A complete AI/LLM application architecture. API gateway, LLM orchestration, vector database, prompt management, streaming responses, and cost monitoring - all scaffolded.
OpenAI/Anthropic integration with fallbacks, retry logic, and response streaming.
Pinecone/Weaviate for semantic search, RAG, and knowledge base retrieval.
Versioned prompt templates, A/B testing, and performance tracking.
Request preprocessing, context assembly, LLM call, and response post-processing.
Token usage tracking, budget alerts, and per-user cost allocation.
Automated quality testing, hallucination detection, and regression monitoring.
Three steps to design an MLOps-ready AI platform architecture
Define your AI application's core capabilities — the models you train or fine-tune, data sources, inference requirements, and latency constraints. The AI maps these to proven ML infrastructure patterns.
Cybewave generates a complete architecture covering data ingestion, feature engineering, model training orchestration, experiment tracking, model serving, and monitoring — designed for the unique compute demands of AI workloads.
Review production-ready diagrams showing how your training pipeline, feature store, model registry, inference service, and monitoring stack connect — with clear boundaries between experimentation and production environments.
Critical scenarios where visualizing your ML infrastructure prevents costly missteps
Architect end-to-end training pipelines with data validation, feature computation, distributed training, hyperparameter optimization, and model evaluation stages — all orchestrated for reproducibility.
Design inference architectures handling real-time and batch predictions with model versioning, A/B traffic splitting, autoscaling GPU instances, and graceful rollback capabilities.
Build a centralized feature store serving consistent features to both training and inference, with point-in-time correctness, feature versioning, and low-latency online serving.
Architect experimentation infrastructure that routes traffic between model versions, collects performance metrics, and makes statistically valid deployment decisions automatically.
Design a labeling pipeline combining pre-labeling with model predictions, human annotation interfaces, quality assurance checks, and consensus algorithms for ground truth generation.
Architect compute infrastructure with GPU scheduling, preemptible instance management, training job queuing, and cost optimization strategies across cloud providers.
AI startups face architectural challenges that traditional software companies never encounter. Model training and inference have fundamentally different compute profiles — training demands massive burst GPU capacity while inference requires consistent low-latency responses. Designing these as a single monolithic system leads to either wasted GPU resources during idle training periods or inference latency spikes when training jobs compete for compute.
Data pipeline reproducibility is critical for AI products in ways that most engineers underestimate. When a model's performance degrades in production, you need to trace back through the exact data, features, and hyperparameters that produced the previous version. Without a well-architected pipeline with versioned artifacts at every stage, debugging model regressions becomes guesswork rather than systematic investigation.
Cybewave helps AI teams visualize the full MLOps lifecycle — from raw data ingestion through feature engineering, training orchestration, model registry, inference deployment, and monitoring feedback loops. By diagramming these systems before building them, you can identify where data lineage tracking must be enforced, how model versions flow through your deployment pipeline, and where monitoring hooks need to catch data drift before it silently degrades your product's quality.
AI-designed LLM architecture with scaffolded code. Free to start.
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