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Esaholic
Production Tech Stack

Enterprise AI Technology Stack Directory

Esaholic maintains a strict production technology evaluation framework across 16 software categories. We select agentic frameworks, vector databases, LLM inference runtimes, and MLOps platforms based on deterministic execution, sub-50ms latency SLAs, zero-data-retention compliance, and verifiable ROI.

Information-Gain Framework

Our Technology Selection Criteria & Trade-off Matrix

We never select tools based on vendor marketing. Every framework is evaluated on production gotchas, cost efficiency, and latency limits.

Agentic AI Frameworks

→ Category Index

State graph orchestrators, multi-agent frameworks, and Model Context Protocol (MCP) servers used to build deterministic, autonomous agent swarms with memory persistence.

Selection Decision Rule:

We select LangGraph for stateful multi-step cycles requiring checkpoint recovery, and Pydantic-AI/MCP for enterprise tool schemas requiring strict type safety.

LangGraphMCPLangChainLlamaIndexCrewAIPydantic-AI

Vector Databases & Search

→ Category Index

High-performance vector indexing platforms, dense-sparse hybrid search engines, and embedding storage layers for enterprise RAG and semantic retrieval.

Selection Decision Rule:

We deploy pgvector for sub-10M vector datasets co-located with relational data, and Pinecone/Qdrant for sub-50ms latency across 100M+ vector scales.

PineconepgvectorWeaviateQdrantMilvusChroma

LLMs & Model Runtime Engines

→ Category Index

Commercial foundation models, open-weights LLMs, and high-throughput inference engines (vLLM, TensorRT-LLM) optimized for sub-300ms token streaming.

Selection Decision Rule:

We route high-reasoning tasks to Claude 3.5 Sonnet / GPT-4o, and deploy vLLM-quantized Llama-3 70B for zero-data-retention on-premise execution.

Claude 3.5GPT-4oLlama 3vLLMDeepSeekMistral

Machine Learning Frameworks

→ Category Index

Deep learning libraries, classical ML algorithms, and computer vision transformers engineered for custom classification, regression, and forecasting.

Selection Decision Rule:

We standardize on PyTorch for custom neural network architectures and XGBoost for structured financial fraud tabular models.

PyTorchXGBoostTensorFlowscikit-learnHuggingFace

LLM Observability & Guardrails

→ Category Index

Real-time prompt tracing, hallucination detection, cost tracking dashboards, and adversarial prompt injection safety barriers.

Selection Decision Rule:

We deploy LangSmith/Langfuse for distributed trace telemetry and NeMo/Guardrails AI for strict PII and prompt injection filtering.

LangSmithLangfuseGuardrails AINeMo GuardrailsRagas

MLOps & Pipeline Orchestration

→ Category Index

Model registry servers, automated feature stores, automated retraining loops, and GPU cluster autoscaling infrastructure.

Selection Decision Rule:

We build MLflow model registry pipelines on Kubernetes for continuous integration and automated model drift detection.

MLflowKubeflowWeights & BiasesRayDVC

Data Orchestration & Pipelines

→ Category Index

High-throughput data streaming, ETL transformation pipelines, and real-time database CDC event buses for AI ingestion.

Selection Decision Rule:

We deploy Apache Kafka for real-time transaction event streams and Airflow/dbt for scheduled analytical warehouse loads.

KafkaAirflowdbtSparkDagster

Cloud AI & Compute Infrastructure

→ Category Index

Managed cloud AI model endpoints, enterprise security VPC tenancies, and GPU serverless compute clusters.

Selection Decision Rule:

We leverage AWS Bedrock for enterprise BAA compliance and Google Vertex AI for multi-modal vision document pipelines.

AWS BedrockAzure AIGoogle Vertex AINVIDIA NIMDatabricks

Evaluating AI Architecture & Tool Trade-Offs?

Schedule an architectural stack selection session with CTO Umar Abbas to review performance benchmarks and gotchas.

Schedule Stack Selection Session