Your data pipeline. Your models. Your infrastructure.
From real-time streaming to model training to edge inference — a complete data and AI platform that never requires your data to leave your perimeter.
Run any model. One line of code. Your hardware.
Replicate and Hugging Face Inference API let developers run models with a single API call — but the inference happens on their servers, and your sensitive data travels to their infrastructure. AravaliStack gives you the same developer experience, entirely on your own GPUs.
Deploy any Hugging Face model, any custom-trained model, or any fine-tuned checkpoint to your AravaliStack GPU cluster. Serve it through a standardised REST API. Your data never leaves your perimeter.
Axiom reimagined observability for AI engineering — prompt tracing, cost attribution per LLM provider, and agent workflow visibility. AravaliStack ships this capability natively, on your infrastructure, with zero-sampling log retention.
End-to-end OpenTelemetry tracing across your entire application stack — services, databases, queues, and AI inference calls — in one unified trace view.
Trace every LLM call, every agent step, every tool invocation. See prompt → response → cost per call. Debug agent loops. Identify which prompts are driving latency or cost spikes.
Per-team, per-project, per-model token consumption tracked in real time. Set budget limits on AI inference spend. Alert before a runaway agent loop becomes a ₹10L bill.
Axiom built its reputation on one claim: never sample your logs. AravaliStack's observability layer uses a columnar log store — every log line retained, queryable at any time, at any scale. No data loss, no "we sampled it out."
For regulated environments — RBI, IRDAI, SEBI — this isn't just useful. It's the answer when the auditor says: "Show me every authentication event from March 2024."
Track model drift, accuracy degradation, and latency SLAs over time. Compare model versions. Roll back to previous checkpoints when production performance degrades.
Real-Time Data Streaming
High-throughput event streaming with Kafka. Multi-tenant topics, per-tenant access controls, schema registry. No data to an external broker.
Distributed Processing
Spark and Flink for batch and streaming workloads. GitOps-managed jobs — reproducible, auditable, auto-scaled with KEDA.
ML Model Training
GPU-scheduled training with MLflow experiment tracking. Every run reproducible — data, code, parameters, and results versioned together.
Data Science Notebooks
Browser-based JupyterLab with pre-configured ML libraries. Notebooks version-controlled. Environments defined as code in Git.
Edge Inference
Deploy trained models to edge locations via KServe/Triton. Offline-first operation with GitOps sync on reconnection.
Self-Hosted CDN
Intelligent caching and origin shielding. Bandwidth-optimised delivery. No third-party CDN required.
- One-click model deployment from Hugging Face Hub — to your hardware
- Standardised inference API — same interface as Replicate, pointed at your endpoint
- GPU auto-scaling — scale to zero when idle, scale up on demand
- Multi-model serving — run dozens of models concurrently with resource isolation
- Private model registry — fine-tuned models versioned and served from Harbor
- Zero data egress — inference input and output stays on your infrastructure
- Prompt evaluation & experimentation framework
- A/B testing across model versions
- Automated drift detection alerts
- OpenTelemetry
- Jaeger
- Tempo
- LangChain
- AutoGen
- LlamaIndex
- Per-namespace
- Budget alerts
- import aravali_inference
- client = aravali_inference.Client(
- endpoint= "https://infer.your-dc.in"
- output = client.run(
- For BFSI & Government: KYC models, fraud detection, document classification — all run on your hardware. Sensitive customer data processed by AI with zero cloud egress.
- 📈 Model Performance Monitoring