Poolside AI
Open-weight agentic coding models — Laguna XS 2.1 (33B) and Laguna S 2.1 (118B) — built for code that cannot leave your security boundary.
Poolside is one of the few credible options if your code legally cannot leave your network but you still want modern agentic coding. Open weights, a 118B-param / 8B-active flagship with a 1M context (Laguna S 2.1), a small on-device sibling (Laguna XS 2.1, 33B / 3B active / 256K), and a governance runtime with sandboxing, RBAC and end-to-end traces are a genuinely rare combination. The second path in is real too: OpenRouter and Vercel AI Gateway let you call the same Laguna weights without standing up vendor infrastructure. For an organization that can run its own checkpoints or is happy with gateway access, that is a strong position. For an individual developer comparing against hosted
Verified 7d ago · liveness 69/100 · cite: rightaichoice.com/tools/poolside-ai
- Regulated engineering teams in finance, healthcare, or defense that cannot send code to a third-party cloud
- Enterprises running air-gapped or multi-cloud infrastructure that need agentic coding inside the perimeter
- Organizations that want to inspect, fine-tune, and own the weights of their coding model
- Teams with long-horizon, multi-step refactors that benefit from a 1M-context model and sandboxed agents
- Individual developers or small startups without an enterprise procurement process
- Teams that only need fast autocomplete and are fine sending code to a hosted SaaS assistant
- Projects wanting quick throwaway prototyping without governance 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 — Open-weight agentic coding models — Laguna XS 2.1 (33B) and Laguna S 2.1 (118B) — built for code that cannot leave your security boundary. Best for Regulated engineering teams in finance, healthcare, or defense that cannot send code to a third-party cloud, Enterprises running air-gapped or multi-cloud infrastructure 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 3 updates: 3 launches.
Introducing Laguna S 2.1
Poolside releases Laguna S 2.1, a frontier-class open-weight model with 118B params, 8B active and a 1M context window, aimed at longer-horizon agentic coding work.
Introducing Laguna XS 2.1
An upgrade to XS.2 with 33B params and 3B active, designed to run on-device as Poolside's lightest and fastest agentic coding model, with a 256K context.
AI, your way: introducing the Poolside Platform
Poolside introduces its platform for production-grade AI agents with auditability and governance inside a security boundary, including orchestration, sandboxed execution, RBAC and trace observability.
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 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
- VS Code and Visual Studio extensions (per Poolside blog)
- macOS desktop assistant (per Poolside blog)
- Model selection, tool grouping, and project management in the client
- Always-ask approval mode before edits are accepted
- 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
About Poolside AI
Poolside builds open-weight foundation models and the agentic systems around them, aimed at software work that has to happen inside a security perimeter. Two models anchor the family as of mid-2026: Laguna XS 2.1, released 2026-07-02, at 33B params with 3B active and a 256K context, small enough to run on-device; and Laguna S 2.1, released 2026-07-21, the newer 118B-param model with 8B active and a 1M context window, which Poolside positions as frontier-class reasoning at mid-size cost. Spending only 8B active parameters rather than running fully dense models is the efficiency story here. Around the models sits the Poolside Platform, introduced 2026-05-05 for production-grade AI agents with auditability and governance inside a security boundary: single- and multi-agent orchestration, sandboxed execution so agent code runs without touching production, role-based access control for both humans and agents, and end-to-end trace observability. Developers work through Desktop, CLI, and VS Code and Visual Studio extensions; the on-page demo shows model selection, tool grouping, connectors, projects, and an always-ask approval mode. Teams already embedded in an existing stack can reach the same models through OpenRouter or the Vercel AI Gateway rather than standing up vendor infrastructure, and Poolside offers custom fine-tuning plus Forward Deployed Research Engineers who work alongside your team. The buyer profile is narrow on purpose: regulated engineering organizations in finance, healthcare, and defense, or anyone running multi-cloud or air-gapped networks where source code cannot leave the perimeter. Open weights matter to that crowd — you can inspect the model, fine-tune it on your own repositories, and show auditors what generated a change. Poolside's public research cadence, including the six-part Model Factory series and its writing on reward-hacking mitigations, is part of the pitch to technical evaluators who want to see the method, not just the benchmarks. In August 2026, Bloomberg reported that Nvidia would pay Poolside $6B for a license, per an anonymous source — a signal of enterprise-scale backing rather than indie-developer economics.
