# Cargo

> Cargo is a go-to-market orchestration platform that sits between a company's CRM, data warehouse, enrichment vendors, and outbound tools, and runs the logic that connects them. It provides shared data models for companies, contacts, and deals, event-driven workflows called Plays, typed integration actions called Tools, and LLM agents that use those tools. Everything can be built visually or declared in TypeScript and deployed with a CLI, and usage is billed in credits rather than per seat.

- Category: GTM Engineering (https://saastracker.org/categories/gtm-engineering)
- Website: https://www.getcargo.ai
- Starting price: Free plan with 100 credits per month; paid plans from about $165 per month
- Free plan: 100 credits per month, community support, all features included, no SSO
- Free trial: Free plan with 100 credits and no payment method required, plus a 14-day satisfaction guarantee on paid plans
- Founded: 2023, HQ: San Francisco, California, with a team in Paris, Ownership: Independent, venture-backed
- Profile last reviewed: 2026-08-23
- Canonical profile: https://saastracker.org/products/cargo

## Overview

Most revenue teams do not have a data problem so much as a wiring problem. Account data lives in the CRM, firmographics come from three enrichment vendors, product usage sits in a warehouse, scoring lives in a spreadsheet someone maintains by hand, and the routing rules exist only in a Slack thread. Cargo is built for the person who has been asked to make all of that behave like one system: the GTM engineer or technical RevOps hire who is comfortable writing a transform but does not want to run a bespoke ETL stack for the sales team.

The product is organized around a small set of primitives. Connectors attach external systems and hold the credentials, which means Cargo does not resell data; you bring your own Clearbit, Apollo, or OpenAI keys and pay those vendors directly. Data models are tables built from those connectors, so a company record can be assembled from HubSpot, a warehouse table, and two enrichment providers without any of them becoming the source of truth by accident. Tools are typed actions across the connector catalog. Plays are the workflows that fire on a data change, a schedule, or a manual trigger. Agents are LLM workers that hold a prompt, a set of tools, and a trace of every run.

The distinguishing bet, and the one that has sharpened since 2025, is what Cargo calls GTM as Code. A workspace is a folder of TypeScript files, one define call per resource, deployed with the cargo-ai CLI. That makes revenue logic reviewable in a pull request, diffable between environments, and reproducible after someone leaves. Teams that prefer the visual builder can stay in it; teams that want scoring changes to go through code review get a path that no visual-first competitor really offers.

The honest framing for a small business: Cargo is not an SMB tool in the way a sequencing product is. The free plan is 100 credits a month, which is a look around rather than a working deployment, and the first genuinely usable paid step is in the $165 to $250 per month range before enrichment vendor costs. Above that the ladder jumps to roughly $1,190 and then $3,000 a month. It is buyable without a sales call, which is more than several competitors offer, but it expects a technical operator on the other side and there is no meaningful value in it for a team that just wants to send email.

## How it works

1. You create a workspace and attach connectors: a CRM such as HubSpot or Salesforce, a warehouse, enrichment providers, an LLM provider, and whatever sequencing or messaging tool the team already uses. Credentials live at the workspace level, so individual plays and agents never carry their own keys, and swapping an enrichment vendor is a connector change rather than a rewrite.

2. Next you define data models. A model is a table assembled from one or more connectors, typically companies, contacts, and deals, plus custom objects for whatever the business actually sells. Records are upserted into the model, which is what gives Plays a stable object to react to instead of a stream of webhook payloads.

3. Plays are the execution unit. A Play is triggered by a record changing, a schedule, an inbound event, or a manual run, and then walks a sequence of steps: call a tool, branch on a condition, wait, write back to the model, hand off to an agent. Each run produces a trace, and steps retry with backoff, which is the difference between a workflow you can operate and one you rebuild every time it silently fails.

4. Agents are steps inside that sequence rather than a separate product. An agent holds a prompt, a tool list, and access to the same data models, so a research agent can gather and verify account context, a qualification agent can read what it wrote and score against an ICP definition, and a handoff step can push the result into the CRM or a sequencer. Because they share one model, chaining agents does not mean passing blobs of text between them.

5. For code-first teams, the same workspace is expressed as TypeScript: defineConnector, defineModel, defineTool, defineWorkflow, defineAgent, definePlay, defineApp. The cargo-ai CLI plans and reconciles the folder against the live workspace, updating only what changed. Apps are Vite single-page applications deployed alongside the workspace, which is how a rep-facing view of the data gets built without standing up separate hosting.

