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    Syntropylabs vs Langfuse

    Their strengths as we found them in their documentation; ours as the flow book states them. Nine rows, three states.

    Langfuse is the open-source default in this space, and deservedly: MIT self-hosting with every core feature, a typed score model that covers human and automatic signals alike, timestamped dataset versions, an evaluator builder that previews matching volume and judge cost before a rule is enabled, and a free tier that gates almost nothing. Its tracing has no APM layer, and it has no simulation, sandboxed agent runs or red-teaming. Syntropylabs is a hosted service that traces the backend around the model call, turns coding-agent sessions into traces, and runs generated scenarios and attacks against your real agent.

    Feature table

    Cells about Syntropylabs cite the flow book that specifies the product (the F- and C- ids are its flow numbers); a partial is stated with its limit. Cells about the other product come only from our documented teardown of its public documentation, dated 2026-09-06, with the source linked below. Where that teardown is silent, the cell says “not documented here”, never “no”.

    Feature comparison: Syntropylabs versus Langfuse
    FeatureSyntropylabsLangfuse
    Tracing depth
    yes

    Trace waterfall, sessions, users, error groups, logs, and services → endpoint → request in one tool (F-TR-02, F-TR-04 to F-TR-07). Span cost is computed from the price sheet in the browser and shown as approximate (F-TR-02).

    yes

    Traces, Sessions, Users, Observations (ten types), Timeline and Agent graphs; environments as a first-class filter; OTLP ingestion mapping GenAI, OpenInference and MLflow attributes. No APM layer (services, endpoints, service map, latency histograms); the metrics API caps at 1,000 rows.

    Coding-agent tracing
    partial

    Claude Code, Codex, Gemini CLI, Cursor, Windsurf and OpenCode in one command; one trace per turn with subagents nested (C-INS-01). Cost arrives on a separate record merged onto the model call, so a lost record leaves a turn priced by the catalogue estimate; tool output for non-shell tools needs the CLI hook (F-TR-17).

    not documented here
    Evaluation runs
    yes

    Runs from a dataset with existing or generated outputs, per-row tri-state scores with judge reasons, compare against a baseline, gates, and SDK-created runs that open like any other (F-EV-01 to F-EV-07).

    yes

    Datasets and Experiments; a Dataset Runs table with aggregated scores, latency and cost; compare two runs with item-level deltas and annotate from the compare view; experiment-action posts PR comments. UI experiments always run the latest dataset version; no repetitions or trials.

    Online rules
    yes

    Per-environment rules with evaluators, judge, interval, sampling, filters and a daily budget that pauses the rule (F-EV-08, F-EV-09). Deterministic assertion rules attached to an online rule still run through the judge path (F-EVL-04).

    yes

    Evaluator + Rule: stacked filters and a sampling rate on incoming observations; matching volume from the past seven days and estimated cost shown before enabling; backfill; judge executions are traces in a reserved environment.

    Datasets and snapshots
    yes

    CSV and JSONL import, rows from traces, snapshots that runs pin to, and runs over time on the dataset (F-DS-01 to F-DS-03, C-DS-01). The snapshot diff shows counts and row references, not before/after values, and rows cannot be restored from a snapshot (F-DS-14).

    yes

    Dataset items keep sourceTraceId and an ACTIVE / ARCHIVED status; every item change creates a timestamped version pinned in get_dataset(version=); JSON-path field mapping with preview when adding from observations.

    Simulation
    yes

    No-code, code and connector agents; generated or hand-written scenarios; live runs; LLM-judge scoring; run comparison (F-SIM-01 to F-SIM-09). Deterministic scores carried on SDK-run spans are not rendered yet (F-SIM-10).

    no

    No simulation, no sandboxed agent runs, no scenario generation.

    Red-team
    yes

    Vulnerabilities × attack strategies on the sandbox runner, a risk report, and every materialized attack kept as a trace that can become a regression row (C-SIM-01).

    no

    No red teaming.

    Self-hosting

    Hosted at syntropylabs.ai. On-premise or VPC deployment is an Enterprise-plan line; there is no community self-hosted edition.

    MIT self-host with the same Postgres / ClickHouse / Redis / S3 stack as the cloud, free with all core features; the EE licence covers only RBAC, protected labels, retention, audit logs and SCIM.

    Pricing model

    Free: tracing for one project, 7-day retention, 10,000 spans a month soft cap, coding-agent traces included. Pro $20 a month: evaluation, datasets, simulation. Enterprise: custom. Upgrades are requested in-app; self-serve billing is not built (F-ORG-06).

    Hobby $0: 50k units/mo, 30-day access, 2 users, 1 annotation queue, 2 alerts. Core $29: 90 days, 3 queues. Pro $199: unlimited queues, retention management, SOC2. Teams add-on $300: protected labels, SSO, project RBAC. Enterprise $2,499: audit logs, SCIM. LLM-as-a-Judge, experiments, playground and dashboards are not gated.

    fig. 1 · nine rows, three states · reviewed 2026-09-15

    Sources for the Langfuse column

    Choose Langfuse if…

    • You want to self-host under an MIT licence with every core feature; we are a hosted service with on-premise only on Enterprise.
    • You want one typed score model (numeric, categorical, boolean, text; API, annotation or eval source) attachable to traces, observations, sessions and runs; ours is still several shapes for one concept.
    • You want to see seven-day matching volume and estimated judge cost before enabling an online rule; ours shows the daily budget, not a forecast.
    • You want the most generous free tier of the four: judge, experiments, playground and dashboards are ungated on Hobby.

    Choose Syntropylabs if…

    • You need services, endpoints, logs and error groups in the same tool as the LLM spans.
    • You want to generate scenarios, run them against your real agent in a sandbox, score the run with a judge, and red-team the same agent.
    • You want Claude Code, Codex, Gemini CLI, Cursor, Windsurf or OpenCode sessions as traces in one command, subagents nested where they ran.
    • You want per-trace Optimize with a paste-ready fix prompt, online-eval budgets and judge calibration built in.

    Next steps

    Try the tracing side first

    Tracing is free for one project on every plan, coding-agent traces included. Evaluations, datasets and simulation are Pro and up.

    Start freeRead the docsPricing