Experiential Labs vs TrustedRouter: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Experiential Labs and TrustedRouter — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
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.
TrustedRouter
Lore Hex Corp
OpenAI-compatible gateway to 600+ models across 90+ providers, routed through an attested enclave that logs no prompt or output content.
Key features
- One OpenAI-Compatible Endpoint: Point an existing OpenAI SDK client at api.trustedrouter.com/v1 and reach 600+ models across 90+ providers and three clouds without rewriting call sites.
- Attested No-Log Prompt Path: Requests cross a gateway running in a Trusted Execution Environment that publishes no prompt or output logs, with the image digest matchable to a public source commit before production traffic moves.
- Routing Promise Aliases: Named models like trustedrouter/auto, /zdr, /e2e, /eu and /synth make failover, zero-retention, confidential compute, EU residency and multi-model answers explicit at request time.
- Provider Failover and Regional Routing: Automatic rollover to healthy providers keeps one upstream outage from becoming an application outage, with region-constrained routes available.
- Bring Your Own Key: BYOK preserves existing committed-spend discounts and enterprise rate limits while still gaining the attested prompt path and unified API.
- Per-Model Published Pricing: Input, cached-input, output and provider-specific prices are listed per model, with a flat 5.5% markup and no monthly plan.
- Agent-Ready Discovery: Agents can read llms.txt or connect through the provided MCP server instead of scraping documentation.
- Open Source Codebase and SDKs: The gateway is fully open source with JS/TS, Python and Go clients, so privacy and attestation claims can be independently audited.
Best for
- Regulated Data Processing: Running legal, medical or financial documents through LLMs where a no-retention, inspectable prompt path is a client requirement.
- OpenRouter Migration: Moving an existing multi-model application to a cheaper gateway with one base_url change and no client rewrite.
- Cost Optimization Across Models: Routing routine traffic to open models like Qwen, GLM, DeepSeek, Gemma, Kimi or MiniMax while keeping frontier models available for hard requests.
- EU Data Residency: Constraining inference to European providers for workloads governed by EU rules.
- Production Reliability Engineering: Using provider failover so a single vendor outage does not take down a customer-facing AI feature.
- Enterprise Spend Consolidation: Unifying several provider contracts behind one API and one key while keeping negotiated BYOK discounts.