## Best for

Technical revenue operators and GTM engineers at funded startups and mid-market software companies who need enrichment, scoring, routing, and agent workflows to run as one governed system, and who want that logic versioned in code rather than trapped in a visual canvas.

## Not the right fit for

- Small teams without a technical operator; the platform assumes someone who can reason about data models, retries, and API semantics, and the visual builder does not remove that requirement.
- Anyone who wants sequencing included; Cargo has no native email or LinkedIn sequence builder and expects you to hand off to Instantly, lemlist, Smartlead, or your CRM's own tooling.
- Buyers who need bundled contact data; Cargo orchestrates enrichment providers but you bring and pay for those accounts separately, so it is not a replacement for a data vendor.
- Budgets under a few hundred dollars a month once enrichment vendor spend is included, since the free tier's 100 monthly credits will not sustain a production workflow.
- Teams that mainly need general-purpose automation across finance, support, and internal ops; a horizontal automation tool covers that ground more cheaply.
- Organizations that want a large public review corpus before buying; Cargo is still thin on independent reviews compared to older tools in this space.

## Features

### Data layer

Shared models that give workflows and agents one object to read and write.

- **Unified data models**: Companies, contacts, deals, and custom objects defined as tables assembled from multiple connectors, so no single system silently becomes the source of truth.
- **Bring-your-own connectors**: Enrichment, LLM, and CRM credentials are held at the workspace level and billed by those vendors directly, which keeps Cargo out of the data resale margin.
- **Warehouse sync**: Models can be built from warehouse tables, so product usage and billing data participate in scoring without being copied into the CRM first.
- **Waterfall enrichment**: Sequence several providers for the same field and stop at the first acceptable result, which is the standard pattern for cutting per-record enrichment cost.
- **Deduplication and verification**: The research path checks for duplicates and validates contact records across connected sources before anything is written back to a downstream system.
- **Context and knowledge layer**: ICP definitions, personas, and playbooks are stored as versioned context that both humans and agents read from, rather than being restated in every prompt.

### Plays and orchestration

Event-driven workflows with the operational plumbing you need to trust them.

- **Event, schedule, and manual triggers**: A Play can fire when a record changes, on a cron schedule, from an inbound event, or on a button press, which covers both always-on monitoring and one-off backfills.
- **Typed tools across 100+ connectors**: Integration actions are typed rather than raw HTTP calls, so a broken field mapping surfaces at plan time instead of halfway through a run.
- **Branching and conditions**: Route records down different paths based on model fields, agent output, or external lookups, with waits for the cases where a downstream system needs time.
- **Retries with backoff**: Steps retry automatically on transient failures, which matters when a workflow depends on four third-party APIs with different reliability profiles.
- **Run traces**: Every run records what each step received and returned, so debugging a bad score means reading a trace rather than reproducing the conditions by hand.
- **Territories and routing**: Capacity-aware assignment rules distribute accounts and leads across reps, including round-robin and rule-based ownership written back to the CRM.

### AI agents

LLM workers that live inside workflows rather than beside them.

- **Research agent**: Gathers and verifies account and contact context from connected sources and writes structured fields back to the model instead of returning prose.
- **Qualification agent**: Scores records against a stored ICP definition and explains its reasoning, with the score written to the model where routing rules can read it.
- **SDR agent**: Drafts and personalizes outbound based on the researched context, then hands off to whatever sequencing tool is connected rather than sending directly.
- **Bring your own agent**: Agents built elsewhere can run on the same runtime and use workspace tools and credentials, which avoids a second orchestration layer appearing next to this one.
- **Shared tool access**: Agents call the same typed tools as workflows, with credentials managed centrally, so an agent cannot quietly acquire access a workflow does not have.
- **Agent chaining through the model**: Multiple agents in one Play each read what the previous one wrote to the data model, which keeps handoffs structured instead of passing free text along.

### GTM as Code and interfaces

The code path, the CLI, and the surfaces the rest of the team touches.

