# Inference.net Inference.net is infrastructure for running, monitoring, evaluating, training, and deploying production AI workloads. It helps AI-native teams observe model behavior, route traffic across providers, and improve cost, latency, reliability, and quality through connected workflows. - Findings24: - Site: - Preços: Freemium - Categorias: Plataformas de inferência de IA, Implantação de modelos de IA, Observabilidade de IA ## O que é Inference.net? Inference.net is an AI infrastructure platform for teams operating production model workloads. Its product surface combines model deployment, LLM observability, tracing, continuous evaluation, and specialized fine-tuning workflows. Teams can route traffic through an LLM gateway, inspect requests and agent runs, compare model behavior, and deploy models from the catalog or models trained for their workload. The platform is designed to connect production data to model improvement. Observe captures model-call telemetry across providers, while Trace records agent execution details such as prompts, tool calls, framework steps, outputs, metadata, and errors. Evaluate turns production traces into evaluation datasets and scores behavior against selected standards. Training workflows can use observed traces and evaluation failures to create training-ready data, validate variants against baselines, and retrain as product requirements change. Inference.net also offers production model hosting with dedicated infrastructure, portability of model weights, controlled model changes, and a stated 99.99% uptime target. Pricing includes a free pay-as-you-go tier with monthly included credits, a Growth plan, and Enterprise arrangements with custom contracts and dedicated infrastructure. ## O que Inference.net pode fazer? ### Gateway-based model observability Route LLM traffic through a single integration point to capture requests, traces, latency, cost, and provider behavior without changing the underlying model stack. ### Agent tracing and analysis Capture LLM calls, tool calls, framework steps, and custom spans as OpenTelemetry traces. Trace views preserve inputs and outputs, message history, tool data, model metadata, agent structure, and errors. ### Production search and monitoring Search production events and investigate latency, reliability, usage, quality signals, drift, anomalies, and failure modes using trace, model, user, or custom attributes. ### Continuous evaluation Convert production traces into evaluation datasets, score models with automated checks, task-specific metrics, or human review, and compare new variants with baseline behavior before promotion. ### Fine-tuning and retraining workflows Move from observed traces and evaluation failures to training-ready samples, then fine-tune models for domain, task, quality, cost, or latency objectives and retrain on new production data. ### Production model deployment Deploy catalog models or custom models on infrastructure intended for sustained workloads, with support for public-cloud, private-cloud, or hybrid environments and control over model swaps. ## Casos de uso ### Debugging multi-step agents Engineering teams can inspect a complete run, including framework spans, tool arguments and results, model metadata, and exceptions, then use Halo analysis to identify recurring failure modes and link proposed fixes to trace IDs. ### Comparing providers and model routes Teams running models from OpenAI, Anthropic, open-source sources, or Inference.net can use the gateway to compare provider performance, attribute spend by route or workflow, and identify opportunities to change models without rebuilding their pipeline. ### Improving models from real traffic Product and ML teams can turn representative production traces and evaluation failures into datasets, test candidate variants against existing behavior, and establish an improvement loop based on actual user workloads. ### Operating custom models in production Teams with specialized or fine-tuned models can deploy them on dedicated infrastructure, retain control over when models change, and keep weights portable across supported deployment environments. ## Perguntas frequentes ### How is observability added to an existing model pipeline? For provider-agnostic request monitoring, teams can route LLM traffic through the Inference.net gateway. The gateway connects to downstream providers and captures requests in a unified dashboard; the site says setup can begin in about five minutes. Models deployed on Inference.net are observed automatically without an additional SDK. ### What does Trace capture? Trace captures LLM calls, tool calls, framework steps, and custom spans as OpenTelemetry traces. Recorded information can include inputs and outputs, ordered user/system/assistant/tool messages, tool-call arguments and results, model and token metadata, agent or graph structure, and errors or exceptions. ### Can teams use their own agent frameworks? The Trace page lists support for OpenAI, Anthropic, LangChain, LangGraph, the Vercel AI SDK, OpenAI Agents, LiveKit Agents, Claude Agent SDK, and Pydantic AI, and says additional frameworks are being added. The site also describes an AI-assisted CLI that can instrument a codebase and lets users review changes before they are applied. ### How do evaluation and training fit into the workflow? Production traces can be converted into evaluation datasets and scored with automated scoring, task-specific checks, or human review. Evaluation results and observed traces can then feed training-ready samples, model fine-tuning, baseline validation, and later retraining as the product evolves. ### What plans are available? The pricing page lists a free Pay as you go plan, a $250-per-month Growth plan, and Enterprise plans available through sales. Included limits and credits differ by plan, while Enterprise can include custom contracts, dedicated infrastructure, deployment limits, and direct support. ## Informações rápidas - **Category**: AI infrastructure and developer platform - **Primary users**: AI-native engineering, product, and ML teams - **Core workflow**: Observe production traffic, evaluate model behavior, train improvements, and deploy models - **Tracing format**: OpenTelemetry traces and spans - **Deployment environments**: Public cloud, private cloud, or hybrid environments - **Pricing model**: Free pay-as-you-go tier, Growth subscription, and custom Enterprise plans