LLM & MLOps
Hybrid LLM control with full observability.
Manage model training, fine-tuning, and inference across self-hosted Ollama clusters, Azure OpenAI, AWS Bedrock, and any OpenAI-compatible API from a single console.
Hybrid Model Management
Manage Ollama, vLLM, OpenAI, Azure OpenAI, and AWS Bedrock models side-by-side with unified API routing and fallback chains.
Training & Fine-Tuning Pipelines
Visual pipeline builder for model training, fine-tuning with LoRA/QLoRA, and automated evaluation with custom benchmarks.
Inference Optimization
Dynamic model routing, load balancing, GPU scheduling, quantization support, and automatic scaling based on demand.
Token & Cost Analytics
Real-time token usage tracking per model, user, and team. Budget alerts, cost allocation, and ROI dashboards.
Model Performance Monitoring
Track latency, throughput, error rates, and quality scores. Automated alerting when model performance degrades.
Guardrails & Safety
Content filtering, PII detection, prompt injection protection, and output validation across all models.
Capabilities
Everything included, out of the box
Use Cases
Built for real-world scenarios
Self-Hosted LLM Deployment
Deploy Llama, Mistral, or DeepSeek on your own GPU clusters with Ollama or vLLM. Full data sovereignty with zero API costs.
Hybrid API + Self-Hosted Strategy
Route sensitive queries to self-hosted models and general queries to cloud APIs. Automatic fallback when self-hosted clusters are at capacity.
Enterprise RAG Deployment
Build RAG pipelines that connect LLMs to your corporate wikis, documentation, and databases. Automatic chunking, embedding, and retrieval.
Model Cost Optimization
Reduce LLM costs by 60-80% with intelligent routing between expensive frontier models and cost-effective open-source alternatives.
See this in action
Book a personalized demo and see how Dataraq can transform your operations.
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