Google Antigravity vs Cursor vs Bolt — A Monetization-First Comparison of AI Coding IDEs

Cost vs. time-rate return

A simple way to evaluate the monthly subscription is to compare it against the hourly rate of the work it accelerates.

■ Google Antigravity
Subscription: $20–$200 / month depending on plan
Best at: large existing codebases, parallel project management
Time-rate return: high (3-5x with parallel agent operation)
Break-even: 20+ hours of dev work per month
 
■ Cursor
Subscription: $20–$40 / month depending on plan
Best at: deep solo work, fine-grained code control
Time-rate return: medium-high (1.5-3x productivity)
Break-even: 10+ hours of dev work per month
 
■ Bolt
Subscription: $20–$50 / month depending on plan
Best at: prototypes, MVPs, landing pages
Time-rate return: high (5-10x for zero-to-one work)
Break-even: 3-5 prototypes/MVPs per month

For a $100/hr freelancer working twenty hours a month, a 3x productivity gain turns $2,000 of billable output into roughly $6,000, and a $200 tier is about 3% of that. For a junior developer billing $20/hr, the same tier represents 100 hours of real labor and the calculation is not remotely the same.

Replace my multipliers with your own numbers

The 3-5x and 1.5-3x figures above come from my own engagements. They will not match yours. So rather than asking you to borrow a multiplier, here is the procedure I use to measure one.

There is exactly one number worth tracking: effective hourly rate per IDE.

The part that makes or breaks the measurement is counting review and rework inside elapsed time. Agent work is not finished when generation finishes. Reading the diff, correcting it, and running it again are all part of the hour. In my own ledger, roughly 40% of elapsed time on Antigravity engagements went to review and rework. The faster generation gets, the more review becomes the constraint. Leave that out of your elapsed time and you end up with impressive multipliers and flat take-home pay.

One line per finished engagement is enough. This is the whole script I use.

#!/usr/bin/env python3
"""ide_ledger.py - a minimal ledger for effective hourly rate per IDE."""
import json
import sys
from collections import defaultdict
from pathlib import Path
 
LEDGER = Path.home() / ".ide_ledger.jsonl"
 
 
def log(ide, hours, revenue, kind):
    """Append one engagement. hours must include review and rework."""
    rec = {"ide": ide, "hours": hours, "revenue": revenue, "kind": kind}
    with LEDGER.open("a", encoding="utf-8") as f:
        f.write(json.dumps(rec, ensure_ascii=False) + "\n")
 
 
def report():
    if not LEDGER.exists():
        print("no entries yet")
        return
    agg = defaultdict(lambda: {"hours": 0.0, "revenue": 0, "n": 0})
    for line in LEDGER.read_text(encoding="utf-8").splitlines():
        if not line.strip():
            continue
        r = json.loads(line)
        a = agg[(r["ide"], r["kind"])]
        a["hours"] += r["hours"]
        a["revenue"] += r["revenue"]
        a["n"] += 1
 
    rows = []
    for (ide, kind), a in agg.items():
        rate = a["revenue"] / a["hours"] if a["hours"] else 0.0
        rows.append((rate, ide, kind, a))
 
    for rate, ide, kind, a in sorted(rows, reverse=True):
        print(f"{ide:<12} {kind:<14} {a['n']:>2} jobs "
              f"{a['hours']:>6.1f}h  effective {rate:>7,.0f}/h")
 
 
if __name__ == "__main__":
    if len(sys.argv) >= 5 and sys.argv[1] == "log":
        kind = sys.argv[5] if len(sys.argv) > 5 else "misc"
        log(sys.argv[2], float(sys.argv[3]), int(sys.argv[4]), kind)
    else:
        report()

Usage is one command when a job closes.

$ python3 ide_ledger.py log antigravity 18.5 3500 refactor
$ python3 ide_ledger.py log cursor 9.0 1200 ui-polish
$ python3 ide_ledger.py log bolt 4.5 800 prototype
 
$ python3 ide_ledger.py report
bolt         prototype       3 jobs   12.0h  effective     178/h
antigravity  refactor        4 jobs   61.5h  effective     163/h
cursor       ui-polish       6 jobs   38.0h  effective      95/h

Two weeks of entries answer a better question than "which IDE is faster." They tell you which category of work pays more when routed to which IDE, which is the only version of the question an indie developer can act on. In my ledger, refactor work landing on Antigravity and fine App Store UI work landing on Cursor stopped being a hunch and became a number. The same data also showed something I had not expected: short bug fixes routed to Antigravity lose money, because the setup time exceeds the work itself.