Behind the Verdict
The interesting thing about Poolside in 2026 is that the company stopped selling a story and started shipping a stack. The model line is now concrete: Laguna XS 2.1 (2026-07-02) at 33B params, 3B active, 256K context, explicitly sized to run on-device; and Laguna S 2.1 (2026-07-21) at 118B params, 8B active, 1M context, aimed at long-horizon reasoning. The sparse-activation design is what makes those numbers usable — you are not paying dense-model inference costs to get a 1M-context coding model. Around the weights sits the Poolside Platform, launched 2026-05-05, which is where the enterprise argument actually lives: sandboxed execution so generated code never touches production, role-based access control applied to agents as well as people, and end-to-end trace observability so an auditor can reconstruct what an agent did and why. Multi-agent orchestration and planning are part of the same runtime, and the client surface spans Desktop, CLI, and VS Code and Visual Studio extensions, with connectors to repositories, databases and warehouses. Strengths. Open weights plus on-prem/VPC/workstation deployment is the combination regulated buyers ask for and rarely get at this quality level. The context ceiling on Laguna S 2.1 (1M) is large enough for whole-repo-scale refactors, and XS 2.1 gives you a genuinely local option for lighter work. Two escape hatches — OpenRouter and the Vercel AI Gateway — mean you can evaluate the models before committing to running them yourself. Custom fine-tuning on your own repositories and Forward Deployed Research Engineers working alongside your team address the two hardest parts of adoption: domain adaptation and getting a first agent into production. And the research cadence (the Model Factory series, the reward-hacking write-up) gives technical evaluators something to inspect besides a benchmark table. Weaknesses. The governance runtime is the product, and governance overhead is real — teams that just want fast tab-completion inside an IDE are paying for machinery they will not use. The open-weight family is young: the Laguna line reaches back only to the March 2026 release of M.1 and XS.2, and Poolside's own earlier framing described the family as a research preview. The company has not published first-party pricing, so cost conversations start with Poolside rather than with a rate card, and the reported $6B Nvidia license (Bloomberg, 2026-08-22, single anonymous source) points at an enterprise-scale commercial posture. Treat that report as reported, not confirmed. If you need a per-seat list price before you can get budget approval, you will be doing that work yourself. Where it fits. Regulated engineering organizations in finance, healthcare and defense; multi-cloud and air-gapped environments; teams doing long, multi-step refactors where a 1M-context model with sandboxed execution and full traces is worth the governance tax. Where it does not. Individual developers, small teams without procurement, and
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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 2026-09-24
Limitations
- Laguna S 2.1 (118B params, 8B active, 1M context, released 2026-07-21) and Laguna XS 2.1 (33B params, 3B active, 256K context, released 2026-07-02) are open-weight agentic coding models positioned for on-prem, VPC, or workstation deployment.
- The client spans Desktop, CLI and more, with a macOS desktop assistant plus VS Code and Visual Studio extensions noted in Poolside's own blog.
- The site offers model access via OpenRouter and Vercel AI Gateway, and lists data connectors, RBAC, trace observability, and governance/auditability aimed at regulated environments.
- The Laguna family is young — Poolside's own material framed the earlier M.1 and XS.2 models as a research preview when they shipped in March 2026 — and the company has not published first-party pricing.
- Governance tooling (sandboxing, RBAC, traces) is overhead if you only want IDE autocomplete.
as of 2026-10-01
Verification history
We have re-verified Poolside AI 42 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-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-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
Showing the 6 most recent of 42 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.
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Open-weight agentic coding models you can self-host, from 33B on-device to 118B frontier-class.
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Continue
Open-source AI coding agent for VS Code and JetBrains, acquired by Cursor in January 2026 and now an unmaintained codebase you fork, not subscribe to.
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.
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.
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.
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.
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.
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.
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.
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.
Pi Coding Agent vs Poolside Ai
If you're an enterprise in finance or defense needing auditable, on-prem AI agents with custom models, Poolside AI is the clear choice—but it comes with a heavy price and vendor engagement. For individual developers who want full control over models and workflows in the terminal, Pi Coding Agent is free and infinitely extensible. Choose based on your need for governance vs. flexibility.
Alternatives to Poolside AI
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Poolside
Open-weight agentic coding models you can self-host, from 33B on-device to 118B frontier-class.
Cosine Genie
Cosine Genie is a coding AI built on production code, with CLI, cloud, and air-gapped deployment for maintainable software.
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