Windsurf Fastest agentic IDE
The fastest agentic code editor at the lowest Pro price — VS Code fork with Cascade agent, SWE-1.5 speed, and unique Codemaps visual navigation.
The ownership saga — what actually happened
To understand where Windsurf stands in 2026, you need to understand the chain of events that got it here — because it's one of the most turbulent corporate stories in recent AI history.
Windsurf started life as Codeium, an AI coding assistant founded in 2021. By 2024 it had millions of users, a $65M Series B, and a growing enterprise business — $82M ARR with enterprise revenue doubling quarter-over-quarter. Codeium rebranded to Windsurf in late 2024, pivoting from "autocomplete extension" to "agentic IDE." The product bet was clear: VS Code fork, agent-first UX, Cursor's most credible rival.
Then the acquisition talks started. OpenAI reportedly entered negotiations to acquire Windsurf for roughly $3 billion. Those talks collapsed — the exact reasons were never made public, but the timeline aligns with OpenAI's increasing focus on building its own coding tools. Google separately secured a licensing deal for Windsurf's underlying technology, adding another layer of complexity to the ownership picture.
The deal that closed: in July 2025, Cognition AI acquired Windsurf. Cognition is the company behind Devin, the autonomous software engineer that made headlines in 2024. The price has been reported at approximately $250 million — the largest M&A deal in AI dev tools to date. The entire Windsurf team (around 210 employees) was acquired, with full vesting acceleration and financial participation in the deal for all employees.
Scott Wu, Cognition's CEO, framed it as a combination of two complementary bets: Windsurf's IDE expertise and go-to-market reach, plus Devin's autonomous agent capabilities. In practice, the integration has materialized through SWE-1.5 (Cognition's proprietary coding model, now the backbone of Windsurf), Codemaps (visual codebase navigation), and embedded Devin sessions accessible directly from the Windsurf editor.
Windsurf is now a Cognition product. Google has a separate technology license. The brand continues under Cognition ownership. The team is intact. These are facts — what's harder to assess is whether the multi-party IP situation creates future complications. We'll flag this honestly in the verdict.
What Windsurf actually is
Windsurf is a VS Code fork — like Cursor, it takes the VS Code codebase as its foundation, strips out Microsoft's proprietary telemetry layer, and rebuilds the editor internals around AI-first architecture. The fork decision exists for the same reason Cursor made it: deep editor-level integration (multi-file diffs, agentic execution, in-editor terminal orchestration) requires hooks that the VS Code extension API simply doesn't expose.
The product has three first-class surfaces:
- Cascade — the agentic pane that plans, executes, and stages multi-file edits with per-step review
- Supercomplete — next-action prediction that goes beyond tokens to predict developer intent
- Codemaps — AI-annotated visual maps of codebase structure, unique in the market
Windsurf also ships as a plugin for existing editors — VS Code, JetBrains IDEs, and Vim — via the Windsurf extension. The plugin exposes Cascade and Supercomplete but not Codemaps or the full agentic flow. If you want the complete experience, you want the dedicated editor. For JetBrains users unwilling to switch, the plugin is a credible entry point.
Models supported include every major frontier provider: Claude (Anthropic), GPT-4o and successors (OpenAI), Gemini (Google), plus Windsurf's own SWE-1.5 — a Cognition-built model optimized specifically for agentic coding tasks.
First five minutes in action
The download is around 115MB. Installation on macOS, Windows, and Linux follows the standard VS Code pattern — the import wizard asks if you want to pull in your existing VS Code configuration (themes, extensions, keybindings). Say yes. Everything transfers in one click.
Once the codebase is open, Windsurf indexes it silently using its Fast Context engine — a proprietary RAG approach with SWE-grep that retrieves relevant code reportedly up to 20x faster than traditional vector-search-based agentic context. You don't configure anything. It just happens in the background.
The first thing you notice is how clean the UI is. Cursor has accumulated feature badges, nested panels, and mode-switching complexity over successive releases. Windsurf feels more considered — fewer UI elements competing for attention, Cascade in a side pane that doesn't feel bolted on. The aesthetic difference is real and it's not superficial: a cleaner interface means faster flow for the user.
Hit Cmd+I to open Cascade. Type a natural-language task. Cascade starts by reading your codebase — files, imports, dependency graph — and produces a plan: which files it will touch, in what order, what each change does. You approve the plan. It executes, stages the diff. You review per-file, accept or reject changes, run your tests. Commit. That's the core loop.
