# Experiential

> An open source AI gateway that routes requests to every major model provider through one OpenAI-compatible endpoint at provider list price with zero token markup.

Experiential is an open source AI gateway built by Experiential Labs, a Y Combinator-backed company, that lets teams access every major AI model through a single OpenAI-compatible endpoint. The gateway is written in Python (with a compiled native data plane), licensed under Apache 2.0, and available both as a self-hosted deployment and a managed hosted service at api.experientiallabs.ai/v1.

## What It Is

Experiential sits in front of hosted providers (OpenAI, Anthropic, Google, Meta, Mistral, DeepSeek, and others), bring-your-own-key (BYOK) connections, and self-hosted local models, routing every request through one `/v1` endpoint at the provider's list price with 0% token markup. On top of the routing layer sits an intelligence layer that watches traffic patterns and surfaces model-switching recommendations, caching opportunities, and — for high-volume workloads — a fine-tuned model trained on the customer's own traffic and served behind the same endpoint.

## How the Gateway Works

The core routing model is straightforward: change the base URL to `api.experientiallabs.ai/v1`, keep existing OpenAI SDK code, and every model answers from one key. Key capabilities include:

- **Provider failover** — automatic fallback across providers and pooled accounts on capacity errors
- **Hard budget caps** — per-key spending limits enforced on every request, by day, week, or month
- **Model allowlists** — restrict which models a given key, agent, or team can call
- **Zero Data Retention routing** — policy-enforced routing only to providers under a ZDR agreement
- **Full attribution** — every token logged by agent, person, model, and day with live request logs showing route, cost, and TTFT

The gateway can be started locally with `pip install experiential && exp`, or pointed at the hosted platform with no infrastructure to manage.

## The Intelligence Layer

Above the gateway, Experiential's intelligence layer adds three optimization paths described on the product site:

1. **Model suggestions** — traffic monitoring surfaces when a model switch would improve quality, speed, or cost, including new models on the day they ship
2. **Caching** — the system identifies cache-hit opportunities and shows projected savings; repeated tokens return at 90% off when enabled
3. **Custom model ownership** — for workloads that repeat at volume, Experiential can fine-tune an open source model on the customer's own traffic, validate it in closed-loop simulation, and serve it behind the same endpoint

The company publishes case studies on its website documenting measured results from this fine-tuning approach, including a computer-use agent and a claims-research workload.

## Open Source Deployment Model

The gateway core is published at github.com/experientiallabs/experiential under the Apache License 2.0. The repository had approximately 4,485 stars and 165 forks as of the latest GitHub metadata. Self-hosting runs the full OpenAI-compatible `/v1` endpoint, key management, budgets, provider waterfalls, and the usage API. The `uvx --from experiential exp run` command starts it on any infrastructure. Telemetry is anonymous aggregate data via PostHog and can be disabled with `exp config telemetry disable`.

## Update: v0.7.67

The latest release as of the GitHub metadata is **v0.7.67**, published on 2026-09-11. The repository was created in June 2026 and has seen active development, with the last push also on 2026-09-11. The project direction signals continued investment in the intelligence layer (model optimization, caching, custom fine-tuning) alongside the open source gateway core.

## Features
- OpenAI-compatible /v1 endpoint
- Zero token markup routing
- BYOK (bring your own provider keys)
- Local and self-hosted model support
- Provider failover and resilience
- Hard budget caps per key, agent, or team
- Model allowlists per key or agent
- Per-agent and per-person usage attribution
- Live request logs with route, cost, and TTFT
- Zero Data Retention routing policy
- Prompt caching with 90% off repeated tokens
- Per-prompt model optimization suggestions
- Custom fine-tuned model training on your traffic
- Usage API for external reporting
- API key management dashboard
- Self-hosting via pip install
- Org-wide policies and RBAC (Enterprise)
- SSO: SAML and SCIM (Enterprise)
- Private networking and data residency (Enterprise)
- SOC 1 compliance

## Integrations
OpenAI, Anthropic, Google Gemini, Meta (Llama), Qwen, GLM (Zhipu), Kimi, DeepSeek, Mistral, AWS Bedrock, Azure AI Foundry, Fireworks, Modal, Vertex AI, OpenRouter, DoubleWord, Tencent Cloud, Claude Code, Cursor, Codex, Aider, OpenTelemetry

## Platforms
MACOS, WEB, API, CLI

## Pricing
Open Source, Free tier available

## Version
v0.7.67

## Links
- Website: https://www.experientiallabs.ai
- Documentation: https://platform.experientiallabs.ai/docs
- Repository: https://github.com/experientiallabs/experiential
- EveryDev.ai: https://www.everydev.ai/tools/experiential