Inference.net

What Inference.net does

Inference.net provides AI inference infrastructure for AI-native teams, enabling blazing-fast inference for open-source, custom, and fine-tuned AI models at massive scale. The platform helps teams ship AI that is faster, smarter, and dramatically more cost-efficient by delivering lower latency and higher-quality models at a fraction of the cost, with full OpenAI compatibility and no vendor lock-in. It offers tools to monitor existing providers, evaluate new models, and deploy at scale. Inference.net handles the infrastructure, allowing companies to power real-time AI features, automate workflows, and scale mission-critical systems without excessive costs. The company also focuses on creating a marketplace for otherwise wasted compute capacity, securing steep discounts from data centers and passing these savings to users, aiming to accelerate the widespread proliferation of AI.

Products

• Inference.net DeployHigh-performance model hosting for production workloads. Serves models reliably at massive scale across public cloud, private cloud, or hybrid environments with 99.99% uptime. Allows deployment of models from a catalog or custom-trained models, with transparent pricing and control over model swaps.
• Inference.net ObserveProduction-grade monitoring and observability for any model on any provider. Captures requests, generates insights, and provides end-to-end traces for LLM calls, tool calls, and framework steps. Offers latency breakdowns, cost attribution, drift and anomaly alerts, and full-text search for debugging.
• Inference.net TrainPlatform for fine-tuning custom frontier-level language models in minutes. Enables automatic fine-tuning workflows, curation of training data from observed traces, and validation of new model variants against baseline behavior. Supports specialized language models built for production workloads to achieve better performance with less compute.
• Inference.net EvaluateContinuously evaluates models against production traces. Provides a repeatable way to measure model quality before and after changes, compare model options, and validate rubrics and datasets for fine-tuning. Utilizes an LLM-as-a-judge mechanism for scoring model outputs.
• CatalystA platform that allows users to monitor, train, and deploy self-improving AI models. It integrates with the Catalyst Gateway to gather metrics on live production data and supports the end-to-end workflow from data capture to training, evaluation, and deployment of task-specific models.
• SchematronA family of small, purpose-built models for schema-guided extraction. Transforms messy HTML into clean, structured JSON, delivering frontier-level extraction quality at a fraction of the cost and latency of large general-purpose LLMs. Used for tasks like HTML-to-JSON extraction, invoice data extraction, and web scraping.
• Halo (Hierarchical Agent Loop Optimization)An open-source agent-loop optimizer that reads traces from Inference.net Observe and provides ranked findings with concrete fixes for AI agents. It helps identify systemic failure modes across runs and offers scheduled analysis reports.

Key capabilities

• Blazing fast inference for open-source, custom, and fine-tuned AI models
• Massive scale deployment
• Automatic capture and optimization of LLM performance in production
• OpenAI compatibility
• No vendor lock-in
• Production-grade monitoring for any model on every provider
• 99.99% uptime for deployed models
• Fine-tune custom frontier-level language models
• Continuous evaluation of models against production traces
• High intelligence, low cost
• Private data flywheel for LLMs
• Deploy LLMs anywhere (public cloud, private cloud, hybrid)
• LLM Observability for any model on any provider
• Automatic fine-tuning workflows
• Curate training data on autopilot from observed traces
• Validate model variants before promotion
• Pay-as-you-go and subscription pricing plans
• Dedicated infrastructure and deployment limits for enterprise
• Model swaps under user control
• Ownership of model weights
• Transparent pricing
• Autoscaling for real-world traffic spikes
• Direct push of fine-tuned models from Train to Deploy
• End-to-end traces for AI agents (LLM calls, tool calls, framework steps)
• Works with popular agent frameworks (OpenAI, Anthropic, LangChain, etc.)
• AI-powered CLI for automatic instrumentation
• Halo agent-loop optimization for identifying and fixing systemic failures
• Full span annotation with inputs, outputs, message history, tool calls, model metadata, and errors
• SOC 2 Type II compliant
• Encryption at rest and in transit
• Secrets never logged
• Full data retention controls
• Knowledge distillation for smaller, more efficient student models

Use cases

• Powering real-time AI features
• Automating workflows
• Scaling mission-critical systems
• Optimizing AEO (Ad Engine Optimization) software for ranking accuracy
• Processing millions of daily food images with custom vision models for nutrition tracking apps
• Processing billions of monthly video frames for decentralized data networks
• Deploying, observing, evaluating, and training GPT-5 quality models
• Reducing p-90 round-trip latency for ad networks
• Building compounding LLM flywheels by observing production traces and training on new data
• Adding powerful observability to existing LLM pipelines
• Fine-tuning models for specific tasks to reduce latency, cost, and improve accuracy
• Monitoring and debugging LLM pipelines
• Comparing model options for specific tasks
• Building real-time food verdict systems for consumer apps
• Training specialized LLMs for AI-native ad networks to serve intent-aware sponsored suggestions
• Processing videos at lower cost for search engines
• Invoice data extraction
• Web scraping for structured data (e.g., e-commerce product data, real estate listings, competitor prices)
• Optimizing AI agents and agent-loop performance
• Building video clip search engines

AI approach

Inference.net provides infrastructure for AI inference, monitoring, deployment, evaluation, and training of AI models. They focus on optimizing performance, reducing latency, and cutting costs for open-source, custom, and fine-tuned models. They offer a platform to build and deploy specialized language models, including knowledge distillation techniques, and provide tools for LLM observability and evaluation. They also build custom models for clients.

Tech named: LLMs, VLMs, OpenAI, Anthropic, Gemini, GLM-5.2, Z.ai, Kimi K2.6, Moonshot AI, MiniMax-M2.5, GPT-OSS 120B, DeepSeek v3.2, Claude Opus 4.6, Catalyst platform, Schematron V2, NVIDIA NeMO framework, OpenTelemetry, Halo (Hierarchical Agent Loop Optimization), LangChain, LangGraph, Vercel AI SDK, OpenAI Agents, LiveKit Agents, Claude Agent SDK, Pydantic AI, Gemma family, FP8 quantization

Industries served

• Software Development
• Advertising
• Consumer Applications
• Nutrition and Health
• Data Networks
• E-commerce
• Real Estate

What it says sets it apart

• Optimized for open-source, custom, and fine-tuned AI models
• Lower latency and higher-quality models at a fraction of the cost
• Full OpenAI compatibility with no vendor lock-in
• Handles infrastructure, allowing teams to focus on AI features
• Functions as a spot market for perishable compute capacity, securing steep discounts
• Provides a complete AI lifecycle platform: Monitor, Deploy, Train, Evaluate
• Specialized language models offer 90% lower cost and 5x lower latency with frontier-level quality
• Automated fine-tuning workflows and data curation from production traces
• LLM Observability for any model on any provider with end-to-end traces and cost attribution
• Dedicated infrastructure for stable latency and high availability
• Ownership and portability of model weights
• SOC 2 Type II compliant with robust security features (encryption, secret stripping, data retention controls)
• Halo agent-loop optimizer for actionable insights on agent performance
• Schematron for schema-guided extraction, offering high quality at low cost for structured data extraction from HTML
• Forward-deployed engineering approach, embedding with customer teams for tailored solutions

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.