Create a .windsurfrules file at your repo root before writing a single prompt. Windsurf reads it as a system prompt — your conventions, what to avoid, your team's patterns. Same mechanic as Cursor's .cursorrules. The quality difference between sessions with and without it is significant.
Cascade: the agent at the center
Cascade is the reason most developers choose Windsurf over extending an existing editor with an AI plugin. It is not a chat window that happens to suggest code. It is an autonomous agent that plans, executes, and self-corrects — with a human-in-the-loop approval step before anything lands on disk.
The mechanics: you describe a goal in natural language. Cascade queries the codebase with Fast Context to understand what's relevant, produces a step-by-step plan, and asks for approval before executing. Once approved, it writes code across however many files are needed, runs terminal commands (including tests, builds, and linting), and surfaces a reviewable diff. If a test fails mid-execution, Cascade can read the failure output and self-correct — trying a different approach without requiring you to restart the session.
The per-step approval model is the honest differentiator. A lot of "agentic" tools ship changes directly or require you to review a massive single diff at the end. Cascade's approach — plan first, execute in observable steps, review granularly — is the design that actually works when you're deploying to production code. You stay in control. The agent does the mechanical work. You do the judgment work.
Flows extend this further: structured agentic sequences that can be paused, resumed, and replayed across sessions. Where a single Cascade run handles a bounded task (add auth to this route), a Flow handles a multi-phase project (migrate this service from REST to GraphQL, phase by phase, with review gates between each phase). Flows are particularly useful for long-running changes where you want natural breakpoints for review before continuing.
The Agent Command Center — a Kanban-style dashboard introduced after the Cognition acquisition — lets you manage multiple Cascade sessions and Flows simultaneously. You can delegate three different tasks to three different Cascade agents, monitor their progress in real-time, and review results in parallel. For solo developers juggling frontend, backend, and infra changes simultaneously, this is a genuine multiplier.
Supercomplete: intent prediction, not token prediction
Supercomplete is Windsurf's answer to Cursor's Tab autocomplete — but the design philosophy is different. Cursor's Tab predicts your next edit based on what you just typed. Supercomplete predicts your next action based on a broader read of what you're doing: your recent changes, the file you're in, the function you're writing, and where your cursor is positioned.
In practice: you write a function signature. Supercomplete doesn't just autocomplete the function body — it understands what the function is supposed to do from context (the filename, the class it's in, the other methods around it, the tests that reference it) and generates a complete, contextually appropriate implementation. The prediction goes up the abstraction stack rather than completing at the token level.
It also predicts navigation. After writing a new function, Supercomplete predicts where you'll want to go next — the test file, the usage site, the type definition — and pre-fetches it. The "Tab to Jump" feature surfaces this as a navigational shortcut: press Tab to jump to the next predicted location in your workflow, not just the next token in your file.
Compared to Cursor's Tab, Supercomplete is more ambitious and occasionally less reliable. Cursor's Tab feels tighter and more consistent on mechanical changes (renaming, refactoring patterns, boilerplate). Supercomplete is more impressive on novel code — when you're writing something genuinely new and it correctly infers the intent from surrounding context.
SWE-1.5: the proprietary model advantage
The most consequential change from the Cognition acquisition is SWE-1.5, Cognition's proprietary coding model, released October 2025 and now the default for Cascade in Windsurf.
The headline claim: SWE-1.5 runs at 950 tokens per second — reportedly 13x faster than Claude Sonnet 4.5 and 6x faster than Haiku 4.5, while maintaining near-frontier quality on coding benchmarks (Scale AI's SWE-Bench Pro). The model was built by co-optimizing the architecture, inference stack, and agent harness together — rather than taking a general-purpose model and adding a system prompt.
What this means in practice: tasks that took 20 seconds with Sonnet complete in under 5 seconds with SWE-1.5. For interactive agentic sessions, that difference is enormous. The feedback loop between "I gave the agent a task" and "I'm reviewing the diff" goes from patience-required to near-instant. That changes how you use the tool — you delegate smaller, more frequent tasks instead of batching everything into one big prompt.
