◆ Gentoo Logic · Modeling Warehouse

Architecture comparison

Lightning AI vs Together AI

Lightning AI runs closer to the foundational model (Native (own / self-hosted weights)) than Together AI (Native (own / self-hosted weights)). Shared foundation: Meta · Llama (highlighted).

Lightning AI

AI Infra

Proximity
Native (own / self-hosted weights)
Models
Meta · Llama
Cloud · datastore
Multi-Cloudobject
Compliance
SOC 2
Architecture

Builds PyTorch Lightning + a studio to train/serve open models (Llama, etc.) on cloud GPUs.

Together AI

AI Infra

Proximity
Native (own / self-hosted weights)
Models
Meta · LlamaMistralproprietary / self-built models
Cloud · datastore
HybridAmazon S3 (Iceberg tables)[2]Apache Iceberg[2]MotherDuck (DuckDB-based analytics serving layer)[2]Kinesis[2]Kafka[2]
Compliance
PCI (via PCI-compliant infrastructure for regulated enterprise deployments)[12]
Architecture

Together AI operates its own GPU cloud and managed Kubernetes clusters as an AI-native infrastructure layer, while also integrating with external cloud storage and streaming services for data and analytics workloads.[2][11][12] The production stack is centered on open-weight models deployed as containerized workloads on Together-managed GPU clusters, exposed via serverless inference APIs and voice/agent pipelines.[3][4][9][11][16]

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