- **TypeScript workspace definition**: Connectors, models, tools, agents, plays, and apps are declared as TypeScript files, one define call per resource, checked into a repository like any other code.
- **cargo-ai CLI**: Log in, plan, and deploy from the terminal; the CLI reconciles the folder against live state and updates only what changed, so deploys are diffable.
- **Visual builder**: The same primitives are editable in a canvas for people who do not want to write TypeScript, with both paths acting on the same underlying workspace.
- **Internal apps**: Vite single-page applications deployed with the workspace give reps and ops a purpose-built view of the data without separate hosting or auth.
- **Open templates and skills**: A public manifest template and a library of reusable GTM agent skills covering TAM building, account scoring, and enrichment give new workspaces a starting point.
- **Credit metering visibility**: Per-integration credit costs are inspectable from the CLI and the workspace UI, which is the only practical way to forecast spend under a usage model.

## Use cases

- **GTM engineer at a Series B software company**: Lead scoring lives in a spreadsheet, enrichment runs through three separate vendor dashboards, and nobody can explain why a given account was routed to enterprise. Outcome: Scoring, waterfall enrichment, and territory assignment are defined as Plays over one company model, deployed from a repository, and every routing decision has a run trace attached to it.
- **RevOps lead consolidating an agent sprawl**: Three teams have each wired up their own AI research script against different API keys, with no shared definition of the ICP and no logging. Outcome: The scripts are rebuilt as agents on one runtime with centrally held credentials and a shared context layer, so the ICP is defined once and every run is traceable.
- **Founder-led sales team monitoring a target account list**: The team wants to know within a day when an account hires a VP of Marketing, changes its tech stack, or posts a relevant job, but nobody has time to watch manually. Outcome: A scheduled Play re-researches the account list, writes changed signals to the model, and pushes a qualified handful into the sequencing tool each morning with the evidence attached.
- **Agency running outbound for several clients**: Every client has a slightly different enrichment stack and qualification rule, and the agency rebuilds the same workflow by hand each time. Outcome: One workspace repository becomes a template, cloned per client with different connectors and thresholds, and changes ship through pull requests instead of being clicked through a canvas.

## Pricing

Usage-based credits on a subscription plan, with no per-seat charge and no feature gating between tiers. Credits are consumed by integration tasks (priced per integration), orchestration steps (roughly 1 credit per 100 steps), and storage upserts (roughly 1 credit per 1,000 upserts). Enrichment and LLM provider costs are separate, since you connect your own accounts. Prices are quoted as from figures because per-integration credit consumption varies.

- **Free**: $0 per month. 100 credits per month; Every feature included, including agents and the CLI; Community support, no SSO. Enough to build and test a workflow end to end; not enough to run one against a real account list.
- **Starter**: From $165 per month. About 1,500 credits per month; Email support; Full access to plays, agents, data models, and 100+ integrations.
- **Professional**: From $250 per month. About 2,500 credits per month; Email support; Same feature set, higher throughput. The practical entry point for a team running continuous enrichment on a live account list.
- **Enterprise**: From $1,190 per month. About 17,000 credits per month; Priority support; SSO included. The jump from Professional is large; there is no intermediate step between roughly 2,500 and 17,000 credits.
- **Premium Enterprise**: From $3,000 per month. 50,000 or more credits per month; Dedicated support; SSO and overage billing available.

Billing notes:

- Credits reset at the end of each billing term and do not roll over, so an under-used month is money spent.
- Three separate things consume credits (integration tasks, orchestration steps, storage upserts), which makes month-to-month forecasting harder than a single per-record price would be.
- Enrichment providers and LLM tokens are billed by those vendors on your own accounts, so the Cargo line item is not the whole cost of running a workflow.
- Automatic top-ups can be enabled when credits run low, and Enterprise workspaces can enable overage with monthly invoicing rather than hard stopping.
- There is no per-seat charge on any tier and no feature gating apart from SSO and support level, which is unusual in this category.
- The gap between the roughly $250 tier and the roughly $1,190 tier is the sharpest step in the ladder and the point where a growing team should model credit consumption carefully.

Value assessment: Priced against the alternative of a data engineer maintaining glue scripts, Cargo is inexpensive; priced against the tools a five-person sales team actually buys, it is not an impulse purchase. The seat-free model is genuinely favorable for agencies and for teams where many people benefit from workflows one person builds, and the absence of feature gating means the Starter plan is the whole product rather than a demo. The weak spot is predictability: with credits consumed by integration calls, orchestration steps, and storage writes at once, the first two months are an estimation exercise, and the leap to the Enterprise tier arrives faster than most buyers expect once always-on plays are running.