SWE-1.5 is not the right model for every task. For architecture discussions, complex reasoning about design trade-offs, or code in unfamiliar domains, the frontier models (Claude, GPT-4o, Gemini) still produce better output. SWE-1.5 is optimized for execution speed on well-scoped coding tasks — and for that use case, it's a genuine competitive advantage.
Codemaps: the feature nobody else has
Codemaps are AI-annotated visual maps of your codebase structure. Not a file tree. Not a dependency graph. A hierarchical, interactive visual that shows how components relate to each other — which functions call which, how data flows through the system, where the seams between modules actually are — with AI annotations that explain what each cluster of code does in plain English.
For onboarding to a new codebase, Codemaps shortcut the three-day "just read the code" phase. You can navigate from a high-level architectural view down to a specific function and understand the context without manually tracing call chains. The annotations stay up-to-date as you edit — Windsurf re-runs the analysis incrementally as the codebase changes.
For large monorepos, Codemaps are the killer feature of 2026. Neither Cursor, GitHub Copilot, nor Claude Code has shipped an equivalent. The closest alternatives are external tools like CodeSee or Sourcegraph, which don't have the IDE integration depth that makes Codemaps actually useful mid-session.
The limitation: Codemaps are generated at indexing time and can lag on very rapid-fire changes. In a session where you're making many small edits quickly, the map can briefly be stale. For a tool designed to aid understanding rather than assist with micro-edits, this is an acceptable trade-off.
MCP support and tool integrations
Windsurf supports the Model Context Protocol (MCP) natively within Cascade. You can connect any MCP-compatible server — GitHub, Postgres, Figma, Linear, Slack, custom internal APIs — and Cascade can call those tools as part of an agentic workflow. This means Cascade can query your database schema, open a GitHub issue, check a Figma design spec, and write the corresponding code, all within a single task execution.
The limit is 100 total tools active at a time, which is generous for most setups. Configuration is via a JSON file in your project root — similar to how you'd configure Claude Code's MCP connections. The tooling ecosystem is growing fast; as of mid-2026 there are hundreds of MCP servers available through the MCP registry.
Spaces (introduced post-acquisition) bundle an agent session, the related files, and the linked GitHub PR into a unified workspace. You can pause a Space, come back to it three days later, and pick up exactly where you left off — Cascade retains the context, the file state, and the tool connections. For developers working on multiple features in parallel, Spaces are an organizational tool that actually works.
Three real workflows, end-to-end
Migrate a REST API to typed endpoints with Zod
The task: take an existing Express API with 38 route files, no input validation, and ad-hoc TypeScript types, and migrate it to Zod schemas with runtime validation at every endpoint. A one-week estimate manually.
We opened Cascade, typed the task in plain English. Cascade's plan: generate a base schema directory, write a Zod schema for each route's expected input by reading the existing handler logic, add .parse() calls at each route entry point, update the TypeScript types to derive from the schemas, and add a shared error handler for Zod validation failures. 38 files, sequenced.
Execution with SWE-1.5 took about 4 minutes for the full pass. We reviewed file by file — the schemas were accurate for 35 of 38 routes. Three routes had ambiguous input shapes (they accepted two different request formats depending on a query param) that Cascade flagged with a comment: "ambiguous input shape — two variants detected, see lines 44 and 67. Recommend splitting into two endpoints." Not wrong. Good judgment call.
The error handler Cascade generated was production-grade — correct HTTP status codes, sanitized error messages for non-development environments, structured JSON response format. It read our existing error handling patterns and matched them.
Add real-time notifications using WebSockets
We asked Cascade to add real-time notifications to a Next.js app: user gets a toast when a task is assigned to them. Cascade identified that the app already used Server-Sent Events for a different feature, and proposed using that pattern rather than adding a WebSocket dependency — matching the codebase's existing approach. We didn't tell it about the SSE usage. It found it.
The feature shipped in 22 minutes. What impressed us: Cascade correctly handled the Next.js App Router's distinction between Server Components and Client Components, kept the SSE handler in /api/notifications/route.ts where the existing SSE handler lived, and wrote a reusable useNotifications() hook in the same style as the other hooks in /lib/hooks/. Style and pattern matching without instruction.
Understand an inherited monorepo in under an hour
The scenario: new contractor, handed a TypeScript monorepo with four packages (API, shared types, admin panel, background jobs). No documentation. Brief: "add a new billing tier."
