GitHub Copilot vs Poolside

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	GitHub Copilot Coding	Poolside Coding
Tagline	The original AI pair programmer, now with chat and agents.	Frontier open-weight foundation models purpose-built for agentic software engineering.
Category	Coding	Coding
Pricing	Paid· Free: $0 · Pro: $10 · Pro+: $39 · Max: $100	Enterprise· No public price list. Direct enterprise/government engagements are contact-sales; the Laguna 2.1 open-weight models can also be consumed pay-per-token via third-party gateways (OpenRouter, Vercel AI Gateway) at their published rates.
Model	GPT / Claude / OpenAI o-series (configurable)	Poolside Laguna 2.1 (in-house) — Laguna S 118B/8B-active MoE and Laguna XS 33B/3B-active MoE
Editorial score	9.1 / 10	—
Use cases	autocompletechatPR reviewagents	Self-hosted coding agents inside enterprise VPCsAir-gapped / on-prem code generation for defense and governmentLarge-context refactors across monoreposLong-horizon autonomous PR generationPrivate fine-tuning on proprietary codebasesSwap-in coding model for existing IDE or CLI agentsModel routing via OpenRouter or Vercel AI GatewayRLCE-based continual improvement from execution traces
Pros	Excellent JetBrains + VS Code support Tight GitHub PR integration Now offers multiple model choices Free tier for individuals	Open-weight release means you can self-host, quantise, or fine-tune Laguna S/XS without vendor lock-in. MoE design (8B or 3B active) delivers frontier-tier reasoning while keeping inference cost closer to mid-tier models. 1M / 256K context windows are large enough to reason over sizeable monorepo slices in a single call. Explicit on-device and air-gapped deployment story fits regulated, government, and defense buyers. Available through OpenRouter and Vercel AI Gateway, so teams can try the models without an enterprise contract. Training pipeline built around reinforcement learning from code execution, which is a strong signal for coding-agent quality. Model + agent runtime + training stack are offered together, so enterprise customers can keep improving a private model with their own execution traces.
Cons	UX less integrated than Cursor Multi-file edits are catching up but not yet leading	No public pricing and no obvious self-serve signup for the enterprise platform — direct access is sales-led. Not a turnkey IDE assistant; you supply the agent scaffolding, editor integration, and evaluation harness. Ecosystem, community, and third-party benchmark coverage are still thin compared with GPT, Claude, Gemini, and Qwen-Coder. Open-weight, not fully open-source — training data, RLCE recipe, and code are not published. Very large parameter counts mean serious GPU capacity is required to self-host Laguna S at useful throughput. Product surface (docs, dashboards, tooling) is younger and less polished than incumbent coding-model vendors.
Website	github.com	poolside.ai

Pick GitHub Copilot if

✅ Excellent JetBrains + VS Code support
✅ Tight GitHub PR integration
✅ Now offers multiple model choices
✅ Free tier for individuals

Pick Poolside if

✅ Open-weight release means you can self-host, quantise, or fine-tune Laguna S/XS without vendor lock-in.
✅ MoE design (8B or 3B active) delivers frontier-tier reasoning while keeping inference cost closer to mid-tier models.
✅ 1M / 256K context windows are large enough to reason over sizeable monorepo slices in a single call.
✅ Explicit on-device and air-gapped deployment story fits regulated, government, and defense buyers.

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