Poolside AI
Poolside builds open-weight agentic coding models — Laguna XS 2.1 (33B) and Laguna S 2.1 (118B) — for code that stays inside your security
If your code legally cannot leave your network but you still want agents doing real refactors, Poolside is one of the few credible answers — open weights you can inspect and fine-tune, a 1M-context 118B flagship, and a runtime with sandboxing, RBAC and traces. The gateway path through OpenRouter or Vercel means you don't have to stand up vendor infrastructure to try the Laguna weights. Individual developers and small teams fine with hosted autocomplete should look elsewhere and save themselves
Verified 7h ago · liveness 69/100 · cite: rightaichoice.com/tools/poolside-ai
- Regulated engineering teams in finance, healthcare, or defense that cannot send source code to a third-party cloud
- Enterprises on air-gapped or multi-cloud networks that need agentic coding inside the perimeter
- Organizations that want to inspect, fine-tune, and own the weights of their coding model
- Teams running long-horizon, multi-step refactors that benefit from a 1M-context model and sandboxed agents
- Individual developers or small startups with no enterprise procurement process
- Teams that only need fast autocomplete and are fine sending code to a hosted SaaS assistant
- Quick throwaway prototyping where governance and sandboxing are pure overhead
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Skip Poolside if your team just wants fast in-IDE autocomplete and is comfortable sending source code to a hosted SaaS assistant — the governance runtime, sandboxing and trace overhead here are built for regulated environments, not for that workflow.
Running Laguna S 2.1 (118B params) yourself means paying for the GPU capacity to serve it — the license is open-weight, the inference bill is not.
Poolside has not published first-party pricing, so there is no list rate to compare against. The company's posture is enterprise: the reported Nvidia license deal (Bloomberg, 2026-08-22, single anonymous source) is a $6B figure, which signals an enterprise-scale commercial motion rather than indie-developer economics. Compare against hosted assistants with published per-seat rates if you need an approved line item today; compare against self-hosting other open-weight coding models if you mainly
In short
Poolside AI — Poolside builds open-weight agentic coding models — Laguna XS 2.1 (33B) and Laguna S 2.1 (118B) — for code that stays inside your security. Best for Regulated engineering teams in finance, healthcare, or defense that cannot send source code to a third-party cloud, Enterprises on air-gapped or multi-cloud networks that need agentic coding inside the perimeter, Organizations that want to inspect, fine-tune, and own the weights of their coding model. Contact Sales pricing.
What's new in Poolside AI
Checked 7 days agoAcross the latest 2 updates: 2 launches.
What people actually say about Poolside AI — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
40 mentions across 4 sources (Hacker News, YouTube, Bluesky, Lemmy) · researched Jul 17, 2026.
Average across the 4 sources that answered — each source counts once, not each post.
- +Open-weight models with strong SWE-bench scores (72.5%).
- +On-prem, VPC, and air-gapped deployment for high security.
- +256K context length supports long-horizon reasoning tasks.
- +Multi-agent orchestration with sandboxed execution environments.
- +Executive-grade auditability and role-based access control.
- −Community feedback is scarce; limited real-world user reviews.
- −Platform is still in research preview as of April 2026.
- −Pricing is opaque; only 'contact us' with no published tiers.
- −Trademark dispute with Poolside FM creates name confusion.
- −Learning curve is steep for advanced agent orchestration features.
