linkgo

Caveman vs Lightning AI: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Caveman and Lightning AI — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

C

Caveman

Julius Brussee

Freemium

Efficiency stack that caches, compresses, and routes AI traffic to cut LLM output tokens by up to 65% with verified savings.

Key features

  • Caveman Skill: MIT-licensed Claude Code skill that teaches 30+ agents (Claude Code, Codex, Cursor, and more) to answer in a compressed dialect, cutting output tokens ~65% while keeping code and errors byte-exact.
  • Local Proxy Wrap: One command (`caveman claude`) launches your agent with recoverable local context compression — no account required, BYOK, engine stores original bytes before lossy replacement.
  • Recoverable Context Compression: Engine recognizes logs, JSON, code, diffs, and tables, then sends smaller eligible context to the model and can restore the originals on demand.
  • Agent SDK: `@caveman-ai/agent` TypeScript SDK adds catalog-price guards, per-request token bills, and eval-gated context plans to production agents.
  • Cave Score & Ledger: Inferred local savings score and a verified 'causal-cache' ledger on paid tiers so you can prove cut tokens and cut dollars.
  • Managed Cloud Gateway: Point traffic at one URL and caching / compression / routing run eval-gated on autopilot, with a synced savings dashboard.
  • Browser Extension: Ships for ChatGPT, Claude, and Gemini so end-user chats benefit from the same output compression without any code changes.
  • Enterprise & OEM: Same stack self-hosted in your cloud or datacenter with signed savings receipts, zero data retention, and OEM embed options.

Best for

  • LLM Bill Reduction: Cap OpenAI, Anthropic, or Google spend without changing model choice by cutting output tokens per response across your agent fleet.
  • Coding Agent Efficiency: Install the skill to make Claude Code, Codex, Cursor, and other CLI agents produce terse, byte-exact answers so long tasks fit in context.
  • Provider Wrap for Production Agents: Use the SDK to add per-call token bills, catalog-price guards, and eval-gated context plans to LangChain / custom agents.
  • Central Cost Gateway: Point every agent in the org at Caveman Cloud so caching and routing are enforced from one URL with a shared dashboard.
  • On-Prem or OEM Embed: Ship the Enterprise stack inside a regulated network or embed it in your own AI product with signed savings receipts and zero data retention.
  • Chat-App Compression: Install the browser extension for ChatGPT, Claude, or Gemini to keep casual chats short, cheaper, and inside the context window.
View Caveman details
Lightning AI logo

Lightning AI

Lightning AI

Freemium

All-in-one platform to prototype, train, scale, and serve ML models from the browser with zero setup, from the creators of PyTorch Lightning.

Key features

  • Browser-based Development: Zero-setup web studio for coding, prototyping, and collaborative experiments directly from the browser, reducing onboarding friction for teams.
  • Integrated Training Stack: First-class integration with PyTorch Lightning and Lightning Fabric to run experiments, leverage built-in training features, and accelerate model development workflows.
  • LitServe Inference Engine: Deploy any model type (vision, audio, text) or full AI systems (agents, RAG, pipelines) with batching, multi-GPU support, streaming outputs, and custom logic without YAML or heavy MLOps.
  • Model Hosting and Checkpoints: LitModels capability to save, load, host, and share model checkpoints with enterprise-grade access controls and options to host on Lightning or self-managed cloud.
  • Autoscaling Cloud Deployment: One-command deployments to Lightning AI cloud with autoscaling, security controls, and high-availability SLAs (99.995% uptime when deployed via platform).
  • LLM Router & Agent Framework: Tools and libraries to route calls to LLM APIs, unified billing, retries/fallbacks, logging, and a minimal agent framework for building LLM-based applications.
  • Dataset & Optimization Tools: Utilities such as litData for transforming and optimizing datasets at scale and Lightning Thunder compiler for performance/memory optimizations during training and inference.
  • Self-Host Flexibility: Option to self-host all components for full control or use Lightning's managed cloud for faster time-to-production with built-in monitoring and security.
  • Browser-based collaborative development with zero setup
  • End-to-end tooling: prototype, train, optimize, host, and serve models
  • LitServe: flexible inference engine for agents, RAG, pipelines, multi-model serving, streaming and batching
  • LitModels: save, load, host, and share model checkpoints with enterprise-grade access controls
  • LitData: dataset transformation and optimization at scale
  • PyTorch Lightning / Lightning Fabric integration for structured training and low-level control
  • Lightning-thunder: PyTorch compiler optimizations for performance, memory, and parallelism
  • One-click cloud deployment and CLI (e.g., lightning deploy server.py --cloud) with autoscaling and managed uptime
  • Support for self-hosting or managed hosting, multi-GPU, custom logic, and advanced routing (LLM router, retries, fallback, logging)
  • Open-source components under Apache-2.0 and active GitHub ecosystem

Best for

  • Collaborative Prototyping: Rapidly prototype model ideas and iterate with teammates in a browser workspace without local environment setup.
  • Training Large Models: Run scalable training experiments using PyTorch Lightning/Fabric with built-in optimizations and support for multi-GPU or distributed setups.
  • Production Inference for Agents and RAG: Deploy multi-model agents, chatbots, or retrieval-augmented generation pipelines with LitServe’s batching, streaming, and custom logic features.
  • Model Hosting and Sharing: Save, host, and share model checkpoints with access controls for team collaboration or enterprise governance using LitModels.
  • Cloud Deployment with Autoscaling: Deploy model servers to Lightning AI cloud with autoscaling and high uptime guarantees for production traffic.
  • Self-Hosted Enterprise Deployments: Run the full stack on private infrastructure for customers needing full control over data, security, and compliance.
  • Rapid prototyping and collaborative model development in the browser without environment setup
  • Training and fine-tuning models using structured PyTorch Lightning workflows
  • Deploying inference services, agents, chatbots, and RAG pipelines with multi-model and streaming support
  • Hosting and sharing model checkpoints with access controls and integration into training workflows
  • Transforming and optimizing datasets for faster training at scale
  • Applying compiler-level optimizations for faster training and inference on multi-GPU setups
  • Self-hosting ML systems on customer infrastructure or using Lightning AI managed cloud for autoscaling production
View Lightning AI details