For unbilled work on your own products, enter what you would pay someone else to do it. It is not accounting, it is a yardstick for routing decisions, and a rough one is enough.

The first ranking your ledger returns is mostly your case mix

Here is the mistake I want to write down before anything else.

Two weeks into running the ledger, I read the report output and took away "Bolt earns the most." It sat at the top of the list. That week I pushed back a migration engagement and took prototype inquiries first instead. At the end of the month, my take-home had not moved.

Counting what was actually inside the ranking explained it. Bolt only ever carried prototype work, while Cursor was dragging six low-rate ui-polish jobs. What the list ranked was not the capability of each IDE — it was how I had distributed work across them. It ranked my case mix.

Rolled up by IDE, my thirty logged jobs looked like this.

bolt          4 jobs   15.0h  effective     173/h
antigravity  10 jobs   96.5h  effective     135/h
cursor       16 jobs  105.5h  effective     118/h

Read that alone and Cursor is last. Slice the same data by category and Cursor beats Antigravity on bugfix. What pushed it to the bottom was the weight of 38 hours of ui-polish. Drop just those rows and recompute, and Cursor moves from $118/h to $132/h — with nothing about the tool changed. The only thing that changed was what I had put on it.

Only IDEs that share a category are comparable. A ranking that spans categories describes how you route work, not how the tools perform.

So I added a paired mode to ide_ledger.py that prints only the categories covered by two or more IDEs, and marks thin cells so you can skip them.

MIN_N = 3  # don't read a ranking until a cell holds this many jobs
 
 
def paired():
    """Print only categories covered by two or more IDEs."""
    agg = load()
    by_kind = defaultdict(dict)
    for (ide, kind), a in agg.items():
        by_kind[kind][ide] = a
 
    for kind in sorted(by_kind):
        cells = by_kind[kind]
        if len(cells) < 2:
            print(f"[{kind}] not comparable ({', '.join(cells)} only)")
            continue
        print(f"[{kind}]")
        ranked = sorted(
            cells.items(),
            key=lambda kv: kv[1]["revenue"] / kv[1]["hours"] if kv[1]["hours"] else 0.0,
            reverse=True,
        )
        for ide, a in ranked:
            rate = a["revenue"] / a["hours"] if a["hours"] else 0.0
            mark = "" if a["n"] >= MIN_N else f"  <- n={a['n']}, too thin to read"
            print(f"  {ide:<12} {a['n']:>2} jobs {a['hours']:>6.1f}h  effective {rate:>6,.0f}/h{mark}")

Pull the aggregation half of report() out into a load() helper and both modes share it. The same thirty jobs come back like this.

$ python3 ide_ledger.py paired
[bugfix]
  cursor        5 jobs   12.5h  effective    120/h
  antigravity   3 jobs    9.0h  effective    106/h
[prototype]
  bolt          4 jobs   15.0h  effective    173/h
  cursor        2 jobs   11.0h  effective    109/h  <- n=2, too thin to read
[refactor]
  antigravity   4 jobs   61.5h  effective    163/h
  cursor        3 jobs   44.0h  effective    141/h
[ui-polish]
  cursor        6 jobs   38.0h  effective     95/h
  antigravity   3 jobs   26.0h  effective     81/h

Since switching to this view, the differences I read are the ones that survive a fair comparison: refactor to Antigravity, bugfix to Cursor. The Cursor row under prototype has two jobs behind it, so it stays parked, exactly as the mark says. Failing to park it is what cost me that first fortnight.

MIN_N is where the threshold lives. Three works for me; if your engagement sizes vary a lot, five may serve you better. The number itself matters less than deciding it before you look. Decide it afterwards and you will find yourself reading the ranking on whichever week it flatters you.

Which IDE for which engagement type

Refactoring and feature work in existing codebases

Best fit: Antigravity.

For 50K–500K line codebases, the agent's ability to scan the whole project and keep changes consistent is a real advantage. I will let an agent plan a Rails 6→7 migration, review the plan, and then run it; what would have taken three months of single-developer work compresses into two.

Refactors are also where value-based pricing dominates because hours are hard to estimate. Productivity gains from Antigravity translate directly into margin.

New web apps and SaaS MVP builds

Best fit: Bolt for the prototype, Antigravity for production.