First action: open Codemaps. Within 90 seconds, a visual map of the four packages appeared — showing which packages depend on which, where the shared type definitions live, and which modules the billing-related logic was concentrated in. The AI annotations identified the billing module immediately: "billing logic concentrated in /packages/api/src/billing/ — three service classes, two webhook handlers, one Prisma model."
Second action: open Cascade, ask "what would I need to change to add a new billing tier called Enterprise?" Cascade read the billing module (thanks to Fast Context having already indexed everything) and produced a precise plan: update the Prisma schema, update the billing service's plan validation, update the Stripe product mapping, update the admin panel's plan display. Four files, sequenced.
The contractor had a PR open by lunch. Without Codemaps, that timeline is two to three days of code reading minimum.
Performance, measured
Across 40 representative prompts on a mid-size Next.js + Express codebase (about 35k LOC), here's how Windsurf with SWE-1.5 stacks up on the metrics that matter:
bench --tool=all --metric=latency,acceptance,multi-file n=40 prompts
The headline finding: Windsurf with SWE-1.5 is nearly twice as fast on agentic tasks as Cursor. Acceptance rate is close — Cursor edges it by 4 points, within margin of error for different codebases and prompt styles. Multi-file success is within 4 points as well. The practical story: Cursor is marginally more accurate on average; Windsurf is dramatically faster. Which matters more depends on your workflow.
Windsurf vs Cursor
a/windsurf b/cursor
This is the comparison that matters for most developers in 2026. Both are VS Code forks. Both have agentic multi-file editing. Both support Claude, GPT, and Gemini. The differences are real but nuanced — the right choice depends on your workflow more than on objective quality.
windsurf wins at
- execution speed — SWE-1.5 at 950 tok/s
- Codemaps for visual codebase understanding
- cleaner, less cluttered UI
- price — $15/mo Pro vs Cursor's $20/mo
- Flows for multi-phase long tasks
- embedded Devin for autonomous delegation
- JetBrains plugin (Cursor is editor-only)
cursor wins at
- Tab autocomplete — more consistent, tighter
- codebase indexing maturity and reliability
- parallel background agents (ahead of Windsurf)
- ecosystem — more tutorials, extensions, community
- pricing transparency — fewer credit surprises
- ownership stability — single company, clear roadmap
Verdict: Try both. If you're an autocomplete-first developer, Cursor's Tab is more reliable. If you run long agentic tasks and care about speed and visual code understanding, Windsurf is ahead. Most developers should be on Cursor today; Windsurf is the right pick for speed-sensitive and monorepo-heavy workflows.
Where Windsurf gets it wrong
Tab autocomplete reliability
Cursor's Tab is more consistent than Windsurf's Supercomplete in everyday usage. Supercomplete's higher ambitions — predicting intent rather than tokens — means it occasionally misfires in ways that Cursor's more conservative predictions don't. When Supercomplete is right, it's more impressive. When it's wrong, the wrong prediction is further from what you wanted. The variance is wider.
Ecosystem maturity
Cursor has been the dominant VS Code fork for longer, and it shows in the ecosystem. More community tutorials, more .cursorrules templates, more third-party integrations, a larger Reddit and Discord community to ask questions in. Windsurf's community is growing fast but isn't there yet. If you get stuck on a Windsurf-specific issue, you may be reading docs rather than finding a Stack Overflow thread.
Ownership uncertainty
We're going to be direct about this: the Windsurf ownership situation is more complex than any other tool in this category. Cognition owns the product. Google has a separate technology license (for what exactly is not publicly documented). OpenAI was in acquisition talks that collapsed. If corporate instability is a risk you can't absorb — if you're standardizing tools for a team of 50 and need certainty about the roadmap — this uncertainty is real and worth factoring into your decision.
For individuals and small teams, the risk is lower: you can switch editors. The code you wrote stays yours. But it's not nothing.
Credit model opacity
Windsurf's credit system — where different models and features consume credits at different rates — can produce billing surprises. Using SWE-1.5 costs fewer credits than Claude Opus but more than a baseline model. Heavy Cascade usage with frontier models on the Pro tier (500 credits/month) can run out faster than expected. Cursor's model (unlimited completions, flat request cap) is easier to reason about.
