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Dagger vs Falco

Dagger logo

Dagger

Developer Tools

Programmable CI/CD engine that runs your pipeline as containers on any runner

From
Free
Free plan
Yes
Full reviewWebsite
Falco logo

Falco

Cybersecurity

CNCF-graduated runtime threat detection for Linux and Kubernetes using eBPF

From
Free
Free plan
Yes
Full reviewWebsite

The short version

  • Each has a real cost: Dagger dagger is not a CI provider, so you keep paying for GitHub Actions or GitLab runners underneath it; the Dagger Cloud subscription is an addition to your CI bill, not a replacement for it.; Falco falco detects and alerts but does not block; stopping an attack requires wiring up Falco Talon or your own response tooling, so out of the box a confirmed detection still means a human intervening after the fact.
  • They diverge on capability: Dagger covers Pipelines as code, Falco covers eBPF kernel instrumentation.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Dagger and Falco actually diverge.

Attributes where Dagger and Falco differ
AttributeDaggerFalco
Pricing modelPer month per team for Dagger CloudOpen source, no licence fee
PlatformsLinux, macOS, Windows, Self-hostedLinux, Kubernetes, Self-hosted
CategoryDeveloper ToolsCybersecurity

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Dagger

  • Pipelines as code
  • Local and CI parity
  • Content-addressed caching
  • Dagger Functions and modules
  • CI-provider agnostic
  • Dagger Cloud traces
  • GitHub Checks integration
  • Container-native execution

Only in Falco

  • eBPF kernel instrumentation
  • System call rules engine
  • Container and Kubernetes context
  • Default rule set
  • Falcosidekick
  • Falco Talon
  • Plugins framework
  • DaemonSet deployment

What people use each for

The jobs each tool is most often brought in to do.

Dagger

  • A platform team maintaining near-identical build YAML across forty repositories that wants one versioned module every repository calls insteadnot Falco
  • An engineering organisation migrating off a CI provider that does not want to rewrite every pipeline as part of the migrationnot Falco
  • A team whose engineers waste hours pushing commits to debug CI-only failures and needs to reproduce the exact run locallynot Falco
  • A monorepo where most commits touch a small subset of services and cache-accurate step skipping cuts build minutes materiallynot Falco

Falco

  • A platform team that needs to know when a shell is opened inside a production container, which image scanning cannot detect because it happens at runtimenot Dagger
  • A regulated business required to evidence host and container intrusion detection on Kubernetes nodes for an auditnot Dagger
  • A security team wanting a vendor-neutral detection layer whose rules they can read and modify rather than a black-box agentnot Dagger
  • A cluster where a compromised dependency might write to sensitive paths or open unexpected outbound connections, and only kernel-level visibility will catch itnot Dagger

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Dagger

  • Dagger is not a CI provider, so you keep paying for GitHub Actions or GitLab runners underneath it; the Dagger Cloud subscription is an addition to your CI bill, not a replacement for it.
  • Writing pipelines in Go or TypeScript raises the barrier to entry: a release engineer who could edit a YAML step now needs to read code, and the people who can fix a broken build shrink to those comfortable with the SDK.
  • The engine runs as a container on the runner and needs a persistent cache volume to deliver its main benefit, so ephemeral CI runners give you correctness without the speed, and provisioning durable cache storage is extra platform work.
  • The Team plan caps at ten users and ten million events per month; an organisation of any size crosses that quickly and moves to Enterprise pricing that is not published, so cost at scale is unknowable up front.
  • The module ecosystem is young and thinly maintained relative to the marketplace of a mature CI provider, so integrations that exist as a one-line Action often have to be written yourself as a Dagger function.

