Model Selection
- Benchmark-driven selection
- Open vs. closed-source tradeoffs
- License compliance review
Model Hub & Fine-Tuning
Hugging Face Model Hub & Fine-Tuning
Open-source and fine-tuned models where they outperform a general-purpose API on cost or accuracy.
Why Fine-Tuning Matters
A frontier API model is often overkill — and overpriced — for a narrow, repetitive task, and sending sensitive data to a third-party API isn't always an option. Fine-tuned open-source models, hosted where you need them, frequently beat general-purpose models on cost and accuracy for domain-specific work.
The Business Impact
- Lower Inference Cost
Fine-tuned small models cost far less than a frontier API at volume.
- Better Task Accuracy
Domain-specific fine-tuning often beats general models on narrow tasks.
- Full Data Control
Self-hosted models keep sensitive data inside your infrastructure.
Where We Work
From model selection to production serving.
Fine-Tuning
- LoRA / parameter-efficient tuning
- Dataset preparation & labeling
- Evaluation against baselines
Hosting & Serving
- Self-hosted inference (vLLM, TGI)
- Autoscaling GPU infrastructure
- Model versioning & rollback
How We Help
From model selection to self-hosted production serving.
Fine-Tuning Engagements
Beat the general-purpose APIWe prepare your data, fine-tune an open-source base model, and benchmark it against a general API before recommending a switch.
Improved
Task Accuracy
- LoRA / parameter-efficient fine-tuning
- Dataset preparation support
- Baseline benchmarking
Self-Hosted Model Serving
Keep sensitive data in-houseProduction-grade inference infrastructure for teams that need models running in their own environment.
Full control
Data Residency
- vLLM / TGI serving setup
- Autoscaling GPU infrastructure
- Model versioning & rollback
What This Looks Like in Practice
Results teams see after moving to fine-tuned, right-sized models.
Up to 60%
Inference cost
Typical reduction versus a general-purpose API for high-volume, narrow tasks.
Improved
Task accuracy
Fine-tuned models frequently outperform general models on domain-specific tasks.
Full control
Data residency
Self-hosted models keep sensitive data inside your infrastructure.
Why Teams Choose Us
We fine-tune when it earns its cost, not by default.
Benchmark Before Building
We compare fine-tuned candidates against your current API baseline before recommending a switch.
- Baseline vs. fine-tuned benchmarking
- Cost/accuracy tradeoff analysis
Data Stays Where It Needs To
Self-hosted serving keeps sensitive data inside your infrastructure when it must.
- Self-hosted inference delivered
- Full data residency control
Right-Sized Models
Small, fine-tuned models handle narrow tasks cheaper and often more accurately than frontier APIs.
- Up to 60% inference cost reduction
- Task-specific accuracy gains
Frequently Asked Questions
Common questions about our Model Hub & Fine-Tuning and implementation services.