ABAXUS vs Lago — Two Self-Hosted Billing Approaches Compared
Both ABAXUS and Lago are self-hosted billing options. The difference is in the production architecture, support model, and what 'self-hosted' means in each case.
Last updated: 2026-04-09
The Shared Starting Point
ABAXUS and Lago are both self-hosted billing options for B2B SaaS companies with usage-based pricing. Unlike the comparisons with Metronome, Orb, or Stripe Billing, this isn’t a SaaS vs self-hosted comparison — both options keep billing data inside your own environment.
The relevant differences are in production architecture, support model, and what the self-hosted experience looks like at different scales.
What Is Lago
Lago is an open-source metering and usage-based billing platform. It is available as a self-hosted deployment (Docker Compose, Kubernetes) and as a managed cloud offering (Lago Cloud). The open-source version is MIT licensed. Lago is backed by Y Combinator and positions as the “Stripe Billing of open-source.”
ABAXUS is a commercially licensed self-hosted billing engine. It is not open-source. It is available as a self-hosted deployment on your Kubernetes cluster or via Docker Compose.
Architecture Differences
Dependency surface
Lago’s self-hosted stack includes: Rails application, PostgreSQL, Redis, Kafka (for event streaming), ElasticSearch (for event search), and AWS S3 (for PDF generation). This is a meaningful infrastructure footprint — multiple stateful services, each requiring monitoring, scaling, and operational ownership.
ABAXUS runs as a single Go binary backed by PostgreSQL. No Redis (added when measured as a bottleneck). No Kafka (PostgreSQL SKIP LOCKED queue handles the ingestion workload). No external search. No object storage dependency. Every dependency was deferred until measured performance data required it.
The operational difference: ABAXUS has one stateful service to run and monitor. Lago has five or more.
Idempotency model
Both platforms support idempotent event ingestion. ABAXUS makes idempotency mandatory — every event submission requires an idempotency_key. There is no path to submit an event without one. The ingestion endpoint returns 202 Accepted (never 200 OK) — events are durably queued before acknowledgement.
Lago supports idempotency keys but does not mandate them.
The mandatory approach eliminates one class of production bug: double-counted events when a client retries a request without an idempotency key.
Meter versioning
When pricing changes, historical invoices need to remain explainable under the rules that were in effect when the billing period closed.
ABAXUS meter definitions are time-bounded: when you update a meter, the old definition remains active for events with timestamps before the update timestamp. Historical invoices always compute under the rules that were in effect at the time of the events, not the current pricing configuration.
Lago handles pricing changes through plan versioning at the subscription level. Subscriptions can be assigned to specific plan versions, preserving historical pricing per customer.
Both approaches handle the problem, but the granularity differs: ABAXUS’s versioning is at the event-timestamp level; Lago’s is at the subscription-assignment level.
Audit trail depth
ABAXUS: Every invoice line item links to the raw events that generated it through SHA-256 checksums. A customer can verify their invoice against their own application logs independently.
Lago: Invoice line items, fee breakdowns, and event-level data available through the API and dashboard. Full audit capability with appropriate tooling.
Support Model
| ABAXUS | Lago (OSS) | Lago (Cloud) | |
|---|---|---|---|
| Support | Direct Slack + weekly architecture calls (early adopter) | Community forums, GitHub issues | Managed support tiers |
| SLA | By contract (Enterprise) | None | Varies by tier |
| Customization | Architecture review included | Community | Lago team |
| License | Commercial | MIT open-source | Commercial |
| Source access | No | Yes | Partial |
The practical difference for production deployments: ABAXUS includes direct engineering access during the early adopter program (Slack, weekly calls, roadmap influence). Lago open-source support is community-driven — response time depends on community activity and GitHub issue volume.
Production Readiness Trade-offs
| Dimension | ABAXUS | Lago OSS |
|---|---|---|
| Infrastructure footprint | 1 service (Go + PG) | 5+ services (Rails, PG, Redis, Kafka, ES) |
| Idempotency | Mandatory | Optional |
| Meter versioning | Event-timestamp level | Plan-version level |
| Audit trail | SHA-256 per line item → raw events | Event-level data via API |
| Operational complexity | Low (single binary) | Higher (multi-service) |
| Open source | No | Yes (MIT) |
| Community | Direct with team | Growing OSS community |
| Customization | Via architecture review + roadmap | Fork or contribute |
Pricing
| ABAXUS | Lago | |
|---|---|---|
| Open source | No | Yes (self-hosted) |
| Entry price | $12K/yr (Starter) | Free (OSS) |
| Growth | $36K/yr + early adopter 75% off | Lago Cloud pricing |
| Enterprise | Custom | Custom |
| Infrastructure | You run it | You run it (OSS) or Lago runs it (Cloud) |
Lago’s open-source path has no software license cost — you pay only for the infrastructure you run. ABAXUS has a commercial license cost but a lower infrastructure footprint (fewer services to run and monitor).
The total cost comparison depends on your engineering team’s time cost for operating a multi-service Lago deployment vs the ABAXUS license fee for a single-binary deployment. For teams optimizing for operational simplicity over license cost, ABAXUS typically has lower total cost of ownership.
When to Choose Each
Choose Lago when:
- Open-source license is a requirement (internal policy, budget, or preference)
- Your engineering team has capacity to operate a multi-service stack
- You want the ability to modify and fork the billing engine source
- Community-driven development and roadmap is acceptable
Choose ABAXUS when:
- Operational simplicity is a priority — single binary, one stateful service
- Mandatory idempotency at the ingestion level is required
- Direct engineering access (support, roadmap, architecture calls) is valuable during rollout
- Event-timestamp-level meter versioning is required for your pricing model change patterns
Related Reading
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