One control plane for data, AI, and model operations

Unify model lifecycle, data engineering, inference, and governance in one place. Multi-cloud, auditable, and secure by default.

The command center for your model fleet

Model Operations is the single place where every model in your organization is registered, versioned, deployed, watched, and retired. Pull artifacts from Hugging Face, GitHub, S3, or any registry, then walk through a guided deployment that handles resources, networking, secrets, and GitOps-ready manifests. Live metrics, scaling history, and full lineage connect what runs in production back to who shipped it, when, and why.

  • One-click registration from Hugging Face, GitHub, S3, Docker Hub, and private registries
  • Deployment wizard with AI-suggested configs (InferenceIQ-powered)
  • Multi-cloud targets: AWS EKS, GKE, AKS, Alibaba ACK, Nebius, and on-prem Kubernetes
  • Live dashboards: GPU use, latency, throughput, and error rates per deployment
  • End-to-end lineage: deployer, timestamp, config snapshot, and rationale
  • Auto-generated Kubernetes YAML & Helm, versioned like application code
  • HashiCorp Vault for secrets, nothing sensitive written to disk
  • Canary and blue/green rollouts with automated rollback
  • Per-team GPU quotas and namespace governance
  • Audit-ready history: deploy logs, change records, and compliance exports

Adapt foundation models to your domain

Fine-tuning turns general models into specialists. This module spans dataset prep through training config, experiment tracking, and evaluation, supporting parameter-efficient and full fine-tuning across the stacks your teams already use, with room to scale out on multi-GPU clusters.

  • LoRA & QLoRA for memory-efficient LLM adaptation
  • Full fine-tuning for smaller models and custom architectures
  • Dataset browser with quality scoring, filters, and augmentation helpers
  • Hyperparameter search with configurable strategies
  • Experiment tracking: metrics, loss curves, checkpoints
  • Distributed jobs across multi-GPU and multi-node clusters
  • Post-run evaluation against your benchmarks, automatically
  • Side-by-side comparison of fine-tuned variants
  • One-click promotion to a deployment-ready artifact
  • Lineage from training corpus to production endpoint

Train at scale. Track everything.

Train from scratch or continue pre-training in a managed, reproducible environment. From single-GPU experiments to large distributed runs, every job is scheduled, versioned, and auditable, so science and compliance stay aligned.

  • GPU-backed training jobs with fair scheduling and autoscaling hooks
  • AutoML paths for architecture search and hyperparameter sweeps
  • Live experiment view: loss, validation metrics, utilization
  • Dataset versioning tied to Data Studio lineage
  • Checkpoints with resume-on-failure
  • PyTorch, TensorFlow, JAX, and custom training loops
  • Cost visibility: GPU-hours per job and per experiment
  • Collaborative notebooks and managed training scripts
  • Templates for transformers, CNNs, diffusion, and more
  • CI hooks for retraining when drift is detected

Prepare, transform, and govern your AI data

Data Studio turns raw inputs into AI-ready datasets. Build pipelines that clean, label, and version data inside a governed perimeter, whether you are fueling training, eval sets, or RAG corpora, with traceability from source to model.

  • Visual pipelines for ingest, transform, and export
  • Dataset versioning with lineage to every downstream run
  • Labeling workspaces, QA scoring, and reviewer workflows
  • Schema checks and automated quality gates
  • PII discovery and redaction for regulated workloads
  • Structured, unstructured, and streaming sources
  • Connectors: S3, GCS, Azure Blob, databases, APIs, uploads
  • RBAC for collaborative dataset ownership
  • Profiling and distribution views for sanity checks
  • One-click handoff to training, fine-tuning, or evaluation

AI that optimizes your AI

InferenceIQ removes guesswork from inference. Point it at any model, from compact encoders to frontier LLMs, and get ranked, scored recommendations for engines, hardware, quantization, and cost, with plain-language rationale and confidence you can defend in a review.