## Strengths

- Revenue logic can be versioned, reviewed, and deployed like software, which is a real answer to the problem of critical scoring rules living inside one person's canvas.
- Agents are steps inside workflows sharing one data model, so multi-agent handoffs stay structured instead of degrading into text passed between prompts.
- Bring-your-own credentials for enrichment and LLM providers means no data resale markup and no vendor lock-in on the data layer.
- No per-seat pricing and no feature gating between tiers, so a small technical team gets the full platform at the entry price.
- Run traces, typed tools, and automatic retries make workflows operable rather than just constructible.
- Both a visual builder and a code path exist over the same primitives, which lets a technical owner and a non-technical operator work on the same workspace.
- Self-serve signup with published prices up to $3,000 a month, where several competitors in this category require a sales conversation.

## Limitations

- The credit meter has three dimensions (integration tasks, orchestration steps, storage upserts), so spend is genuinely hard to forecast before a month of real usage.
- No native sequencing: there is no email or LinkedIn sequence builder, so outbound execution always requires a second tool and a handoff step.
- The built-in enrichment provider catalog is smaller than Clay's, which matters if your waterfall depends on a long tail of niche data vendors.
- The step from about 2,500 credits to about 17,000 credits is a jump from roughly $250 to roughly $1,190 a month with nothing in between.
- Credits do not roll over, so seasonal or campaign-shaped usage patterns pay for capacity they do not use.
- The free plan's 100 monthly credits is an evaluation allowance, not a working free tier, unlike several tools in adjacent categories.
- Independent review volume is thin on G2 and Capterra relative to older tools, so there is less public evidence to check claims against.
- The code-first path is the product's best idea and also its narrowest audience; without someone who writes TypeScript, half the differentiation is unavailable.

## Comparisons

- **Cargo vs ScrapingBee**: Different layers of the same pipeline. ScrapingBee is an HTTP API that returns the rendered HTML of a page, handling proxies, headless browsers, and blocking so you do not have to. Cargo does not scrape; it orchestrates. The common pattern is ScrapingBee as a connector inside a Cargo Play, fetching a careers page or pricing page whose content an agent then reads and turns into a structured field on the company model. If you only need pages fetched, ScrapingBee alone is far cheaper.
- **Cargo vs Octoparse**: Octoparse is a point-and-click desktop and cloud scraper aimed at people extracting lists from websites without writing code, priced from a free tier into the low hundreds per month. It ends at a spreadsheet or an export. Cargo starts where that export lands: models, enrichment, scoring, routing, and CRM writeback. A small team that mainly needs a list of prospects from a directory should buy Octoparse and stop there; a team that needs that list continuously refreshed, scored, and routed is the Cargo case.
- **Cargo vs Clay**: The direct comparison, and the one most buyers are actually making. Clay is spreadsheet-shaped, has the deeper built-in provider waterfall, and is far easier for a non-technical operator to get results from on day one. Cargo is code-shaped, with versioned workspaces, a CLI, run traces, and agents that share a real data model rather than living in table columns. Choose Clay if the person doing the work is a growth marketer; choose Cargo if it is an engineer who wants scoring changes to go through pull requests.
- **Cargo vs n8n**: n8n is general-purpose workflow automation with self-hosting and a much wider node catalog, and it will happily do GTM work. What it lacks is the GTM-specific layer: shared company and contact models, waterfall enrichment patterns, territory assignment, and an ICP context store. Teams already running n8n for internal ops often try to extend it into revenue workflows and hit the point where they are hand-building a CRM data model inside it. That is the moment Cargo makes sense; before it, n8n is cheaper and more flexible.
- **Cargo vs Hightouch**: Hightouch is reverse ETL: it syncs warehouse tables into the CRM and other destinations reliably, and its center of gravity is the data team. Cargo assumes the warehouse is one input among many and adds the reasoning and execution layer on top, including agents and outbound handoffs. Companies with a mature warehouse often run both, Hightouch for governed syncs and Cargo for the workflow logic; companies without a warehouse will find Hightouch has little to do.
- **Cargo vs Apify**: Apify is a marketplace and runtime for scrapers and automation actors, billed by compute and actor usage, and it is the better tool when the hard part is acquiring data from many awkward sources. Cargo is the better tool when the hard part is what happens after the data arrives. They compose cleanly: an Apify actor as a Cargo connector feeding a data model, with plays and agents doing the qualification and routing.

