Inference.net
Inference infrastructure for deploying, monitoring, evaluating, and fine-tuning open-source and custom language models.
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Product Overview
What is Inference.net?
Inference.net helps engineering teams run and improve language models in production. It combines model hosting, cross-provider observability, evaluation, and automated fine-tuning in one platform. Teams can use production traces to identify failures, build training datasets, and validate model improvements against quality, cost, and latency targets.
Key Features
Production Model Hosting
Deploy catalog models and custom fine-tuned models across public cloud, private cloud, or hybrid environments.
Cross-Provider Observability
Capture prompts, tool calls, responses, and request traces while tracking latency, errors, usage, and costs across model providers.
Continuous Model Evaluation
Evaluate models against production-derived datasets using automated scoring, task-specific checks, and human review.
Automated Fine-Tuning
Turn production traces and evaluation failures into training datasets for models tailored to specific workloads.
Model Improvement Workflows
Validate new model variants against baselines and retrain on fresh production data as application requirements evolve.
Use Cases
- Production Application Deployment : Host open-source or custom language models for applications without managing the underlying inference infrastructure.
- LLM Debugging : Inspect request traces and provider behavior to investigate failed tool calls, slow responses, and recurring errors.
- Model Migration : Evaluate open-source alternatives against existing providers before switching production workloads.
- Task-Specific Model Training : Fine-tune models for agent workflows, data extraction, or classification using examples from real application traffic.
- Release Validation : Compare model variants and detect quality regressions before deploying changes to users.
FAQs
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