- • Infrastructure for on-prem deployment likely requires significant hardware investment
- • Consulting fees for Forward Deployed Research Engineers are not transparent
- • No free tier or trial means entry cost is unknown
Viability Score
How well maintained and how widely used is Poolside AI? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- 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 sized small enough to run on-device
- Laguna S 2.1 positioned for frontier-class reasoning at mid-size cost
- Single-agent and multi-agent orchestration for coding tasks
- Sandboxed execution environments so agent code stays off production
- Desktop app for agentic coding sessions with model selection and tool grouping
- CLI agent client with slash commands and keyboard shortcuts
- VS Code and Visual Studio extensions
- Always-ask approval mode before edits are accepted
- Data connectors to repositories, databases, and warehouses
- Role-based access control for human users and agents
- End-to-end trace observability for agent runs
- Projects and multi-conversation management inside the client
- Custom fine-tuning and Forward Deployed Research Engineers
About Poolside AI
Poolside is an open-weight model shop for teams that do agentic coding under real constraints. Its two current Laguna releases cover opposite ends of the same job: Laguna S 2.1, out 2026-07-21, is the 118B-param flagship with 8B active and a 1M context, pitched as frontier-class reasoning at mid-size cost. Laguna XS 2.1, out 2026-07-02, is the 33B-param / 3B-active / 256K-context lightweight, small enough to run on-device for lighter work. The efficiency argument is the sparse activation: you pay for 8B or 3B active parameters per token rather than running a dense model. Around the weights sits the runtime and client stack — single- and multi-agent orchestration, sandboxed execution so generated code never touches production, model selection and tool grouping in the client, always-ask approval before edits land, role-based access control for humans and agents, and end-to-end trace observability. Developers work through a desktop app, CLI, and VS Code or Visual Studio extensions. If you don't want vendor infrastructure, the same Laguna weights are reachable through OpenRouter or the Vercel AI Gateway. Poolside also runs custom fine-tuning and embeds Forward Deployed Research Engineers with customer teams. The audience is deliberately narrow: regulated engineering orgs in finance, healthcare, and defense, plus anyone on air-gapped or multi-cloud networks where source code cannot leave the perimeter. Open weights let those buyers inspect a checkpoint, fine-tune on their own repositories, and show an auditor what produced a change — which is why the company's public research cadence, including the six-part Model Factory series and its writing on reward-hacking mitigations, doubles as sales material for technical evaluators.
Behind the Verdict
Pick Poolside when the constraint is the point. Regulated engineering teams — finance, healthcare, defense — and anyone on air-gapped or multi-cloud networks get something rare here: agentic coding where the weights themselves are inspectable, so you can fine-tune on your own repos and hand an auditor a trace of what generated a change. The two current models split cleanly. Laguna S 2.1 (118B params, 8B active, 1M context, released 2026-07-21) is the one for long-horizon, multi-step refactors where holding a large codebase in view matters. Laguna XS 2.1 (33B / 3B active / 256K, released 2026-07-02) is the small sibling, sized to run on-device when you just need a fast agent on a laptop or an edge box. We'd reach for the sparse-activation design as the practical story: 8B active per token is why a mid-size model can be priced and served like a smaller one. The platform layer is what makes it defensible for enterprises — sandboxed execution for agent-generated code, RBAC covering both people and agents, always-ask approval before edits, and end-to-end traces. Connectors reach repositories, databases and warehouses, and the client work happens in a desktop app, CLI, or VS Code and Visual Studio extensions. Second path in, and it's a real one: if standing up vendor infrastructure is a non-starter, call the same Laguna weights through OpenRouter or the Vercel AI Gateway. Where it bites: this is not a tool you buy with a credit card. Deployments lean on custom fine-tuning and Forward Deployed Research Engineers embedded with your team, which means a procurement conversation rather than a signup — fine for an enterprise, friction for a two-person shop. Teams that only want fast autocomplete and are comfortable sending code to a hosted SaaS assistant are overpaying in
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Real-world workflow fit
Concrete scenarios for the personas Poolside AI actually fits — and what changes day-one when you adopt it.
Deploying Laguna S 2.1 inside the bank's VPC, wiring Poolside's data connectors to the internal Git server and warehouse, and configuring role-based access control so agents inherit the same permission boundaries as the engineers who invoke them.
Outcome: Agents can plan and execute multi-step refactors against internal repositories while every action lands in an end-to-end trace the security review board can inspect.
Calling Laguna S 2.1 through OpenRouter or the Vercel AI Gateway first, comparing it against the current assistant on a real repo, then fine-tuning on the team's own repositories before moving to self-hosted deployment.
Outcome: The team validates model quality against its own codebase before committing infrastructure budget to on-prem serving.
Running Laguna XS 2.1 on a workstation — 33B params with 3B active and a 256K context — and driving it from the Desktop or CLI client with always-ask approval turned on.
Outcome: Agentic coding assistance is available in an environment with no external network path, with every proposed edit gated on human approval before it is applied.
Use Cases
- Deploy custom code-generation agents inside your VPC to automate software development workflows.
- Use multi-agent orchestration to plan and execute complex engineering tasks across heterogeneous environments.
- Audit all AI actions with full traces for compliance with CISO and enterprise review board requirements.