Falco

  • Falco detects and alerts but does not block; stopping an attack requires wiring up Falco Talon or your own response tooling, so out of the box a confirmed detection still means a human intervening after the fact.
  • The default rule set is noisy in real clusters and generates a large volume of benign matches from normal operational activity; without weeks of tuning, alert fatigue sets in and the team stops reading the feed, which is the usual failure mode.
  • There is no storage, console, search or case management in the project, so a working detection capability means also running Falcosidekick, an event store, a dashboard and alert routing, all of which you build, host and maintain.
  • The modern eBPF driver requires kernel 5.8 or later; older hosts fall back to the legacy probe or the kernel module, which brings driver-building against kernel headers and the operational fragility that comes with it on every kernel upgrade.
  • Per-node syscall instrumentation carries measurable CPU overhead on busy hosts, and the cost scales with syscall volume rather than with cluster size, so the noisiest and most performance-sensitive workloads are exactly the ones that feel it most.

Pricing, plan by plan

Dagger

Free
  • IndividualFree
    • One user
    • One million events per month
    • Workflow logs and function call traces
  • Team$50/month
    • Up to ten users
    • Ten million events per month
    • Module insights and module catalogue
  • Enterprise$undefined/year
    • Custom user and event limits
    • SSO
    • Managed single-tenant deployment

Falco

Free
  • Falco (open source)Free
    • Apache 2.0 licence, CNCF graduated project
    • eBPF and kernel module drivers
    • Full rules engine and default rule set

Which should you pick?

Choose Dagger if

  • You need pipelines as code.
  • You want to start without paying.
  • You work on Linux, macOS, Windows, Self-hosted.
  • You also want local and ci parity.
Dagger review

Choose Falco if

  • You need ebpf kernel instrumentation.
  • You want to start without paying.
  • You work on Linux, Kubernetes, Self-hosted.
  • You also want system call rules engine.
Falco review

Questions people ask

Is Dagger or Falco better?
Neither clearly leads. Dagger starts at Free and Falco at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Dagger or Falco?
Dagger starts at Free and Falco at Free.
Does Dagger or Falco run on more platforms?
Dagger runs on Linux, macOS, Windows, Self-hosted. Falco runs on Linux, Kubernetes, Self-hosted.
Can I use Dagger for free?
Both have a free tier, so you can try either at no cost before committing.
What is Dagger best used for?
Dagger is most often used for a platform team maintaining near-identical build yaml across forty repositories that wants one versioned module every repository calls instead, an engineering organisation migrating off a ci provider that does not want to rewrite every pipeline as part of the migration, a team whose engineers waste hours pushing commits to debug ci-only failures and needs to reproduce the exact run locally, a monorepo where most commits touch a small subset of services and cache-accurate step skipping cuts build minutes materially. Of those, a platform team maintaining near-identical build yaml across forty repositories that wants one versioned module every repository calls instead and an engineering organisation migrating off a ci provider that does not want to rewrite every pipeline as part of the migration are not what Falco is typically brought in for.
What can Dagger do that Falco cannot?
Dagger covers Pipelines as code, Local and CI parity, Content-addressed caching, Dagger Functions and modules. Falco covers eBPF kernel instrumentation, System call rules engine, Container and Kubernetes context, Default rule set.

Answered from the vendors’ own pages

Dagger: Does Dagger replace GitHub Actions?

No. Dagger runs inside GitHub Actions or any other CI. It replaces the YAML that describes your pipeline steps, not the trigger and runner layer.

Falco: Does Falco block attacks?

No. It detects and emits events. Response requires Falco Talon or your own automation on top.

Dagger: Is the engine open source?

Yes, the Dagger engine and SDKs are open source and free. Dagger Cloud, the observability product, is what costs money.

Falco: Is Falco owned by Sysdig?

Sysdig created and open sourced it, but it graduated within the CNCF in February 2024, so governance sits with the foundation rather than the vendor.

Dagger: What is an event in the pricing?

Dagger Cloud meters pipeline telemetry events. One million per month is free; the Team plan at 50 USD per month allows ten million.

Falco: What does it cost?

The project is Apache 2.0 with no licence fee. The cost is the storage, routing, tuning and staff time needed to make its output useful.

Dagger: Can I run the same pipeline on my laptop?

Yes, that is the main reason teams adopt it. The dagger CLI executes the identical graph locally, so CI-only failures become reproducible.

Falco: What kernel version do I need?

Kernel 5.8 or later for the default modern eBPF driver. Older hosts need the legacy eBPF probe or the kernel module.

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