AEGIS
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Head to head · updated 2026-07

AEGIS vs Langfuse

These are different categories. Langfuse is the leading open-source LLM observability platform. AEGIS is a runtime firewall + compliance evidence layer. Buyers conflate them — this page pins down what's actually different and when you need both.

Written by the AEGIS team. Read the Langfuse docs and decide for yourself.

The 8 things buyers ask about first

Capability
AEGIS
Langfuse
Trace collection + span linkage
Both instrument LLM + tool calls and stitch parent/child traces.
yes
yes
Runtime enforcement (block / pending)
AEGIS gates the call before it fires. Langfuse observes it after.
yes
no
Cryptographic audit chain (Merkle)
AEGIS ships Ed25519-signed transparency log. Langfuse stores traces in Postgres (or ClickHouse) — no third-party-verifiable chain.
yes
no
Workflow-anchored policies
Langfuse has no policy DSL — enforcement is not in scope.
yes
no
Compensator / rollback
AEGIS ships a saga engine with binding-scoped compensators.
yes
no
Prompt versioning + eval harness
Langfuse-native strength. AEGIS records prompt/response but does not offer prompt A/B or LLM-judge eval as a first-class primitive.
partial
yes
EU AI Act evidence pack
Article 12-15 signed pack from AEGIS. Langfuse traces feed into your own compliance workflow.
yes
no
Air-gapped deploy
Both offer self-host. AEGIS ships NetworkPolicy in its Helm chart; Langfuse has a self-host guide but less-strict network isolation posture out of the box.
yes
yes

Where the two products split

01

Before vs after

The single most important difference. AEGIS runs in the request path — a tool call goes through the gateway before the LLM sees the result. Blocking a bad call means it never happens. Langfuse runs alongside — traces flow to it after the fact for analysis, but nothing gets intercepted. Firing an alert on an already-shipped hallucinated Stripe refund is not the same as blocking it.

02

Cryptographic evidence vs analytical evidence

Langfuse's Postgres traces are for you to analyze — filter, chart, backtest. AEGIS's Merkle-anchored log is for you to attest — Ed25519 signature over the chain, offline-verifiable against the gateway's published public key. If your auditor asks "did this decision happen and was the record untampered", AEGIS answers in one JSON download. Langfuse would require you to build the attestation layer.

03

Ship them together

Nothing stops you from running both. AEGIS's gateway can forward every decided call to Langfuse for product-analytics review while the AEGIS log holds the audit-of-record. That's the healthiest mature stack we've seen: AEGIS for runtime + compliance, Langfuse for prompt/eval iteration. Different jobs, different data models, complementary.

Full capability matrix

Capability
AEGIS
Langfuse
Trace collection + span linkage
Both instrument LLM + tool calls and stitch parent/child traces.
yes
yes
Runtime enforcement (block / pending)
AEGIS gates the call before it fires. Langfuse observes it after.
yes
no
Cryptographic audit chain (Merkle)
AEGIS ships Ed25519-signed transparency log. Langfuse stores traces in Postgres (or ClickHouse) — no third-party-verifiable chain.
yes
no
Workflow-anchored policies
Langfuse has no policy DSL — enforcement is not in scope.
yes
no
Compensator / rollback
AEGIS ships a saga engine with binding-scoped compensators.
yes
no
Prompt versioning + eval harness
Langfuse-native strength. AEGIS records prompt/response but does not offer prompt A/B or LLM-judge eval as a first-class primitive.
partial
yes
EU AI Act evidence pack
Article 12-15 signed pack from AEGIS. Langfuse traces feed into your own compliance workflow.
yes
no
Air-gapped deploy
Both offer self-host. AEGIS ships NetworkPolicy in its Helm chart; Langfuse has a self-host guide but less-strict network isolation posture out of the box.
yes
yes
Prompt playground UI
Langfuse ships a prompt/model playground. AEGIS scope stops at gateway + audit + compliance.
no
yes
LLM-as-judge eval built-in
AEGIS has an alignment/judge detector; Langfuse ships a fuller eval workflow with dataset management.
partial
yes
Dataset management + regression
Langfuse-native. AEGIS does not currently manage eval datasets.
no
yes
License
Both fully open-source. Both offer hosted paid tiers.
MIT (core)
MIT

Honest picking guide

Pick Langfuse when

  • Your #1 problem is prompt iteration — you need dataset management, prompt versioning, and LLM-judge eval as first-class.
  • You're pre-launch, no compliance surface yet, and want to instrument agents for product analytics.
  • Your team is small and one tool per problem is preferred over two.

Pick AEGIS when

  • You need to block a call before it fires, not observe it after.
  • An auditor will want a signed evidence artifact (SOC 2 / HIPAA / EU AI Act).
  • You need rollback for financial / PHI / deploy actions.
  • You want workflow-anchored policies — same tool, different node, different rule.

Or run both: gateway forwards decided calls to Langfuse for analytics review; AEGIS chain holds the audit-of-record.

Try the AEGIS playground.

Compile an English rule and evaluate a tool call — no signup, all client-side.

Open playground → Pricing