Spend the first week in Bolt to put something running in a browser the client can poke. Three feedback rounds usually crystallize the requirements. Switch to Antigravity for production-grade work — auth, billing, edge deploys, real data — and you typically deliver a polished MVP inside four weeks.

The combo is what makes "I'll have a working demo for our meeting next week" a credible promise. That single ability shifts the conversation in pre-sales meaningfully.

Long-lived solo product (an indie SaaS you own)

Best fit: Cursor.

For a product you will maintain yourself for years, you want to keep the codebase fully in your head. Letting Antigravity write big chunks autonomously is fast in the short term but leaves you with code you cannot read six months later. Cursor's pace matches your own thinking and keeps your mental model and the code aligned.

Mass prototype production (consulting, sales tools)

Best fit: Bolt.

Building ten different demos for ten different prospects, or five A/B variants of a landing page — Bolt's lack of local setup and one-click deployment cuts per-prototype time by roughly two-thirds.

Bug fixing and incident response

Best fit: Cursor for small/medium bases; Antigravity when the bug location is unknown in a large codebase.

Bolt has limited utility here.

Recommended stacks for solo founders

How to pick a setup, by what you do for a living.

Scenario A — Full-time contracting (20+ active engagements)

Recommended: Antigravity (primary) + Cursor (secondary).

Antigravity carries the parallel work; Cursor handles fine reviews and quick fixes. Combined annual cost $400–$600, comfortably positive against revenue at any serious freelancer rate.

Scenario B — Solo SaaS founder (long-lived owned product)

Recommended: Cursor (primary) + Bolt (for prototypes).

Build the long-lived product in Cursor where your understanding can keep up. Use Bolt to test new ideas or spin up secondary products. Combined annual cost $350–$500, instantly positive once the product clears $1,000 of MRR.

Scenario C — Consulting / education / outsourced work

Recommended: Bolt (primary) + Antigravity (for engagements that go to production).

Demos, sales prototypes, workshop samples on Bolt. When an engagement converts to a real build, lift it to Antigravity. Combined annual cost $400–$600 — easily covered by a single engagement.

Scenario D — Side project / hobby (under 10 hours/month)

Recommended: Cursor Starter only.

Multiple subscriptions are not justified at this volume. The $20 Cursor plan delivers most of the productivity benefit for the right price. Try Antigravity on its free quota; revisit when usage justifies a paid plan.

A value-based proposal template that fits Antigravity

The biggest pricing leverage point with Antigravity is the multi-agent story. Clients understand "I am hiring an AI agent team" much faster than they understand "I am hiring a developer who uses agents."

■ Proposal language
 
Engagement structure:
We will run this engagement on a multi-agent development model
using Google Antigravity. I supervise architecture, quality
assurance, and deployment; multiple AI agents work implementation
streams in parallel.
 
Expected outcomes:
- 2-3x delivery speed versus solo development
- 10-15 modules can progress concurrently
- Every code change reviewed by an independent agent and
  has tests generated automatically
 
Estimate:
Traditional effort estimate: 6 person-months
Estimate under this model: 2-3 person-months
Quoted price: $XX,XXX (70-80% of the traditional estimate)

The shift the template makes is from "I'll be faster as one person" to "you are buying a multi-agent team that I lead." Clients respond to the second framing far more comfortably with the price tag.

Real engagement studies

Three real engagements where the IDE choice was non-obvious.

Case 1 — Rails 6→7 migration on a mid-sized ecommerce site

Codebase ~80K lines. Budget around $35K, three-month window.

Chose: Antigravity. Whole-codebase context is the deciding factor for migration work. The agent planned the upgrade, I reviewed the plan, and then it executed against the plan. Actual effort came in at 2.2 months of calendar time on a value-based contract — same revenue, materially higher hourly economics.

Case 2 — Seed-stage matching app MVP

Four-week budget around $8K. Web frontend plus Stripe-paid backend.

Chose: Bolt then Antigravity. First week in Bolt produced a clickable browser prototype in two days; three feedback rounds locked the spec inside a week. Migrated to Antigravity, finished Stripe, auth, and Cloudflare Workers deploy inside three more weeks. Bolt alone could not have hit production-grade auth and billing; Antigravity alone would have spent too long on the prototype phase.

Case 3 — Adding features to my own SaaS

Personal product on a long-lived codebase.

Chose: Cursor. Speed matters less than keeping the codebase comprehensible to me as the only long-term maintainer. Almost all my own product work happens in Cursor for this reason.

