Designed and built in the EU

Observe & optimize agents
in the wild

See how deployed AI behaves in production. Detect failures, understand why they happen, improve what you ship, and keep the record that proves it.

Start monitoring See how it works

Zero-dependency SDK · Python, Android & iOS · Deploy in your VPC

Production model health Live
Wild Edge production model health dashboard
Built for production ✓Zero dependencies ✓Audit-ready records ✓EU AI Act evidence support ✓Your cloud or VPC

Every inference feeds the cycle

01 / Deploy

Instrument in minutes

Add the zero-dependency Wild Edge SDK. No changes to inference code.

$ uv add wildedge-sdk
~1ms overhead Zero dependencies Python Android iOS
02 / Monitor

Model performance in one place

Detect drift, latency degradation, and confidence shifts across all models before a support ticket lands.

Model health dashboard
Hardware breakdown by device
Hardware

Deep inference analytics

Latency, error rate, and drift broken down by device, OS, accelerator, and thermal state.

Agentic trace timeline
Traces

Agentic traces and reasoning chains

Every step of agentic pipelines: timing, tokens, cache hits, tool call sequences.

Confidence Drift TTFT Token Usage Latency breakdown
03 / Capture

Build datasets from production

Filter production events by confidence, device, or outcome. Attach inputs. Build a dataset from real failures and user feedback.

Filtering inference events to build a dataset
Filter

Confidence-based event filtering

Slice production events by model confidence, device type, or outcome to seed your dataset.

Dataset analysis with confidence distribution and user feedback signals
Analysis

Dataset quality and coverage

Confidence distribution and user corrections surface where the model is weakest.

Active learning Confidence-filtered Hard case mining User feedback
04 / Retrain

The data is yours

Query the full inference history in plain language or SQL. Filter by hardware, model version, confidence score, or any field captured at inference time.

SQL analytics explorer
Full event history NL + SQL Open table format Snowflake / BigQuery
05 / Report

Turn telemetry into compliance evidence

Generate an EU AI Act compliance report from the evidence already captured in production: model behaviour, safeguards, traceability, and corrective action.

Wild Edge compliance evidence Export report
EU AI Act
Compliance Report

A continuously updated record of model behaviour, privacy safeguards, traceability, and corrective action.

Overall evidence status
Good
WindowLast 30 days
GeneratedLive
Evidence12,486 records
01 · GOODAccuracy & drift
02 · GOODData protection
03 · REVIEWTraceability
04 · GOODRemediation
See if it applies to you

Works with your stack.

Python, Android, and iOS SDKs. No code changes for most setups.

Python
OpenAI Anthropic Gemini OpenRouter Transformers PyTorch TensorFlow ONNX MLX Keras
Android
TFLite ONNX Runtime ML Kit LiteRT LiteRT LLM Gemini Custom models
iOS & macOS
Core ML TFLite ONNX Runtime ML Kit Custom models
Compare

Benchmarking ends at launch.
Monitoring starts there.

Lab numbers tell you a model can work. Wild Edge tells you whether it is working, on which devices it is degrading, and exactly which inferences to retrain on.

See how we compare to benchmarking tools
Enterprise

Deploy where your data lives

Wild Edge runs entirely within your infrastructure. No inference data leaves the perimeter.

VPC Deployment

Runs inside your cloud account or private network. All ingestion, storage, and query traffic stays within the perimeter. Nothing crosses to a third-party cloud.

AWS GCP Azure Private infra

On-Premise / Air-Gapped

Full datacenter deployment with no external network dependencies. Meets strict data residency, sovereignty, and classification requirements.

Air-gapped Data residency No egress

Bring your own storage

Point Wild Edge at an existing object store. Data lands in the bucket, under your keys. Stop using Wild Edge and the data is still there, still queryable.

S3 GCS Azure Blob MinIO

Privacy by design

The SDK emits structured telemetry by default. Raw inputs and outputs are never captured unless the team explicitly enables it for a specific case.

  • No raw data by default
  • Opt-in capture with defined scope
  • Open table format, no vendor lock-in
  • SSO and audit logs
  • Zero-dependency SDK, no supply chain exposure

How this maps to EU AI Act obligations

Articles 12, 19, 26(6), 72, and Annex IV point 9, plus what Wild Edge does not cover.

Read the mapping

Deployment model trusted by

Healthcare Financial Services Defense
Talk to us

Simple pricing.

Start free. Upgrade when you need more.

Free
$0
No credit card
  • 3 models
  • 100k events / month
  • 14-day retention
Get Started
Pro
$299
/ month
Per project, billed monthly
  • Unlimited models
  • 1M events / month
  • 90-day retention
  • Email support
Get Started
Enterprise
Custom
For teams with security, volume, or deployment requirements
  • Everything in Pro
  • VPC & on-premise / air-gapped deployment
  • Bring your own object store
  • Custom retention, data residency & sovereignty
  • SSO & audit logs
  • Dedicated support & SLA
Talk to us Retention requirements under the EU AI Act

Your models are
running right now.

Find out what they're failing at before users notice.

Get Started Talk to us