## Implementation

- Setup time: A first working Play in a day for someone comfortable with APIs: connect a CRM, define a company model, add one enrichment step, and trigger on record change. A production deployment covering enrichment, scoring, routing, and CRM writeback more realistically takes two to four weeks, most of it spent agreeing on the data model rather than on the tool.
- Learning curve: Moderate to steep, and honestly so. The primitives are few but they assume familiarity with data modeling, idempotency, retries, and rate limits. The visual builder lowers the entry cost but not the conceptual one; teams without a technical operator tend to stall after the first workflow.
- Onboarding: Self-serve signup with 100 free credits and no payment method. Documentation is code-first and reasonably complete, with an open manifest template and a public library of reusable GTM agent skills to start from. A certified partner directory exists for teams that want implementation help, and higher tiers add priority or dedicated support.
- Migration: Because credentials for enrichment and LLM providers stay on your own accounts, moving those away from Cargo is straightforward. Workflow logic is the sticky part: a code-defined workspace lives in your repository and is at least readable outside the platform, which is more portability than a purely visual competitor offers, but the runtime semantics are Cargo's. Export model data before cancelling, and expect to rebuild rather than convert if you move to a different orchestration tool.

## Platform, API & security

- Platforms: Web application, cargo-ai command line interface, TypeScript CDK, Hosted Vite apps deployed alongside a workspace
- API: Code-first CDK with define calls for connectors, models, tools, workflows, agents, plays, and apps, deployed and reconciled through the cargo-ai CLI. Over 100 maintained connectors expose typed actions, and custom HTTP connectors cover anything not in the catalog. Inbound events and scheduled triggers drive Plays.
- Compliance: GDPR, SOC 2
- Data residency: Cloud-hosted; enrichment and LLM calls execute against your own provider accounts, so much of the sensitive processing stays on credentials you control.
- SSO: Single sign-on included on the Enterprise and Premium Enterprise tiers.
- Security notes: Connector credentials are held at the workspace level rather than embedded in individual plays or agents, which keeps agent access scoped to tools an administrator has granted. Every run produces a trace, giving an audit path for automated decisions that write back to the CRM.

## Support

- Channels: Community support on the free plan, Email support on Starter and Professional, Priority support on Enterprise, Dedicated support on Premium Enterprise, Certified implementation partners
- Documentation: Documentation is organized around the code path, with reference material for each define primitive, the CLI, and per-integration credit costs. It reads as engineering documentation rather than marketing collateral, which suits the audience but offers less hand-holding than a visual-first competitor's help center.
- Community: A partner network of certified implementers and an open-source presence including a workspace manifest template and a published library of reusable GTM agent skills. The independent review footprint on the usual software marketplaces remains small.

## Company

- Founded: 2023
- Founders: Aurelien Aubert, Maxence de Villepion
- Headquarters: San Francisco, California, with a team in Paris
- Ownership: Independent, venture-backed
- Employees: Small team (est. 2026)
- Funding: Y Combinator S23; a pre-seed round in 2023 followed by a Series A reported at roughly $19 million.

Timeline:

- 2023: Founded by Aurelien Aubert and Maxence de Villepion and admitted to Y Combinator's Summer 2023 batch, with a pre-seed round following.
- 2024: Visual GTM orchestration platform launches, positioned against spreadsheet-shaped enrichment tools for technical revenue teams.
- 2025: Series A reported at roughly $19 million; AI agents added as first-class steps inside workflows rather than a separate product.
- 2025: Primary domain moves from getcargo.io to getcargo.ai, with the old domain now redirecting.
- 2026: GTM as Code positioning ships: TypeScript workspace definitions, the cargo-ai CLI, hosted apps, and an open library of reusable GTM agent skills.
- 2026: Pricing published in full up to $3,000 per month with credit-based metering, no per-seat charge, and no feature gating between tiers.

## Integrations

HubSpot, Salesforce, Attio, Snowflake, BigQuery, OpenAI, Anthropic, Apollo, Clearbit, LinkedIn Sales Navigator data providers, Instantly, lemlist, Slack, Google Sheets, Webhooks and custom HTTP connectors

## FAQ

### What is Cargo used for?

Cargo runs the logic that connects a company's CRM, warehouse, enrichment vendors, and outbound tools. Teams use it to build shared data models for companies and contacts, then run workflows that enrich records, score them against an ICP, assign territories, hand off to a sequencing tool, and write results back to the CRM. AI agents handle the research and qualification steps inside those workflows.

