LLMOps & Model Operations
Operationalize large language models in production. From deployment and monitoring to fine-tuning and cost optimization.
LLMOps Services
Production-grade infrastructure for large language models
Model Deployment
Deploy proprietary, open-source, or fine-tuned models with low-latency serving and auto-scaling.
Monitoring & Observability
Track latency, token usage, quality metrics, and cost. Detect drift and anomalies.
Cost Optimization
Reduce token costs through caching, quantization, distillation, and request routing.
Fine-Tuning & Customization
Customize models for your domain with efficient fine-tuning and prompt engineering.
Retrieval & Context
Implement RAG systems with vector databases and knowledge retrieval for accurate responses.
Safety & Compliance
Content filtering, PII detection, audit logging, and governance for responsible AI.
Common Scenarios
Enterprise LLM Chatbots
Deploy branded chat interfaces with knowledge integration, conversation memory, and audit trails.
Retrieval-Augmented Generation
Ground LLM responses in your proprietary data with vector databases and semantic search.
Content Generation Pipelines
Scale content production with quality controls, fact-checking, and brand compliance.
Custom Model Optimization
Fine-tune open-source models to match your task and reduce costs versus API-based models.
Technology Stack
Model Platforms
vLLM, TensorRT-LLM, Ray Serve
Inference Engines
OpenAI API, Anthropic, Hugging Face
Vector Databases
Pinecone, Weaviate, Milvus
Monitoring
Prometheus, Datadog, LangSmith
Implementation Process
Assess
Understand your LLM requirements and use cases
Design
Architecture and model selection
Build
Deploy and integrate with your systems
Optimize
Monitor, fine-tune, and reduce costs
Operationalize LLMs at Scale
Production-ready infrastructure for large language models with monitoring, optimization, and cost control.
Build Your LLM Stack