85%

of ML models never make it to production. The ones that do run on duct tape.

One control plane for every model in production

LLMs, recommendation engines, fraud detectors, diagnostic AI — enterprises run hundreds of models across dozens of frameworks. InferOps unifies inference operations so you stop building a new pipeline for each one.

Start Monitoring Free
inferops agent
$ inferops deploy fraud-detector-v3 --auto
Analyzing model artifacts...
Agent: Detected XGBoost ensemble + GPT-4 explainer. Configuring unified endpoint.
Agent: Setting monitoring thresholds from 90 days of transaction data.
Agent: Canary deployment started. Routing 5% traffic.
Alert: Distribution shift on 'transaction_velocity'. Auto-investigating.
Agent: Root cause: holiday spending pattern. Adjusting thresholds, no retrain needed.
Status: 3 model types. 1 endpoint. 99.97% uptime. 0 manual interventions.
The Problem

The model zoo is eating your engineering team alive

Netflix runs 100+ recommendation models. JPMorgan deploys thousands for risk. Healthcare companies ship diagnostic AI under FDA scrutiny. Every model needs its own pipeline, monitoring, and compliance — and your team can't keep up.

Today's Reality

  • Separate pipelines for LLMs, XGBoost, and PyTorch
  • Manual monitoring thresholds per model
  • 3am pages when a fraud model drifts
  • Weeks to deploy what took days to train
  • Compliance docs rewritten for every audit

InferOps

  • Deploy any model in minutes
  • Thresholds set from training data
  • Agent investigates and remediates drift
  • Framework-agnostic auto-configuration
  • Governance reports generated automatically
Capabilities

Six agents. Every model type. Zero manual work.

Whether you're serving LLMs, classical ML, or multi-model pipelines — InferOps agents handle the entire lifecycle. They coordinate. They escalate when needed. They never sleep.

D

Deploy Agent

Analyzes model artifacts, detects framework, configures serving infrastructure, and runs canary deployments automatically.

M

Monitor Agent

Watches prediction distributions, latency, and throughput. Sets intelligent thresholds from training data, not guesswork.

R

Remediation Agent

When drift or degradation is detected, traces root cause across the feature pipeline and triggers corrective action.

G

Governance Agent

Generates audit trails, compliance documentation, and bias reports. Keeps models production-legal without human paperwork.

T

Testing Agent

Runs A/B tests, shadow deployments, and champion-challenger evaluations. Promotes winners, rolls back losers.

O

Orchestration Agent

Coordinates all other agents. Handles multi-model dependencies, resource allocation, and cross-team notifications.

Netflix-scale recommendations.
JPMorgan-grade risk models.
One platform to run them all.

From GPT-4 to gradient boosting, fraud detection to diagnostic AI — unified inference operations for the model zoo era.

Open Dashboard