Pricing
The homepage does not state prices. A dedicated pricing page exists on the site.
Learn moreInference
Inference infrastructure to run, trace, evaluate and tune production AI
Advanced API
What is Inference?
Inference is an analytics tool. Inference.net is an AI infrastructure platform covering managed deployment, monitoring, agent tracing, evaluation and custom model training, built for teams running production AI workloads who want lower cost and latency.
Inference.net is an infrastructure platform for teams that run AI workloads in production. It groups its offering into six product areas: Deploy, Observe, Trace, Train, Evaluate and HALO. Together they cover the path from picking a model to serving it, watching how it behaves, and tuning it for a specific job. The stated aim is lower cost, faster latency and dedicated support compared with running the same workloads elsewhere. Deploy is the hosting layer, described as fully managed, global and turn-key, with dedicated uptime. Observe monitors live traffic through continuous benchmarking, so quality, latency and cost can be compared side by side. Trace records each step an agent takes, including LLM calls, tool calls and framework steps, which gives engineers a record to debug against. Evaluate is meant for pre-launch checks, running benchmarks on a model before it reaches production. A recent blog post introduces AutoEvals, a feature aimed at finding the right model for a given task. Train addresses the case where off-the-shelf models do not fit. It builds task-specific models tuned to a customer's own data, with delivery pitched in days rather than months. HALO is an open-source agent optimization tool that analyzes traces, ranks failure modes and proposes concrete fixes. The site also lists a model catalog, case studies from named customers, documentation with an SDK install path, and a pricing page. The audience is engineering teams already shipping AI features who care about unit economics and reliability, rather than hobbyists looking for a chat interface. In practice, a team installs the SDK, captures traces from its application, evaluates candidate models against its own tasks, and then moves traffic to a managed deployment or a custom-trained model. Compared with general cloud inference providers, the distinguishing idea is the closed loop of observation, evaluation and training under one roof. Compared with standalone observability tools, it also offers hosting and model training. Buyers with simple, low-volume needs may find the platform broader than they require, and those wanting a self-serve consumer product should look elsewhere.
How do you use Inference?
1Install the SDK
Open the documentation and install the SDK in the application that makes LLM calls. The docs are the starting point for connecting the platform.
2Capture a first trace
Follow the quickstart to record a first trace of LLM calls, tool calls and framework steps from the app.
3Evaluate candidate models
Use the Evaluate tooling to benchmark models against the task before sending production traffic to them.
4Review pricing and deploy
Check the pricing page, then use Deploy for managed, global hosting of the chosen model.
5Train a task-specific model
If general models fall short, look at the Train overview to tune a model on your own data.
Pros and cons
Pros
- Covers deploy, observe, trace, evaluate and train in one platform, so teams avoid stitching separate vendors togetherAI
- Trace captures LLM calls, tool calls and framework steps, which helps debug multi-step agentsAI
- Train builds task-specific models on a customer's own data, aimed at cutting cost for narrow tasksAI
- HALO agent optimization is open source and ranks failure modes from tracesAI
- Docs, case studies, a model catalog and engineer consultations support evaluation before committingAI
Cons
- Aimed at engineering teams, so non-technical users will find little they can use directlyAI
- The homepage gives no pricing figures, so cost claims must be confirmed through the pricing page or salesAI
- Breadth of six products can be more than a small team with simple inference needs requiresAI
- Custom model training depends on having enough quality task data, which the homepage does not detailAI
- Integration and framework support are not spelled out on the homepage, so compatibility needs checkingAI
How much does Inference cost?
Support
Dedicated support is promised, with a Talk to an Engineer option, a documentation site, guides and a blog.
Learn moreIntegrations
The homepage mentions capturing LLM calls, tool calls and framework steps through an SDK, but does not name specific integrations.
Features
Deploy for managed global hosting, Observe for continuous benchmarking of quality, latency and cost, Trace for agent step capture, Train for task-specific custom models, Evaluate for pre-production benchmarks, and HALO for open-source agent optimization.
Frequently asked questions about Inference
How much does Inference cost?
The homepage does not state prices. A dedicated pricing page exists on the site.Does Inference have an API?
Yes, Inference offers an API.How do you use Inference?
The walkthrough on this page covers 5 steps: 1. Install the SDK 2. Capture a first trace 3. Evaluate candidate models 4. Review pricing and deploy 5. Train a task-specific model.What platforms does Inference support?
Inference is available on Web App.What does Inference integrate with?
The homepage mentions capturing LLM calls, tool calls and framework steps through an SDK, but does not name specific integrations.What are the limitations of Inference?
Aimed at engineering teams, so non-technical users will find little they can use directly. The homepage gives no pricing figures, so cost claims must be confirmed through the pricing page or sales. Breadth of six products can be more than a small team with simple inference needs requires.
