Deploy, Run, and Fine-Tune ML Models at Scale

One API to serve any model. From image generation to voice synthesis, Kainguru handles the infrastructure so you can focus on building.

Get Started View Docs

Terminal

# Deploy a model with one API call
$ curl -X POST https://api.kainguru.com/v1/models \
  -H "Authorization: Bearer $KAINGURU_API_KEY" \
  -d '{
    "model": "stable-diffusion-xl",
    "instance_type": "gpu.a100",
    "replicas": 2
  }'

{
  "id": "mdl_7x9k2m",
  "status": "deploying",
  "endpoint": "https://api.kainguru.com/v1/predict/mdl_7x9k2m"
}

Platform Capabilities

Everything you need to deploy and manage ML models in production

Image Generation

Deploy Stable Diffusion, DALL-E, and custom image models with automatic GPU scaling and batch processing.

Voice Synthesis

Real-time text-to-speech and voice cloning APIs with sub-200ms latency and support for 30+ languages.

Fine-Tuning

Fine-tune foundation models on your data with managed training pipelines, experiment tracking, and automatic checkpointing.

Custom Models

Bring your own PyTorch or TensorFlow models. Containerize, deploy, and serve with zero infrastructure changes.

Call Analysis

Transcribe, analyze, and extract insights from audio calls in real-time with speaker diarization and sentiment detection.

How It Works

Three steps from code to production

Define Your Model

Specify your model configuration in a simple YAML file or via the API. Choose from pre-built models or bring your own.

50+ pre-built model templates
Custom model packaging
Version control built-in

kainguru.yaml

name: my-image-model
runtime: pytorch-2.1
gpu: a100
model:
  base: stable-diffusion-xl
  weights: s3://my-bucket/weights
scaling:
  min_replicas: 1
  max_replicas: 10
  target_gpu_util: 75

Deploy via API

Push your model live with a single API call. Kainguru handles container orchestration, GPU allocation, and load balancing.

Zero-downtime deployments
Automatic GPU scheduling
Blue-green rollouts

deploy.sh

# Deploy with the Kainguru CLI
$ kainguru deploy --config kainguru.yaml

Deploying my-image-model...
  Pulling base image......done
  Loading weights.........done
  Allocating GPU (A100)...done
  Starting health checks..done

Model deployed successfully!
Endpoint: https://api.kainguru.com/v1/predict/my-image-model

Monitor & Scale

Track inference latency, GPU utilization, and request throughput in real-time. Auto-scaling adjusts capacity based on demand.

Real-time dashboards
Custom alert rules
Cost optimization insights

metrics.json

// Real-time model metrics
{
  "model": "my-image-model",
  "status": "healthy",
  "replicas": 4,
  "metrics": {
    "p50_latency_ms": 120,
    "p99_latency_ms": 340,
    "requests_per_sec": 847,
    "gpu_utilization": "72%"
  }
}

Why Kainguru

Focus on models, not infrastructure

No GPU Management

We handle GPU provisioning, driver updates, and CUDA compatibility. Just pick a GPU tier and deploy.

Horizontal Scaling

Auto-scale from 1 to 100+ replicas based on traffic. Pay only for compute you actually use.

Enterprise Security

SOC 2 compliant, end-to-end encryption, VPC peering, and role-based access control for your team.

REST API + SDKs

Clean REST API with official SDKs for Python, Java, Node.js, and Go. OpenAPI spec included.

Use Cases

Teams of all sizes use Kainguru to ship ML faster

E-Commerce

Product Image Generation

Generate product photos, backgrounds, and lifestyle images at scale. Reduce photography costs by 80%.

Call Centers

Real-Time Call Analytics

Transcribe, analyze sentiment, and extract action items from customer calls in real-time.

Content Platforms

AI-Powered Content

Generate and moderate text, images, and audio content for social platforms, media, and publishing.

ML Teams

Model Experimentation

Iterate faster with managed training pipelines, experiment tracking, and one-click deployments.

Frequently Asked Questions

Kainguru supports any model that can run in a Docker container. We provide pre-built templates for popular models including Stable Diffusion, Whisper, LLaMA, Mistral, and more. You can also bring your own PyTorch or TensorFlow models.

Yes. Upload your model weights to S3-compatible storage and reference them in your configuration. Kainguru handles packaging, container builds, and deployment automatically. You retain full ownership of your model weights and data.

Pay per compute-second. You're charged for the GPU time your models actively use. When traffic drops, replicas scale down and costs decrease automatically. No reserved instances or long-term commitments required.

We offer NVIDIA A100, A10G, T4, and L4 GPUs. Choose based on your workload: A100 for large model training and inference, T4 for cost-efficient inference, and L4 for balanced price-performance. Multi-GPU configurations are available for large models.

Kainguru exposes a standard REST API. Call it from any language or framework. We provide official SDKs for Python, Java, Node.js, and Go. Webhook support is built-in for async workloads, and we integrate with popular MLOps tools like MLflow and Weights & Biases.

Models run on dedicated GPU clusters in AWS (us-east-1, eu-west-1) and GCP (us-central1). We support VPC peering for private connectivity. On-premise deployment is available for enterprise customers.

All data is encrypted at rest (AES-256) and in transit (TLS 1.3). We are SOC 2 Type II compliant. Role-based access control, API key rotation, audit logs, and VPC peering are included. Your model weights and inference data are never shared or used for training.

Yes. Upload your training dataset, select a base model, and configure training parameters. Kainguru manages the training pipeline, checkpointing, and evaluation. Once training completes, deploy the fine-tuned model with a single click.