  • Architecture-aware analysis: parameters, attention, quantization fit
  • Multi-objective scoring: latency, throughput, cost, reliability, sustainability
  • Engine picks: vLLM, TGI, TensorRT-LLM, ONNX Runtime, Triton, llama.cpp
  • GPU sizing across 13+ profiles and real cloud pricing
  • Quantization guidance: FP16, FP8, INT8, INT4, AWQ, GPTQ, GGUF
  • Quick Optimize: model link in, ranked options in seconds
  • Confidence scores and human-readable trade-off notes
  • Org-scoped learning as your team deploys more
  • Knowledge base fed by experiments, Hugging Face, docs, and research
  • Air-gapped mode for regulated environments, no external LLM required

From Hugging Face to production in minutes

Launch Pad is the fastest path from a public model card to a live endpoint. Browse, compare, wire credentials, and deploy, whether you need a one-click hosted route or a full Kubernetes path with InferenceIQ baked in.

  • Hugging Face discovery with trends, downloads, and community signals
  • Filters by task, architecture, and model size
  • Side-by-side pricing across 29+ hosted inference providers
  • One-click deploy to SageMaker, Nebius, Together, RunPod, and more
  • Kubernetes path with optimization recommendations included
  • Gated models: licenses and tokens handled securely
  • Color-coded cost bands for quick budget sanity checks
  • Rich model cards: architecture, params, recommended use cases
  • Handoff into Model Operations for ongoing lifecycle control
  • Unified history: endpoints, revisions, and owners

Retrieval that reasons, not just retrieves

Agentic RAG builds pipelines where agents decompose questions, pull from multiple sources, verify answers, and iterate until quality bars are met, backed by document processing, chunking, embeddings, and guardrails you can audit.

  • Multi-source retrieval: docs, databases, APIs, knowledge bases
  • Agentic flows: decomposition, self-check, iterative refinement
  • Ingestion for PDFs, Office, HTML, Markdown, code, structured rows
  • Chunking: overlap, semantic splits, recursive strategies
  • Embedding lifecycle across providers
  • RAG Sentinel: scoped access, PII handling, safety policies
  • Index tuning with re-index on source changes
  • Streaming ingestion for near-real-time corpora
  • Evals: retrieval quality, faithfulness, hallucination signals
  • Observability: latency, attribution, and cost per query

Ship with confidence, not hope

Evaluation Hub makes quality gates explicit. Define criteria, run automated benchmarks, compare variants with statistics, and block promotion when metrics regress, across accuracy, safety, latency, and domain-specific rubrics.

  • Custom frameworks with pass/fail thresholds you own
  • Benchmark suites on every version before release
  • Statistical comparisons across model variants
  • Bias and fairness checks across cohorts
  • Toxicity and policy tests with configurable rules
  • Grounding tests for RAG and knowledge-heavy workloads
  • Load-style profiling for realistic traffic
  • Mandatory gates in promotion workflows
  • Historical trends across versions and teams
  • CI integration for eval-on-commit workflows

Generate data that doesn’t exist yet

Synthex produces high-quality synthetic data when real data is scarce, sensitive, or skewed. Shape distributions, stress-test robustness, scrub PII, and leave an audit trail, whether you need thousands of rows or millions.

  • Generation for text, structured fields, and multimodal scenarios
  • Distribution controls aligned to production-like profiles
  • Bias-injection scenarios for robustness experiments
  • Automatic anonymization for compliance
  • Statistical checks that synthetic sets preserve what matters
  • Policy audits against governance rules
  • Seed from small real samples to scale coverage
  • Export into training, eval, or Data Studio
  • Quality and diversity metrics per dataset
  • Cost-efficient alternative to manual labeling at scale

Build, test, and deploy AI agents

Agent Studio is where autonomous agents take shape: tools, workflows, simulation, and production guardrails. From single assistants to multi-agent systems. Design visually, test safely, and ship with monitoring built in.

  • Visual builder for agent flows and handoffs
  • Tooling: APIs, databases, search, custom functions
  • Multi-agent orchestration with delegation patterns
  • Templates for support, research, coding, and more
  • Simulated environments before go-live
  • Guardrails on actions, outputs, and policies
  • Execution traces for debugging and compliance
  • Versioning for agents, tools, and workflow graphs
  • A/B testing for prompts and strategies
  • Production deploys with autoscaling, logging, and cost tracking