### How much does Cargo cost?

There is a free plan with 100 credits a month. Paid plans start at about $165 a month for roughly 1,500 credits, about $250 for roughly 2,500 credits, about $1,190 for roughly 17,000 credits with SSO, and about $3,000 for 50,000 or more credits with dedicated support. Prices are quoted as from figures because credit consumption depends on which integrations you call.

### Does Cargo charge per seat?

No. Every tier is priced on credits rather than users, and there is no feature gating between plans apart from SSO and the support level. That makes it unusually friendly to agencies and to teams where one person builds workflows many people benefit from.

### What consumes a Cargo credit?

Three things. Integration tasks are priced individually by integration, since a contact-finding call and a research call cost different amounts. Orchestration steps are metered at roughly one credit per hundred steps. Storage upserts into Cargo models are metered at roughly one credit per thousand. Per-integration costs are visible in the workspace UI and from the CLI, which is the only practical way to forecast a month.

### Is Cargo the same as getcargo.io?

Yes. The product's primary domain is now getcargo.ai and getcargo.io issues a permanent redirect to it. Older reviews and directory listings still use the .io address. It should not be confused with unrelated companies using the Cargo name in freight and logistics or in biotech.

### Cargo vs Clay: which should we choose?

Clay is spreadsheet-shaped with a larger built-in enrichment provider catalog and is much easier for a non-technical growth marketer to get results from immediately. Cargo is code-shaped: TypeScript workspace definitions, a CLI, run traces, and agents that read and write a real data model. If the person doing the work is an engineer and you want scoring logic in version control, Cargo is the better fit; if it is a marketer who wants results this week, Clay usually wins.

### Does Cargo send emails or run sequences?

No. There is no native sequence builder. Cargo's SDR agent drafts personalized outbound, but sending happens in a connected tool such as your CRM's sequencer, Instantly, or lemlist. Budget for that tool separately when comparing total cost against an all-in-one outbound platform.

### Do I need to buy enrichment data from Cargo?

No, and you cannot. Cargo uses your own accounts with enrichment and LLM providers, holding the credentials at the workspace level. You pay those vendors directly, which means no data resale markup, but it also means the Cargo subscription is not the full cost of running a workflow.

### Can Cargo be used without writing code?

Partly. There is a visual builder over the same primitives, and simple plays can be assembled in it. But the platform assumes familiarity with data modeling, retries, and API behavior, and the code path is where most of the differentiation lives. Teams without a technical operator commonly stall after their first workflow.

### What does GTM as Code mean in practice?

A workspace is a folder of TypeScript files with one define call per resource: connectors, models, tools, agents, plays, and apps. The cargo-ai CLI plans a diff against the live workspace and deploys only what changed. The practical effect is that a change to lead scoring goes through a pull request, can be reviewed and reverted, and survives the departure of whoever wrote it.

### Is Cargo suitable for a small business?

It is buyable without a sales call, which many tools in this category are not, but the realistic entry point is $165 to $250 a month plus enrichment vendor spend and a technical person to operate it. A small business with a defined outbound motion and someone comfortable writing TypeScript can get real value; one looking for plug-and-play prospecting should look at a sequencing or enrichment tool instead.

### Who owns Cargo and how much funding has it raised?

It is an independent venture-backed company founded in 2023 by Aurelien Aubert and Maxence de Villepion, headquartered in San Francisco with a team in Paris. It went through Y Combinator's Summer 2023 batch, raised a pre-seed that year, and has since raised a Series A reported at roughly $19 million.

## Editorial verdict

Cargo is the most convincing attempt yet to treat go-to-market logic as software rather than as a canvas somebody maintains. The primitives are well chosen, agents sit inside workflows instead of beside them, run traces and typed tools make the thing operable, and the seat-free, feature-complete pricing is a genuine kindness in a category full of gated tiers. The costs are equally clear. There is no sequencing, the enrichment catalog is narrower than Clay's, credits are metered along three axes that resist forecasting, and the jump from the $250 tier to the $1,190 tier arrives quickly once plays run continuously. The deciding question is not budget but staffing: with an engineer who wants revenue logic in version control, this is a strong buy at a price a funded small company can absorb; without one, most of what makes Cargo different is out of reach and a spreadsheet-shaped competitor will get further faster.

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Source: SaaSTracker (https://saastracker.org), an independent editorial project. This profile is compiled from public information, carries no peer reviews or paid placement, and was last reviewed 2026-08-23. Awards are judged on published criteria: https://saastracker.org/methodology