Inference.net
Full-stack LLM lifecycle platform with OpenAI-compatible serverless inference, fine-tuning, observability, and custom models at up to 90% lower cost than frontier models.
Overview
Inference.net is a "Full-Stack LLM Lifecycle Platform" — a toolkit for building, deploying, monitoring, and continuously improving AI agents in production. It started as an OpenAI-compatible serverless inference API for open-source models and has expanded to cover the full agent lifecycle: deploy on managed global infrastructure, observe and trace production LLM calls, evaluate against real traces, and fine-tune custom models that the company says can match GPT-5-level quality while running 2-3x faster and costing up to 90% less. It targets teams running high-volume, repetitive AI workloads who want to cut frontier-model costs without sacrificing quality.
The Verdict
Who Should Use Inference.net?
Best For
- Teams spending heavily on closed-source LLM APIs ($50K+/mo) looking to cut costs
- High-volume, repetitive tasks where small custom models can replace frontier models
- Developers wanting OpenAI-compatible serverless inference with a two-line migration
- Engineering teams that want inference, observability, evals, and fine-tuning in one platform
Not Ideal For
- Teams that need the broadest catalog of closed frontier models (GPT, Claude, Gemini) directly
- Projects unwilling to invest in fine-tuning to realize the cost savings
- Buyers wanting a long, proven track record — the company is young (founded 2025)
What's Great
- OpenAI-compatible API with a roughly two-minute, two-line-of-code migration
- Aggressive pricing — open-source workhorse models from $0.03/M input tokens
- Full lifecycle in one place: serverless + dedicated inference, tracing, evals, fine-tuning
- First-class SDKs for TypeScript and Python with Pydantic/Zod structured-output support
- SOC 2 Type II compliance with 99.99% uptime on managed infrastructure
Watch Out For
- Performance and cost claims (GPT-5-level quality, 90% savings) are vendor-reported
- Realizing the biggest savings requires fine-tuning, not just swapping the endpoint
- Young company (2025 founding, seed stage) with a smaller ecosystem than incumbents
- Model catalog is curated open-source/proprietary, not a broad multi-vendor router
Pricing
View all features & details
Key Features
- OpenAI-compatible serverless inference for open-source LLMs
- Batch API for processing data at scale and real-time streaming responses
- Managed global infrastructure with dedicated deployment options (99.99% uptime)
- Observability: capture LLM calls, tool calls, and framework steps via OTEL spans
- Continuous evals against production traces
- Automated fine-tuning and data curation for custom models
- SOC 2 Type II compliance
Models & Platforms
- Proprietary models: Schematron V2 (structured output), ClipTagger 12B (vision)
- Open-source models including Kimi, MiniMax, GLM, GPT-OSS, and Nemotron
- REST API + OpenAI-compatible endpoints
- TypeScript and Python SDKs with Pydantic/Zod support
- Framework-agnostic — works with any agent harness
How It Compares
| Feature | Inference.net | Together AI | DeepInfra |
|---|---|---|---|
| API compatibility | OpenAI-compatible | OpenAI-compatible | OpenAI-compatible |
| Lifecycle scope | Inference + observability + evals + fine-tuning | Inference + fine-tuning | Inference + dedicated GPU |
| Custom models | Purpose-built small models (90% cheaper claim) | Fine-tuning | Fine-tuning |
| Free tier | $1/mo credit + free plan | Free credits | Pay-as-you-go |
| Best For | Cutting frontier-model spend on high-volume tasks | Balanced features | Cost-efficient open-source inference |