jev-codex-router vs Poolside AI

Side-by-side comparison of features, pricing, and ratings

Analysis reviewed Live tool data as of 2026-09-24
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At a glance

Dimensionjev-codex-routerPoolside AI
PricingFree (MIT, self-hosted)Contact sales — no published tier
DeliveryGitHub monorepo + local server, installs into CodexOpen-weight models + agentic platform, deployable on-prem/air-gapped
What it decidesPer-call model tier + thinking-effort depth across four Choice questionsModel choice, tool grouping, agent orchestration, project management
Models involvedCodex native tier ladder Luna → Terra → Sol → AstraLaguna XS 2.1 (33B/3B active, 256K ctx), Laguna S 2.1 (118B/8B active, 1M ctx)
GovernanceLocal decision log at ~/.codex/codex-router/jev-router-live.jsonl, never publishedRBAC for users and agents, audit trails, end-to-end trace observability
Failure behaviorFail-open to a safe fallback route; sentinel-file kill switch bypasses Jev instantlySandboxed agent execution for generated code
jev-codex-router
jev-codex-router

Open-source routing layer that picks a model and thinking depth for every Codex call, not once per session.

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Poolside AI
Poolside AI

Open-weight agentic coding models — Laguna XS 2.1 and Laguna S 2.1 — built for secure on-prem and air-gapped enterprise AI.

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Pricing
Free
Contact Sales
Plans
$0
—
Popularity
1 views
7.1k views
Skill Level
Advanced
Advanced
API Available
Platforms
CLIDesktop
DesktopCLIAPIWeb
Categories
🚦 LLM Gateways & Model Routers💻 Code & Development🛠️ Autonomous Coding Agents⚙️ Developer Infrastructure
💻 Code & Development🛠️ Autonomous Coding Agents⚛️ Foundation Models & LLM APIs🛡️ AI Governance & Guardrails
Features
Per-per-call model selection for Codex, not per-session
Reasoning and thinking-effort depth chosen together with the model
Routing applied to continuations after tool calls, not just the first turn
Four Choice questions per request: Astra policy, capability tier, effort depth, route lease
Native tier ladder Luna → Terra → Sol → Astra with routing priors per tier
Mandatory Astra policy for architecture, independent final code review and risk-focused review
Route leases bounded to one_call, tool_chain or user_turn
Responses in, Responses out — SSE stream relayed verbatim, no format conversion
Two independent projections: Jev sees bounded decision state, the model gets the full canonical replay
Embedded router exempts the jev/auto route from conversation windowing and tool-result aging
Fail-open — any Jev error keeps the turn alive on a safe fallback route
Sentinel-file kill switch that routes without Jev instantly
Quota fallback activated only on observed native quota exhaustion, retrying only when a distinct candidate exists
Local decision log per routed turn at ~/.codex/codex-router/jev-router-live.jsonl, never published
Embedded maintained Codex Router fork under router/ — no submodule or hidden source clone
Laguna S 2.1 open-weight model: 118B params, 8B active, 1M context
Laguna XS 2.1 open-weight model: 33B params, 3B active, 256K context
Laguna XS 2.1 designed to run on-device for lightweight scenarios
Laguna S 2.1 positioned for frontier-class long-horizon reasoning
Single-agent and multi-agent orchestration with planning and tool use
Sandboxed agent execution environments for running generated code safely
Desktop app and CLI for agentic coding sessions
Model selection, tool grouping, and project management in the client
Data connectors to repositories, databases, and warehouses
Role-based access control for both human users and agents
End-to-end trace observability for agent runs
Governance and auditability built for regulated environments
Custom model fine-tuning on domain-specific data
Deployment inside your security boundary: on-prem, VPC, or workstation
Access to Laguna models via OpenRouter and Vercel AI Gateway
Integrations
Codex
LiteLLM
OpenRouter
Vercel AI Gateway

Feature-by-feature

Poolside's capability story is the models themselves: Laguna XS 2.1 (33B params, 3B active, 256K context) built to run on-device for lightweight work, and Laguna S 2.1 (118B params, 8B active, 1M context) aimed at frontier-class long-horizon reasoning. The sparse-active design is the efficiency argument. Around them sits the Poolside Platform: single- and multi-agent orchestration with planning and tool use, sandboxed execution for generated code, a desktop app and CLI for agentic sessions, model selection and tool grouping in the client, connectors to repos, databases and warehouses, RBAC for both humans and agents, and end-to-end trace observability. OpenRouter and Vercel AI Gateway are the listed integrations.

