Experiential Labs vs Moxie Docs: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Experiential Labs and Moxie Docs — 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.
Moxie Docs
jackalope.digital
Moxie Docs indexes your GitHub repos, generates convention-grounded docs, detects doc drift on every merge, and serves an MCP for coding agents.
Key features
- Merge-triggered Re-indexing: Every merge triggers a fresh index pass so documentation stays synchronized with the current state of the repo.
- Grounded Convention Docs: Generates architecture and convention documentation grounded in the actual source, with deep symbol and import-graph analysis for TypeScript, JavaScript, and Python.
- Documentation Drift Detection: Flags pages affected by code changes and regenerates stale documentation with cited diffs so reviewers see what changed and why.
- Friday Cleanup PRs: Pro and Team plans automatically open weekly docs-only pull requests so keeping docs current becomes a bounded review, not a rewrite project.
- MCP Context Server: Exposes an MCP server so Cursor, Claude Code, and Codex pull verified conventions from Moxie instead of re-crawling the repo each session.
- Free Browser-based Doc Utilities: README, AGENTS.md, ADR, .cursorrules, CLAUDE.md, .windsurfrules, Mermaid, SQL-to-ER, and llms.txt generators run in the browser with no account.
- Scoped GitHub App: Access is scoped to the repos you select, tokens are encrypted server-side, and code is used only to generate documentation and MCP context.
- Broad Language Support: Documentation and search work on any GitHub repo, with recognition for TypeScript, JavaScript, Python, Go, Rust, Ruby, Java, PHP, SQL, Svelte, Vue, Kotlin, Swift, Elixir, and Zig.
Best for
- Automating living docs: Point Moxie at a private repo so architecture and convention docs stay grounded in the current code without manual rewrites.
- Feeding Cursor and Claude Code: Connect the MCP server so coding agents pull verified conventions from Moxie instead of re-crawling the codebase.
- Onboarding new engineers: Give a new hire a searchable, always-fresh guide to the codebase with symbol and import-graph context.
- Catching doc drift on merge: Fail loudly when a merged PR leaves documentation stale, with cited diffs pointing to what needs to change.
- Weekly docs cleanup review: Merge the Friday Cleanup PR each week to keep documentation current as a bounded, review-only workflow.
- One-off doc generation: Use the free browser-based generators to draft an AGENTS.md, CLAUDE.md, or llms.txt without signing up.
- SQL data-model diagrams: Paste CREATE TABLE SQL to get a live Mermaid ER diagram and optional AI data-model docs.
- Cursor Rules authoring: Draft a structured .cursorrules file with live preview and shareable export.