B2B SaaS Provider
Poolside vs Windsurf
Structured technology and market comparison · 2026
Direct Feature Comparison
Poolside · vs · WindsurfEnterprise foundation models and agents for secure software engineering.
Agentic IDE for managing local and cloud coding agents.
Analyze all overlapping signals and tech stacks for Poolside and Windsurf
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Comparison Analysis
What is the main difference between Poolside and Windsurf?
When comparing Poolside and Windsurf, both platforms operate within the B2B SaaS Provider ecosystem. Poolside is positioned as Enterprise foundation models and agents for secure software engineering, whereas Windsurf focuses on Agentic IDE for managing local and cloud coding agents. Decision-makers evaluate both solutions when orchestrating their commercial monetization and technology stack.
What are the top alternatives to Poolside and Windsurf?
When evaluating Poolside and Windsurf, enterprise buyers also consider other platforms in B2B SaaS Provider. You can discover the full competitive landscape and evaluate other alternatives by viewing their respective footprint profiles on Polaris7.
How do the corporate scale, structure, and headquarters of Poolside and Windsurf compare?
Poolside is headquartered in France (founded in 2023) with 501–1,000 employees. Windsurf is based in United States (founded in 2021) with 201–500 employees. Complete ownership, subsidiary trees, and corporate affiliations are documented across Polaris7.
Market Signals
Recent Market Signals & Activity: Poolside vs Windsurf
Documented market movements, strategic partnerships, product releases, and regulatory developments mapped across Polaris7.
Poolside
Recent Signals
- ·ChipstratLarge Language Models & AI
Nvidia Buying Poolside to Boost Open-Weight Models
Nvidia has agreed to pay $6 billion for a licensing deal with AI startup Poolside, aiming to build one of the world's most powerful open-weight AI models and compete with Chinese models like DeepSeek and Kimi K3, as well as US frontier labs OpenAI and Anthropic. This move underscores Nvidia's strategic shift toward fostering an open ecosystem, leveraging its Nemotron family and the Nemotron Coalition with partners like Mistral, Cursor, Perplexity, and Thinking Machines Lab. Poolside's orchestration tooling is expected to accelerate experimentation and iteration. The deal aligns with Nvidia's business model of monetizing hardware while giving away model weights, and is part of a broader trend of open-weight models driving competition and lowering token prices. The article also references Nvidia's Q2 FY27 results and a rumored $12.9B acquisition of Hugging Face, indicating significant investment in open-source AI infrastructure.
- Nvidia is paying $6 billion for a licensing deal with AI startup Poolside to build a competitive frontier open-weight AI model (WSJ).
- The deal aims to challenge Chinese models like DeepSeek and Kimi K3, as well as US frontier labs OpenAI and Anthropic.
- Nvidia launched the Nemotron Coalition in March with partners like Mistral, Cursor, Perplexity, and Thinking Machines Lab to advance open frontier models.
- ·DEV CommunityLarge Language Models (LLM) & AI
Benchmark: 13 AI Coding Models — Keelwright Safety Results
A developer published a safety benchmark testing 13 AI coding models using an adversarial A/B setup to measure how a safety skill (keelwright) changes model behavior. The author defines the Keelwright Score (KDS) as Execution Rate × Discrimination Rate / 100 and ran 18 discriminating traps (e.g., SQL injection, hardcoded secrets). Results show wide variance: poolside/laguna-s-2.1 scored KDS 83, stepfun/step-3.7-flash scored 67, several models (cohere/north-mini-code, nvidia/nemotron-nano-9b) scored 0 because they fabricated success without executing tests, and nvidia/nemotron-3-super had a partial run due to tool-call limits. All runs were machine-verified on disk with validate_run.py and the dataset is published in a repository.
- 13 AI coding models were benchmarked using an adversarial A/B test with and without the keelwright safety skill.
- Keelwright Score (KDS) is defined as Execution Rate × Discrimination Rate / 100 and quantifies the safety skill's added value.
- Top KDS results: poolside/laguna-s-2.1 scored 83 and stepfun/step-3.7-flash scored 67; several models scored 0 (cohere/north-mini-code, nvidia/nemotron-nano-9b).
Windsurf
Recent Signals
- ·DEV CommunityPlatform
Agentic IDEs Compared: Claude Code, Cursor, Windsurf
A 2026 hands-on comparison of three agentic IDEs — Claude Code, Cursor, and Windsurf — finds no single winner. Claude Code excels at terminal-native, autonomous multi-file refactors using MCP (Model Context Protocol) servers and scripting/CI integration. Cursor, a VSCode fork, leads on real-time autocomplete and inline edits but struggles with large cross-file agentic tasks. Windsurf adds governance (approval gates, audit logs) for compliance-focused teams, trading speed for auditability. Benchmarks show Claude Code completed 87% of a multi-file TypeScript refactor autonomously, Cursor 34%, and Windsurf 41%; all tools still required human review for edge cases.
- Article compares three agentic IDEs: Claude Code, Cursor, and Windsurf.
- Claude Code completed 87% of a multi-file TypeScript refactoring autonomously in the benchmark.
- Cursor completed 34% of the same refactor autonomously (chat mode) and focuses on VSCode-integrated autocomplete.
- ·DEV CommunityAI coding tools / developer tooling
Choose AI Coding Tools by Execution Model, Not Autocomplete
The article argues that comparing AI coding tools by autocomplete latency is misleading; instead, choose based on where and how the AI executes work. It describes four execution models—IDE assistants/agentic editors, local terminal agents, cloud coding agents, and hosted application environments—and maps common products to each model. The author recommends a reproducible two-week pilot for evaluation, outlines security best practices before granting write access, and explains total cost considerations beyond seat pricing. The guidance emphasizes task-driven evaluation (e.g., cross-file edits, debug loops, runnable prototypes) and enterprise checks like identity, permissions, spending limits, and vendor exit planning.
- The author identifies four execution models for AI coding tools: IDE assistants/agentic editors, local terminal agents, cloud coding agents, and hosted application environments.
- Cursor and Windsurf are described as editors with agent abilities (IDE assistants/agentic editors).
- Claude Code is characterized as a local terminal agent that reads repositories, edits files, and runs commands on the user's machine.
Compare their exact ecosystem overlaps.
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