Aegisora vs Experiential Labs: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Aegisora and Experiential Labs — 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.
Experiential Labs
Experiential Labs
Open-source AI gateway that routes every model through one endpoint at provider cost, then improves that traffic with caching, routing and fine-tuning.
Key features
- Unified Model Endpoint: One OpenAI-compatible POST endpoint fronts every hosted provider, your own bring-your-own keys and your own GPUs, so switching models is a parameter change rather than an integration.
- Zero-Markup Routed Tokens: Routed traffic bills at the provider's list price with 0% added on top, with the company earning on hosted inference and the Pro plan instead of on your tokens.
- Model Recommendation from Traffic: The intelligence layer watches real request patterns and tells you when switching models would win, including newly released models on the day they ship, with optional per-prompt optimization.
- Caching Opportunity Detection: Identifies where cache hit rate could improve and shows the projected savings, with repeated tokens returning at 90% off once enabled.
- Traffic-Trained Custom Models: Fine-tunes a model on your own traffic and proves it in closed-loop simulation before it ever serves, then exposes it through the same endpoint you already call.
- Spend Attribution Console: Breaks requests and dollars down by agent, person, model, provider and day across the whole organization, alongside catalog, usage and limits.
- Live Request Logs and Metrics: Streams per-request time-to-first-token, token counts, provider, status and cost, with dashboard rollups for requests, spend, p50 TTFT and cache hit rate.
- Governance Controls: Budgets, provider allowlists and attribution are available from the free tier upward for controlling who can spend what on which models.
Best for
- Consolidating Multi-Provider Access: Replace separate SDKs and keys for OpenAI, Anthropic, Google and others with a single endpoint and key across every application.
- Cutting Inference Spend: Use caching recommendations and model-switch suggestions to lower the cost of an existing production workload without changing application code.
- Replacing a Frontier Model with a Small One: Distill or fine-tune a small model on your own traffic for a narrow repetitive task and serve it at a fraction of frontier-model cost and latency.
- Chargeback and Budgeting: Attribute AI spend to individual agents, teams or people for internal cost allocation and to enforce per-key budget caps.
- Evaluating New Model Releases: Compare a newly shipped model against your current one on your own traffic before committing to a migration.
- Hybrid Local and Hosted Serving: Route some workloads to self-hosted GPUs at zero marginal cost while sending the rest to hosted providers through the same interface.
- Self-Hosting the Gateway: Run the open-source gateway inside your own infrastructure when hosted routing is not an option.