When AWS revealed Firecracker in 2018, serverless teams everywhere perked up. The promise was intoxicating: microVM isolation with container-like startup times. Since then, engineering blogs from Shopify, iRobot, and Fly.io have walked through gritty production stories—boot storms, density gains, compliance wins. At Cloudythings we have helped several enterprises bring Firecracker-backed serverless platforms online, often as internal FaaS offerings augmenting Lambda or Cloud Run. This post shares the architecture and SRE practices that made those launches successful.
Understand the “why” behind Firecracker
Firecracker shines when you need:
- Hard isolation boundaries for multi-tenant workloads (think payments or ML inference).
- Predictable cold starts with snapshotting and warm pools.
- Higher density than standard VMs without the kernel-sharing risk of plain containers.
Before touching code, we run a discovery workshop emphasizing security, compliance, and performance requirements. We cite AWS’s own Firecracker whitepaper, Chainguard’s distroless research, and the CNCF TAG Runtime reports to align stakeholders on trade-offs. If tenants can live with container isolation, Firecracker might be overkill. But when customers bring regulated workloads, the microVM story wins.
Build the image pipeline
Firecracker workloads boot from a custom kernel and root filesystem. Our pipeline (inspired by Weave Ignite and AWS Proton blog posts) looks like this:
- Buildroot or Bottlerocket-based rootfs with only the libraries your function needs. We inject OpenTelemetry exporters, Fluent Bit, and security agents.
- Custom kernel configuration enabling cgroups v2, BPF, and required device drivers. We sign kernels with Cosign to maintain supply-chain integrity.
- Image catalog stored in AWS ECR or Harbor with metadata tags (
runtime=python3.10,purpose=payments). Every artifact includes an SBOM generated by Syft. - Snapshot generation using Firecracker’s built-in snapshot API. We boot the microVM once, warm caches, and save the state for sub-50 ms restores.
We orchestrate builds with GitHub Actions or Buildkite, leaning on AWS Nitro Enclaves or HashiCorp Vault for signing keys. The entire process is GitOps-managed—no manual AMI tweaks.
Design the orchestration layer
You can run Firecracker via AWS Lambda, but many enterprises prefer self-managed platforms. We typically integrate:
- Firecracker Containerd or Kata Containers runtime classes for Kubernetes. Each function maps to a pod with a microVM runtime, benefiting from existing GitOps workflows.
- Nomad drivers for shops invested in HashiCorp tooling. Nomad handles multi-region scheduling, while Consul provides service discovery.
- Custom control planes (Rust or Go) when latency demands direct control. Fly.io’s open-source code offers great patterns.
Key capabilities:
- Warm pools & snapshots: A control plane maintains ready-to-go microVMs per workload. Scaling events simply attach to a snapshot and resume.
- Workload quotas: Tenants declare concurrency and memory budgets. Schedulers enforce them to prevent overconsumption.
- Ephemeral storage: Functions get 512 MB to several GB of scratch space via virtio-block devices. Anything persistent flows to S3, DynamoDB, or internal data services.
Wire in observability
Firecracker complicates traditional monitoring. We adopted practices shared by iRobot and Datadog:
- Host-level telemetry: Export node metrics (CPU steal, IO, context switches) to Prometheus. MicroVM density shows up as per-tenant dashboards.
- In-guest instrumentation: Functions emit OpenTelemetry traces and structured logs via sidecarless agents baked into the rootfs.
- Snapshot analytics: We track snapshot restore time, success rate, and time-to-first-byte per workload. Anything above 200 ms triggers investigations.
- Audit trails: Firecracker logs (API calls, jailer actions) stream to CloudWatch or Loki for compliance evidence.
We built Backstage plugins to show microVM performance per team, reducing support tickets.
SRE guardrails and incident drills
Firecracker foundations are only as strong as their operations playbook:
- Runbook automation: Scaling, draining, and snapshot rotation use idempotent scripts triggered via GitOps or ChatOps. No SSHing into hosts.
- Chaos experiments: Inspired by Netflix’s Failure Fridays, we inject snapshot corruption, host reboots, and cold-start storms. The system must auto-recover.
- Compliance mapping: SOC 2 and PCI require proof of isolation. We provide diagrams, signed artifacts, and audit log retention policies.
- Security posture: Distroless rootfs, seccomp enforcement, and cgroup isolation are documented and continuously tested.
Incidents tie back to error budgets. If Firecracker hosts exhaust capacity, the deployment pipeline halts until we remediate—mirroring the “freeze on burn-rate breach” philosophy we champion for progressive delivery.
Integrate with existing developer workflows
Developers do not care about microVM internals—they care about shipping features. We smooth the path by:
- Shipping CLI tooling (
ct serverless deploy) that packages code, selects runtimes, and triggers GitOps updates. - Providing local emulation via
igniteorfirecracker-containerdwith VS Code tasks. Developers debug against the same microVM image used in prod. - Automating tests: CI pipelines run unit tests in lightweight containers, then run integration suites inside real microVMs via TestContainers’ Firecracker support (an experimental feature inspired by community contributions).
- Offering golden templates: Terraform modules and Helm charts abstract networking, IAM, secrets, and observability wiring.
Developer experience is the lever that transforms Firecracker from infrastructure novelty into trusted runtime.
When to stay with managed offerings
Self-managed Firecracker is not always the answer. We stay with Lambda, Cloud Run, or Azure Functions when:
- Cold starts already meet SLOs.
- Tenants do not require strict kernel-level isolation.
- Platform teams lack the bandwidth to own hardware lifecycle and patching.
However, in regulated industries or high-density internal platforms, Firecracker repeatedly proves its value. The combination of deterministic images, warm pools, and transparent observability keeps SREs in control while developers enjoy serverless ergonomics.
Serverless and microVMs are no longer opposing forces—they are complementary. By borrowing from pioneers, codifying the practices in GitOps, and treating observability as a first-class feature, you can deliver Firecracker-backed platforms that balance velocity with uncompromising trust.