that grows with every new user and agent.
- To keep their data Prompts and outputs stay on infrastructure they control.
How it works
Feniria does that tuning for you.
We deploy your model alongside a system that monitors the model and the GPU, tests new settings against your real traffic, and keeps the ones that perform better.
- Observability out of the box From service KPIs (latency, time to first token, throughput, queueing, cost per token) down to GPU memory, KV cache and per-process usage.
- Tuned to your traffic Batching, cache, memory, concurrency and parallelism, set for your workload and your latency targets, and re-tuned as load changes.
- Safe for production Every change is validated before it takes traffic. For critical systems, keep a human in the loop to approve it.
- Your cloud or on-prem One container stack next to the model. Data never leaves your perimeter.
Results
Same GPUs. 12.7× more output.
Tokens per second, replies still fast
12.7× Peak: 1,223 (17×)
2×
fine-tuned models per GPU
16 adapters fit in memory where 8 did, on a 16 GB GPU.
3.3×
faster structured output
193 → 631 tokens/s: enough to replace a paid API.
−50%
instance cost, in production
AWS, $21,996 → $10,998 a month. Read the case study
Pilot benchmarks on traffic shaped like clients' workloads · Fast = first token in < 2 s (p95)
Who it's for
Is it right for you?
Built for companies that use LLMs seriously and don't have an inference engineering team.
- 01 Your LLM API bill grows with usage We estimate whether deploying and optimizing your own model pays off, then validate it with your real traffic.
- 02 Your data has to stay with you Privacy, GDPR or plain control: you want the model on infrastructure you own, not behind a public API.
- 03 You run batch jobs Reprocessing calls, sentiment analysis, tender documents. Spin GPUs up for the work window instead of paying for them 24/7.
- 04 You already self-host Inference providers and in-house teams: more throughput per GPU, more fine-tuned models per GPU, or fewer GPUs.
When we say no
With low or spiky volume, an API is usually still cheaper and simpler. We'll tell you, before you spend anything.
What teams usually want to know
Frequently asked questions
What savings can I expect?
It depends on your model, request volume, traffic pattern and current cost. With those four we give you an initial estimate. Real throughput and latency are only validated by observing your real traffic, and they do not carry over from one client to another, so we confirm the number in a pilot before you commit.
Should everyone self-host?
No. With low or spiky volume, an API is usually still cheaper and simpler, and we will tell you so. Self-hosting pays off when the API bill grows with usage, when data has to stay on infrastructure you control, or when batch jobs can run in a GPU window instead of 24/7.
What exactly do you deploy?
One containerized stack, next to the model, inside your cloud project or on-prem. Your apps keep calling a private LLM endpoint; behind it, an open or fine-tuned model runs on your GPUs. The Feniria layer adds observability (latency, time to first token, throughput, queueing, errors, cost per token), GPU and process profiling (memory, KV cache, per-process usage) and the optimizer. Data never leaves your perimeter.
Does it change my production setup on its own?
Feniria runs a continuous loop: observe, test, validate, apply. Every change is validated before it takes traffic. For critical systems you can keep a human in the loop who approves each change before it is applied.
How does an engagement work?
We start with an estimate, validate it in a pilot on your infrastructure with your real traffic, and then keep the deployment tuned through a subscription. You pay for the optimization, not for the GPUs: compute stays on your own bill, so we have no incentive for you to consume more of it.
Who do you work with?
Inference providers that already offer private AI, where Feniria gets more out of every GPU, and companies moving from LLM APIs to their own models without an inference engineering team. Both, today.
Work with us
Find out if self-hosting pays off for you.
Tell us your model, request volume, traffic pattern and current cost. We'll come back with an initial estimate, and an honest answer if an API is still the better choice.