Aegisora vs Router by Ramp: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Aegisora and Router by Ramp — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Aegisora
Aegisora
Narrow control plane for AI agents: enforce least-privilege on tool/API calls, block PII leaks, and generate human-readable audit logs.
Key features
- Sidecar proxy: deploys inside your VPC as a lightweight proxy so raw payloads and PII never touch third-party infrastructure.
- Least-privilege enforcement: block unauthorized tool calls and API requests based on runtime policies.
- Fail-closed compliance: on proxy or network disruption, policies default to fail-closed to prevent unverified execution.
- Immutable audit logs: every tool call, payload interception, and policy decision recorded as structured, human-readable logs.
- PII masking: intercept and mask PII in agent payloads before they leave your perimeter.
- Broad LLM & tool integrations: works with OpenAI, Anthropic, Azure AI, AWS Bedrock, GitHub, Slack, and Vercel out of the box.
- Governance controls: rule-based and dynamic policy libraries, plus RBAC/SSO and multi-tier approvals in Enterprise.
- Compliance-ready: SOC 2 / ISO compliance suite and custom SIEM integration in the Enterprise VPC tier.
Best for
- Security team enforces which tools an internal agent can call and blocks anything outside its least-privilege scope.
- Compliance officer generates SOC 2 / ISO evidence from immutable audit trails of every agent decision.
- CISO deploys AI copilots in a regulated environment while keeping raw payloads and PII inside the corporate VPC.
- Platform team masks PII in outgoing prompts before they hit a third-party LLM provider.
- SecOps investigates an agent incident using a full timeline of tool calls, prompts, and policy decisions.
- Enterprise standardizes runtime governance across many agents, LLM providers, and business units.
Router by Ramp
Ramp
Ramp's LLM gateway routes each request to the cheapest model meeting your quality bar, cutting inference costs ~40% behind one endpoint and one bill.
Key features
- Cost-Aware Automatic Routing: Every request is matched to the lowest-cost model that still meets your performance requirements, reported to cut inference spend by about 40% on average.
- One Key for Every Model: Closed and open-source models from vetted providers sit behind a single endpoint, key and invoice.
- Rolling Strategy Updates: New cost-saving routing strategies and newly benchmarked default models roll in automatically without changing your integration.
- Score Versus Spend Reporting: Built-in benchmarking shows metric distributions and model summaries so you can see quality and cost side by side.
- Flex Tier Routing Share: A tunable split between default and flexible routing lets you dial how aggressively requests are shifted to cheaper models.
- US-Hosted Providers with ZDR: All vetted providers are US-hosted, with zero-data-retention options for sensitive workloads.
- Switchyard Integration: Works with Switchyard for model and provider routing, surfaced directly in the CLI's cost display.
- One-Command CLI Setup: Install and configure with a single curl command from agents.ramp.com, with an agent-friendly copy-paste flow.
Best for
- Trimming Production Inference Spend: Route high-volume, low-difficulty requests to cheaper models while keeping frontier models for the hard ones — Delphi reports a 92% model cost reduction across billions of tokens.
- Multi-Provider Consolidation: Replace separate OpenAI, Anthropic and open-model integrations with one endpoint and one bill.
- Model Benchmarking Before Migration: Test candidate models against your real workloads and compare score against spend before switching defaults.
- Finance and Engineering Alignment: Give CFOs a single, attributable AI spend line while engineers keep the best model for each workload.
- Compliance-Constrained Deployments: Keep inference on US-hosted providers with zero-data-retention options for regulated data.
- Agent Cost Control: Cap the runaway token spend of long-running agent loops by routing their routine steps to cheaper models automatically.