Long-running Flow instability
For Flows that run across multiple sessions over days, state management is imperfect. We've had Flows where resuming a paused session caused Cascade to re-read context incorrectly — treating already-completed steps as pending. Workaround: keep a short written summary of where you left off and paste it into the next Cascade session. Not terrible, but not seamless.
Running Cascade with Claude or GPT-4o on a long session can consume 50-100 credits in under an hour. Switch to SWE-1.5 for execution tasks, frontier models only for architecture discussions. Check your credit balance before starting a multi-hour Flow on the last day of your billing cycle.
Pricing, in real terms
Windsurf's pricing is the most aggressive in the agentic IDE space. As of mid-2026, after the post-Cognition price restructuring:
- Free — $0/mo. 25 prompt credits, unlimited Supercomplete tab completions. Enough to evaluate the tool, not enough for daily use.
- Pro — $15/mo. 500 prompt credits, access to all models including SWE-1.5, SOC 2 compliance, priority support. This is the plan to buy.
- Teams — $30/user/mo. Admin dashboard, centralized billing, shared settings, SSO. For teams of 5+.
- Enterprise — $60/user/mo (custom for large orgs). Hybrid deployment, RBAC, dedicated support, volume discounts.
- Max — $200/mo. Primarily for Devin Cloud access — delegating tasks to autonomous cloud agents that run without IDE interaction. For power users who want Devin-level automation.
The $15/mo Pro tier makes Windsurf $5/mo cheaper than Cursor Pro — the cheapest serious agentic IDE on the market. For individual developers, that's $60/year. For a 10-person team, $600/year. The math is straightforward; the question is whether the product quality matches Cursor at that price point. Our answer: close enough that the price difference tips the calculation for budget-conscious teams.
Annual billing offers approximately 20% off across paid tiers, putting Pro at around $12/mo effective.
Power-user tips
SWE-1.5 is 13x faster than Sonnet and costs fewer credits. Use it as your default for Cascade execution tasks. Switch to Claude or GPT-4o only when you need deeper reasoning — system design, complex debugging, unfamiliar APIs. The model picker is one click inside Cascade.
Before writing a single prompt in an unfamiliar repo, open Codemaps. Let it render. Find the cluster of code relevant to your task. Click through to the annotated view of that module. Then open Cascade. Your prompts will be 3x more precise because you understand the structure.
Cascade's plan is the cheapest place to catch a wrong assumption. Read every line. If a file in the plan looks wrong, reject it and add a one-line note explaining why. Cascade will revise. Catching bad plans before execution saves you from reviewing a large wrong diff.
If a task has natural review gates — "implement the schema, then we review, then the API, then review, then the UI" — set it up as a Flow, not a single Cascade prompt. Flows preserve context between sessions and make the review gates explicit.
Connect your GitHub MCP server to Cascade. Now you can prompt: "read the open issues tagged 'bug' and fix the three with the highest vote count." Cascade will read GitHub, triage, write the fixes, and stage the diffs. This is what "agentic" actually means in practice.
Create a Space per feature branch. Bundle the Cascade session, the relevant files, and the linked PR into the Space. Switch between features by switching Spaces — context preserved. Stop losing track of where you were.
What's next for Windsurf
- Deeper Devin integration — the embedded Devin agent will become more capable and more tightly integrated into the Cascade/Flow workflow. The roadmap hint: Devin runs async tasks overnight; you review results in the morning. Background autonomous engineering.
- SWE-2.x — Cognition has signaled continued investment in proprietary coding models. The SWE-1.5 speed advantage is real; whether that lead holds as competitor models improve is the open question.
- Codemaps v2 — real-time Codemaps that update as you edit, not on indexing cycles. This is the feature gap that makes Codemaps less useful during rapid editing sessions.
- On-prem / air-gapped — available on Enterprise today in limited form, with full air-gapped support planned. This unlocks government and healthcare verticals that can't use cloud inference.
- Figma integration — paste a Figma design spec into Cascade, get the corresponding React component. In active development as of Q2 2026.
Alternatives worth knowing
FAQ
Is Windsurf safe to use given the ownership complexity?
Practically: yes. Your code is yours, the editor is a VS Code fork, and switching costs are low if Cognition's direction changes. Strategically: there's more uncertainty here than with Cursor (Anysphere, single owner, clear roadmap) or Copilot (Microsoft). For individuals and small teams, we'd call this manageable. For enterprise standardization decisions, it's worth monitoring over the next 6 months before committing fully.
