Atlas Inference Engine vs SigmaShake

Atlas Inference Engine and SigmaShake are both inference engines & infra tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Atlas Inference Engine

The vendor page benchmarks Atlas at 3.1x the decode throughput of vLLM on Nvidia DGX Spark hardware — 111 tok/s average versus 37 tok/s on Qwen3.5-35B, with a cold start measured in two minutes instead of ten. That gap exists because Atlas ships no Python, no PyTorch, and no JIT warm-up: every path from HTTP request to kernel dispatch is compiled. The tradeoff is hardware specificity — hand-tuned CUDA kernels target Blackwell SM120/121, so teams not running DGX Spark get none of the headline numbers. The model matrix covers Qwen, Gemma, Nemotron, Mistral, and MiniMax, but every recipe is written for that hardware profile. Teams running other GPU generations are not the audience.

SigmaShake

SigmaShake intercepts tool calls from agents running in Claude Code, Cursor, VS Code Copilot, and Gemini CLI, evaluating each action against a rule set before it executes. The vendor states decisions resolve in roughly 85 ms using deterministic native evaluation — no model inference, no GPU, no token spend. Rules follow an Allow/Ask/Deny pattern, where Ask routes the action to a human approval queue rather than blunting everything with a hard block. The desktop app installs in about 30 seconds with no admin rights; the CLI drops into any shell or CI hook chain. Self-hosting is supported, which means the guardrail layer stays offline and never sends your code or commands to a third-party model.

Attribute	Atlas Inference Engine	SigmaShake
Pricing	Free	Paid
Price	—	$5/mo
Free trial	No	No
Open source	Yes	No
Has API	Yes	No
Self-hosted option	Yes	Yes
Platforms	Linux (Ubuntu 22.04+) with NVIDIA GPU support (Blackwell GB10 primary, Hopper/Ampere in development)	Windows 10+, macOS 14+, Linux (Ubuntu 22.04+ / Fedora 38+ / Pop!_OS)
Pros	~2.5 GB container image with no Python or PyTorch dependencies, which means cold starts take two minutes instead of ten — a difference that compounds across every iteration in an agentic development loop. Compiled Rust + CUDA architecture with no GIL or JIT warm-up, so request latency is consistent from the first token rather than degrading during the warm-up window that costs vLLM its first several minutes. Hand-tuned CUDA kernels per model family with NVFP4 and FP8 on Blackwell tensor cores, so quantized inference does not trade throughput for accuracy the way a generic quantization layer would. Multi-Token Prediction speculative decoding built in, so a single DGX Spark node serving a 35B model reaches throughput that would otherwise require additional hardware or a more complex multi-node setup. OpenAI-compatible API endpoint out of the box, so existing tooling — Claude Code, Cline, Open WebUI — connects without a translation layer or custom client code.	Deterministic local evaluation at roughly 85 ms per check, so you avoid the latency and per-token cost of routing every agent action through a model-based policy guard. Ask mode holds a risky action in a human approval queue rather than blocking it outright, which means your agent keeps moving on safe tasks while you review the one call that needs a second look. PreToolUse hook integration for Claude Code and MCP server integration for Cursor, Codex, and VS Code Copilot, so the guardrail wires into agents your team is already running without a custom shim. Self-hosted deployment with no model inference, so your code, file paths, and shell commands never leave the machine — critical for teams with data-handling obligations. Per-user install with no admin or UAC rights required, which means individual developers can adopt it without waiting for IT to sign off on an organization-wide rollout.
Cons	Every published benchmark and kernel optimization targets Nvidia Blackwell SM120/121 on DGX Spark. Teams running Ampere, Ada, or Hopper GPUs get none of the headlined throughput numbers — the architecture constraint is not a tuning issue, it is baked into the kernel design. Those teams are still on vLLM or TensorRT-LLM. The model matrix is a curated, hand-tuned list — Qwen, Gemma, Nemotron, Mistral, MiniMax — not an open registry. A team that needs to serve a fine-tuned model outside that matrix hits a wall immediately and either waits on the Atlas roadmap, opens a Discord request, or returns to vLLM where arbitrary HuggingFace checkpoints load without curation. AGPL-3.0 is the default license. Any team building a closed-source product or operating a SaaS service on top of Atlas is required to obtain a commercial license. Teams that discover this constraint after building on the free version face a licensing conversation before they can ship.	No API is exposed, so teams building custom agent runtimes or embedding safety checks inside their own orchestration code cannot call SigmaShake programmatically — they wrap the CLI binary, which introduces a process boundary and complicates error handling at scale. The SHAKEDOWN benchmark that positions SigmaShake as the top-ranked guardrail was authored by SigmaShake, and competitor scores were modeled from public docs rather than measured runs; teams doing their own evaluation should run independent tests before treating the benchmark as a neutral comparison. Fleet management and team-level policy enforcement are paid-only features, which means a free-tier team cannot centrally audit what rules individual developers are running — a gap that matters the moment more than one engineer is using an AI coding agent on shared infrastructure. Windows support is the primary release target based on page emphasis and download prominence; macOS and Linux builds are listed but community reports on edge cases outside Windows are sparse, so teams running heterogeneous developer environments should validate on non-Windows machines before committing.

Bottom line

Atlas Inference Engine is free while SigmaShake is paid; Atlas Inference Engine is open source; only Atlas Inference Engine exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between Atlas Inference Engine and SigmaShake?

Atlas Inference Engine is Free and open source, while SigmaShake is Paid. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Atlas Inference Engine better than SigmaShake?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.