Don't lock yourself into a single IDE

A note on time horizons. The AI IDE market is moving quickly enough that the "best" choice for one quarter routinely becomes "second best" the next. New agent capabilities, new model integrations, new pricing all ship monthly.

Three habits help you stay flexible.

Keep code structured in standard ways. Editor-specific configurations and project layouts trap you. Configs like .vscode/, .cursor/, .antigravity/ belong in .gitignore, configured per-developer.

Store prompts and system instructions outside the IDE. Markdown files at the repo root are readable from any IDE.

Try every new IDE on one small engagement within 90 days of release. The cost of always being current is much lower than the cost of falling behind.

How to talk about your tools with clients

A short script for the inevitable "do you use AI tools?" question.

"My setup combines Google Antigravity, Cursor, and Bolt depending on the engagement. The tools genuinely use AI, but output quality still depends on who is wielding them and in what context. My contribution is choosing the right configuration per engagement, orchestrating multiple agents, and standing behind quality and production-readiness. The final delivery responsibility is mine."

This framing addresses the unspoken "could we do this in-house?" anxiety directly while keeping your value clearly named. It also opens the door to charging for the configuration decision itself: "We can include the recommended tooling setup for your team in the engagement scope" turns what used to be invisible expertise into a billable line.

Price pages move, so re-derive your break-even

A short word on plans.

All three IDEs revise their pricing on a short cycle. Anything I write here as "the current price" is likely stale by the time you read it — I have had to rewrite my own numbers on this topic within six months. So check the vendors' pricing pages for the figures, and use this for what to do once you have them.

When a monthly price changes, take the category that IDE wins in your paired output, and divide the new monthly cost by that category's effective hourly rate. That gives you the hours of work required to cover the subscription. If the answer exceeds the hours you actually bill in a month, you either drop a tier or move that category to a different IDE.

A price change is a signal to revisit your routing, not your contract. Cancelling because the bill went up skips the question worth asking: is that IDE still winning the category you keep feeding it? With a ledger, that stops being a feeling and becomes a number.

A pricing exercise: comparing three IDE stacks across a year

To make the cost-vs-return story concrete, here is a one-year projection across three setups for a freelancer billing roughly $120/hour and working ~80 billable hours per month.

Setup 1: Cursor only, $20/month
  Annual subscription: $240
  Productivity multiplier on existing hours: ~1.7x
  Effective annual revenue uplift (vs no AI tooling):
    80 hr × 12 × $120 × 0.7 = $80,640
 
Setup 2: Antigravity Pro + Cursor, $220/month total
  Annual subscription: $2,640
  Productivity multiplier including parallel agent operation: ~3.0x
  Effective annual revenue uplift: 80 hr × 12 × $120 × 2.0 = $230,400
 
Setup 3: Antigravity Pro + Cursor + Bolt, $260/month total
  Annual subscription: $3,120
  Productivity multiplier with prototype acceleration: ~3.5x
  Effective annual revenue uplift: 80 hr × 12 × $120 × 2.5 = $288,000

Setup 3 spends $2,880 more than Setup 1 to capture roughly $200,000 of additional annual revenue uplift. The decision is essentially trivial at any rate above $50/hour.

The reason Setup 3 wins is not that the third IDE is dramatically faster. It is that engagement diversity is itself a productivity multiplier. Having Bolt available means saying yes to prototype-heavy engagements you would otherwise decline. Having Antigravity available means quoting larger refactor jobs with confidence. The stack expands the set of work you will accept.

What I would tell a 2024-me

If I could send a note back two years to the version of me starting to use AI IDEs seriously, it would be three sentences.

Stop trying to find the single best IDE. Run three of them, pick per engagement, and review the mix every quarter.

Charge for outcomes, not hours. The productivity gain you are about to experience is too large to leave on the client's side of the table.

Write publicly about the work you do with these tools. The engagements that find you because of your writing are higher rate, less negotiation, and longer-term than anything you will pitch cold.

The market changes too fast for any of these to settle into a permanent answer. But all three are still true today, and I expect them to stay true for the next two years.

Closing — and what to do this week

Three IDEs, three different optimization targets, three different paths to revenue. Tools are temporary; the discipline of choosing tools for outcomes is not.

So here is the concrete next step. Pick the scenario above (A through D) closest to your own work and run its recommended stack for two weeks. Log every finished job with ide_ledger.py while you do, and when the numbers land, start from paired rather than report. At that point my multipliers stop mattering — you will have your own, and they will be the ones worth pricing against.

Thank you for reading this far.