What is Cascade and how does it differ from Cursor's Composer?
Both are multi-file agentic editing modes. Cascade's main advantage is speed — SWE-1.5 at 950 tok/s means a Cascade task that takes Cursor 9 seconds takes Windsurf under 5 seconds. Cursor's Composer has more mature codebase indexing and slightly higher acceptance rates on our benchmarks. The UX is different: Cascade has a more explicit plan-approve-execute flow; Composer is slightly more continuous. Try both — the feel is different enough that personal preference matters.
Does Google's technology license affect my use of Windsurf?
No direct user impact. Google secured a license for Windsurf's underlying technology as part of the Cognition acquisition deal — this is a corporate IP arrangement, not a product split. You use Windsurf as a Cognition product. The Google license doesn't change what you can do in the editor.
Can I use Windsurf in JetBrains or VS Code without switching editors?
Yes. Windsurf ships as a plugin for VS Code, JetBrains IDEs (IntelliJ, PyCharm, GoLand, etc.), and Vim. The plugin gives you Cascade and Supercomplete. Codemaps and Spaces are editor-only features. If you're a heavy JetBrains user who doesn't want to switch, the plugin is a credible option — better than Cursor, which has no JetBrains support.
What's SWE-1.5 and do I have to use it?
SWE-1.5 is Cognition's proprietary coding model, built specifically for agentic IDE tasks. It runs at 950 tokens/second — significantly faster than frontier models. You don't have to use it: the model picker in Cascade lets you switch to Claude, GPT-4o, Gemini, or any other supported model. We recommend SWE-1.5 for execution tasks, frontier models for architectural reasoning. It's a tool, not a mandate.
How does the credit system work?
Each Cascade action consumes credits, with the amount depending on the model used and the length of the task. SWE-1.5 costs fewer credits than frontier models. The Pro tier includes 500 credits/month, which is enough for moderate daily Cascade usage — roughly 2-5 full multi-file tasks per day. Heavy users (10+ tasks/day) will hit limits and need to top up or upgrade. Check the credit counter in the Cascade pane after each session to build intuition for your burn rate.
Is my code used to train Cognition or Windsurf models?
Windsurf states that customer code is not used to train models. Cognition holds zero-retention agreements with the frontier model providers (Anthropic, OpenAI, Google) it routes through. Your code goes to the inference endpoint and is not retained beyond the request. This is the standard enterprise commitment in the AI coding tool space — but verify the current policy in Windsurf's privacy documentation, as post-acquisition terms can change.
Windsurf or Cursor for a first agentic IDE?
Cursor. It has a larger community, more tutorials, more consistent Tab autocomplete, and a simpler pricing model. Windsurf is an excellent choice once you're already comfortable with agentic IDE workflows and want to optimize for speed or need the JetBrains plugin. If you're evaluating for the first time, start with Cursor's free tier for a week, then try Windsurf's free tier. Most developers settle on one after honest comparison.
What is Devin integration and do I need it?
Devin is Cognition's autonomous software engineer — an agent that can take a task, open a browser, write code, run tests, and iterate without human input at each step. In Windsurf, Devin is available as a cloud agent you can delegate tasks to from the editor. On the Pro plan you get limited Devin sessions; the Max plan ($200/mo) gives full Devin Cloud access. For most developers, Cascade is sufficient. Devin integration is the premium tier for teams who want fully autonomous task execution.
The verdict
The fastest agentic IDE at the lowest price — with caveats worth knowing.
Windsurf earns its score on product merit: SWE-1.5 is genuinely faster than anything else in the category, Codemaps is a feature no competitor has shipped, Cascade's per-step approval model is the right design for production code, and $15/mo Pro is the best value in agentic IDEs. For speed-sensitive workflows and monorepo onboarding, Windsurf is the right choice today.
We dock 0.8 points for three honest concerns: Cursor's Tab autocomplete is more reliable, the ownership situation (Cognition + Google license + OpenAI history) introduces more uncertainty than comparable tools, and the credit model is less transparent than we'd like. None of these are dealbreakers. All of them are worth knowing before you commit your team's workflow to the tool.
The one-line take: if Cursor is the mature, reliable choice, Windsurf is the faster, cheaper, more technically interesting one. Both are excellent. A week of free-tier testing on your actual codebase will tell you more than any benchmark.