- Embed research engineers with your team to co-design AI solutions for specific engineering challenges.
- Fine-tune foundation models on your private repositories and corpora for context-aware code assistance.
- Run agents in air-gapped defense environments with workstation deployment.
- Evaluate the Laguna weights through OpenRouter or Vercel AI Gateway before standing up your own infrastructure.
Models Under the Hood
as of 2026-10-17
Limitations
- Laguna S 2.1 (118B params, 8B active, 1M context) and Laguna XS 2.1 (33B params, 3B active, 256K context) are open-weight agentic coding models, with XS 2.1 sized to run on-device.
- Access is offered via OpenRouter and Vercel AI Gateway, alongside a macOS desktop assistant, CLI client, and VS Code/Visual Studio extensions.
- The profile lists connectors, RBAC, trace observability, and governance/auditability aimed at regulated environments, which is overhead if you only want IDE autocomplete.
as of 2026-10-01
Verification history
We have re-verified Poolside AI 44 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 44 verification passes.
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where Poolside AI's pricing actually pencils out — and where peers do it cheaper.
Poolside has not published first-party pricing, so there is no list rate to compare against. The company's posture is enterprise: the reported Nvidia license deal (Bloomberg, 2026-08-22, single anonymous source) is a $6B figure, which signals an enterprise-scale commercial motion rather than indie-developer economics. Compare against hosted assistants with published per-seat rates if you need an approved line item today; compare against self-hosting other open-weight coding models if you mainly
Setup time & first value
How long it actually takes to get something useful out of Poolside AI — broken out by persona, not the marketing-page minute.
For a developer evaluating the models: minutes — open OpenRouter or Vercel AI Gateway and point at Laguna S 2.1 or XS 2.1. For a team running them in a client: the Desktop or CLI client gets you to a first agentic session quickly, but wiring connectors, RBAC and sandbox policy to a real repository is a project measured in days to weeks. Air-gapped workstation deployment with Laguna XS 2.1 is the
Switching to or from Poolside AI
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a hosted SaaS coding assistant: move agentic coding to Laguna models served inside your VPC, keeping source code inside the perimeter.
- →From a general-purpose frontier model API: switch to Laguna S 2.1 (1M context) or XS 2.1 (256K context) for code-specific work via OpenRouter or Vercel AI Gateway.
- →From self-hosted open-weight checkpoints (Laguna M.1 / XS.2 or other families): adopt the newer Laguna S 2.1 or XS 2.1 weights and Poolside's runtime for orchestration.
- →From ad-hoc scripting against an LLM endpoint: move to Poolside's sandboxed execution and trace observability so agent runs are auditable.
- ↗To a hosted coding assistant: simplest if your security policy permits sending source code to a third-party cloud, and if you do not need sandboxing or audit traces.
- ↗To another open-weight coding model: possible because Laguna weights are open, but you lose the Poolside Platform's orchestration, RBAC and end-to-end trace layer.
- ↗To a general-purpose frontier model API: viable for teams whose work is not code-specific; you would give up the 1M-context code-tuned configuration and the on-device XS 2.1 option.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Poolside AI”, and we withheld 6: 6 could not be judged, because “Poolside AI” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Poolside AI.
Official links
Tools that pair well with Poolside AI
Common stack mates teams adopt alongside Poolside AI, with the specific reason each pairing earns its keep.
Poolside
Open-weight agentic coding models — Laguna XS 2.1 at 33B on-device and Laguna S 2.1 at 118B for long-horizon agent work.
Zhipu GLM
Zhipu GLM is a full-stack large-model platform with open-weights GLM-5.3 coding models, MaaS APIs and autonomous agents.
Endor Labs
Agentic application security that governs AI coding agents and verifies reachable vulnerabilities before code ships
Featured Head-to-Head Comparisons
Recall vs Poolside Ai
Choose Recall if you're an individual Claude Code user who wants free, offline session memory to reduce token waste. Choose Poolside AI if you're an enterprise in a regulated industry needing custom foundation models, long-horizon multi-agent planning, and air-gapped deployment with full governance.
Qoder vs Poolside Ai
Qoder is better for developers who want autonomous multi-agent coding on large codebases with a freemium entry point. Poolside AI is superior for regulated enterprises that need custom, open-weight models deployed within strict security perimeters and require full governance. Choose based on your deployment needs and budget.
