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Apertis vs SigmaShake

Apertis 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.

Apertis

Apertis

Apertis functions as an API gateway layer that sits between your coding agents — Cursor, Cline, Claude Code and the like — and the underlying model providers. You point your agent at one endpoint, authenticate once, and the platform handles provider routing, failover, and cost tracking behind it. The vendor states that automatic failover keeps production agents running when a provider has an outage, which removes a class of silent failures teams usually discover too late. The free tier covers basic models with no payment required; premium models and higher quotas are paid-only features. The platform is cloud-only — no self-hosted option — so your API traffic routes through Apertis infrastructure, and teams with data-residency requirements hit that wall immediately.

SigmaShake

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.

AttributeApertisSigmaShake
PricingPaidPaid
Price$33/quarter$5/mo
Free trialNoNo
Open sourceNoNo
Has APIYesNo
Self-hosted optionNoYes
PlatformsWeb-based API; CLI/TUI agents via supported integrationsWindows 10+, macOS 14+, Linux (Ubuntu 22.04+ / Fedora 38+ / Pop!_OS)
Pros
  • Single API endpoint for multiple model providers, so rotating a compromised key or switching a model mid-project touches one config entry instead of one per agent per provider.
  • Automatic provider failover is built into the routing layer, which means a production coding agent keeps running through an upstream outage instead of throwing an unhandled exception at the worst possible time.
  • Unified billing across providers, so monthly AI infrastructure cost is one line item rather than a reconciliation exercise across five separate vendor invoices.
  • New model versions are added to the platform automatically per vendor documentation, so your agent gains access without a credentials update or a config change on your end.
  • Free tier covers basic models with no payment required, which means a team can validate the integration and routing behavior before committing budget to premium model access.
  • 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
  • No self-hosted deployment option exists — all API traffic routes through Apertis cloud infrastructure. Teams with data-residency requirements, HIPAA obligations, or any compliance posture that restricts where model prompts travel cannot use this platform and will move to a self-hostable gateway like LiteLLM or a direct provider integration instead.
  • The value proposition depends entirely on the providers Apertis has contracted with at any given moment. If your agent's critical model — a specific Anthropic version, a fine-tuned endpoint — is not available through the platform, you are back to maintaining a direct integration alongside the gateway, which recreates the fragmentation problem you were solving.
  • Cost predictability, which the platform positions as a core benefit, breaks down if your agent usage is highly variable and you are comparing against a pay-per-token direct model. Flat subscription pricing on a low-usage month means you overpay relative to direct API access — teams that run bursty, project-gated workloads rather than continuous agent pipelines see worse economics here.
  • 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

Only Apertis exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between Apertis and SigmaShake?

Apertis is Paid, while SigmaShake is Paid. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Apertis 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.

Apertis vs SigmaShake: which should I pick?

Pick Apertis if its pricing model, openness, or platform fit matches your constraints; pick SigmaShake otherwise. Check free-trial availability on each listing if you want to test before committing.

Comparison data is sourced and verified by the AIDiveForge data pipeline. AIDiveForge is editorially independent.