jev-codex-router solves a different problem entirely: it sits between Codex and the backend and picks model plus thinking-effort depth per call, not per session, via four Choice questions (Astra policy, capability tier, effort depth, route lease). Routing continues across tool-call continuations, not just turn one, and the mandatory Astra policy handles architecture and risk review. Responses pass through as a verbatim SSE relay — no format conversion. Two projections keep the decision state and the model's canonical replay separate; failures fail open, a sentinel file kills Jev instantly, and quota fallback only triggers on observed native quota exhaustion. Decisions are logged locally and never published. It integrates with Codex and LiteLLM. One is a model-and-governance stack; the other is a cost-routing middleware layer.

Pricing compared

Poolside is pricing_type: contact — there is no published per-seat number, no self-serve free trial, and no instant sign-up. The page explicitly flags that buyers needing a publicly listed per-seat price before budget approval are not a fit. That means the real cost isn't just license fees: it's procurement time, security review, and whatever infrastructure you run the open weights on, whether that's on-prem, air-gapped, or multi-cloud. The trade is that you own and can fine-tune the weights instead of renting access. Worth noting from the news cycle: Bloomberg reported on 2026-08-22 that Nvidia will pay Poolside a $6B license, per an anonymous source — a signal of commercial validation, not a price you can plan against.

jev-codex-router is free. It's an MIT monorepo on GitHub (0xNatoshi/jev-codex-router) with an install.sh, markdown docs, and a local server component. The cost is your time: setup, maintenance, and comfort working from a README. There's no vendor SLA, no compliance certification, no published release notes, and no measured quota-savings number you can check before adopting — the project publishes a backtest protocol and limitations instead. If you were hoping to compare dollars, there's nothing to compare: one is a negotiated enterprise contract, the other is zero dollars plus engineering hours.

Who should pick which

  • Regulated enterprise engineering team (finance/healthcare/defense)
    Pick: Poolside AI

    Cannot send code to a third-party cloud; Poolside offers open weights, air-gapped deployment, RBAC, audit trails and trace observability.

  • Platform team owning its model stack
    Pick: Poolside AI

    Wants to inspect, fine-tune and own the weights — Laguna XS 2.1 (33B/3B active) and S 2.1 (118B/8B active) are open-weight, which the router does not offer.

  • Codex power user burning through quota
    Pick: jev-codex-router

    Per-turn model and effort routing across tool-call continuations targets quota efficiency; Poolside has no self-serve option at all.

  • Individual dev or small startup without procurement
    Pick: jev-codex-router

    Free MIT install with no sales call, versus a contact-only enterprise product with no published pricing tier.

  • Team wanting routing across tool-call continuations
    Pick: jev-codex-router

    Routes continuations after tool calls, not just the first turn — a per-call decision the per-session alternative doesn't make.

Frequently Asked Questions

Can I run Poolside models through jev-codex-router?

Nothing in either product's data describes that integration. The router lists Codex and LiteLLM; Poolside lists OpenRouter and Vercel AI Gateway. Treat it as unverified rather than assumed.

Does Poolside offer a free tier or trial I can test before procurement?

No published pricing tier and no self-serve sign-up are listed — it's a contact-sales product, and buyers needing a listed per-seat price before budget approval are explicitly not a fit.

What happens to my Codex turn if the router errors out?

It fails open: any Jev error keeps the turn alive on a safe fallback route. A sentinel-file kill switch routes without Jev instantly, and quota fallback only activates on observed native quota exhaustion, retrying only when a distinct candidate exists.

Is the router's decision data published anywhere?

No. Each routed turn writes a local decision log at ~/.codex/codex-router/jev-router-live.jsonl, and it is never published.

Do I have to clone extra repositories to install the router?

No — it ships as a self-contained MIT monorepo (0xNatoshi/jev-codex-router) with a maintained Codex Router fork embedded under router/, so no second checkout, submodule, or hidden source clone is needed.

Which model is right inside the Poolside family?

Laguna XS 2.1 (33B params, 3B active, 256K context) is designed to run on-device for lightweight scenarios; Laguna S 2.1 (118B params, 8B active, 1M context) is positioned for frontier-class long-horizon work.

Can the router pick thinking depth and speed mode per turn?

It picks reasoning and thinking-effort depth together with the model, but every route uses standard speed — manual per-turn speed-mode selection isn't offered.

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Last reviewed: September 24, 2026