Guard Skills vs Poolside Ai
If you're building high-stakes software in finance or defense and need custom models deployed inside a VPC with enterprise governance, Poolside AI is the only option. But for most teams using AI coding agents today, guard-skills is a no-brainer: free, open-source, and instantly catches AI-specific mistakes in code, tests, and docs. Start with guard-skills; graduate to Poolside when compliance demands it.
Fanbox vs Poolside Ai
Poolside AI is built for enterprises needing secure, custom AI agents in regulated environments, while fanbox is a free, individual-focused tool for rapid, visual coding on Mac. Unless you have enterprise requirements and budget, fanbox offers immediate, no-cost value for solo developers on Apple Silicon. Choose Poolside only if you need on-prem deployment, governance, and multi-step agent capabilities.
Windows Copilot Api vs Poolside Ai
Choose Windows-Copilot-API if you need a free, self-hosted API for prototyping with GPT-4/5 and can accept no uptime guarantees. Choose Poolside AI if you are an enterprise in a regulated industry that requires on-prem deployment, long context (256K), multi-agent orchestration, and full governance. They serve completely different needs.
Testsprite Cli vs Poolside Ai
Poolside AI and TestSprite CLI solve entirely different problems. Choose Poolside if you need a secure, custom foundation model for code generation in regulated, air-gapped environments. Choose TestSprite if you're an AI-native team needing an autonomous test suite that grows as your app evolves and feeds failure diagnostics back to your coding agents.
Valmis vs Poolside Ai
Valmis is a strong choice for privacy-conscious teams and open-source enthusiasts who want a free, customizable AI coding assistant without vendor lock-in. Poolside AI, on the other hand, is purpose-built for enterprise-grade, high-consequence software engineering in regulated industries, offering on-prem deployment, multi-agent orchestration, and governance. If you need a zero-cost, self-hosted tool with flexibility, pick Valmis; if your organization requires compliance, auditability, and robust model performance for critical tasks, Poolside AI is the clear winner.
Freebuff vs Poolside Ai
For individual developers looking for a free, ad-supported coding agent, Freebuff is the clear choice with no API key needed and multiple frontier models. However, for enterprises in regulated industries requiring on-prem deployment, 256K context, and full governance, Poolside AI is the only viable option despite its undisclosed pricing and enterprise-only access.
Formkit vs Poolside Ai
Formkit and Poolside AI serve completely different purposes. Formkit is a specialized React form framework optimized for AI agents to generate structured forms with minimal overhead—ideal if your team builds complex React UIs. Poolside AI, with its Laguna models and enterprise platform, targets high-stakes software development in regulated industries where security and governance are paramount. Choose Formkit for forms, Poolside for mission-critical code generation.
Deepseek Reasonix vs Poolside Ai
Pick DeepSeek Reasonix if you're a solo dev or small team wanting a cheap, terminal-based agent optimized for DeepSeek's cache. Choose Poolside AI if you're an enterprise needing auditable, on-premise agents for high-stakes development. For most individuals, Reasonix's cost advantage is unbeatable; for regulated orgs, Poolside's governance is essential.
Value For Fable vs Poolside Ai
Choose Value-for-Fable if you're a cost-conscious developer or small team wanting Opus-like quality at Sonnet prices, and you can self-host under AGPL-3.0. Choose Poolside AI if you're an enterprise in a regulated industry needing custom, secure, on-prem foundation models with multi-agent orchestration and extensive governance—and you have the budget and willingness to engage in a sales process.
Godot Mcp vs Poolside Ai
Godot-MCP is the obvious choice if you're a Godot developer wanting free, lightweight AI integration for rapid game prototyping — it's open-source and works with your favorite AI clients. Poolside AI, on the other hand, is for large enterprises in regulated industries that need custom foundation models deployed inside their own security boundaries with full auditability. Unless you're building high-consequence software and have an enterprise budget, start with Godot-MCP.
Alternatives to Poolside AI
View all
Poolside
Open-weight agentic coding models — Laguna XS 2.1 at 33B on-device and Laguna S 2.1 at 118B for long-horizon agent work.
Zhipu GLM
Zhipu GLM is a full-stack large-model platform with open-weights GLM-5.3 coding models, MaaS APIs and autonomous agents.
Endor Labs
Agentic application security that governs AI coding agents and verifies reachable vulnerabilities before code ships
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