Use both. Here's where each one wins.
Observability tools see failures. Pisama acts on them. They are precondition layers; we are the action layer above. The comparison below is honest about where each one is stronger, not a zero-sum claim.
Pisama vs Galileo
Galileo is the enterprise GenAI observability suite that Cisco announced its intent to acquire on April 9, 2026, expected to close in Q3. The product is being folded into Splunk Observability Cloud. It is a credible category-defining incumbent backed by some of the largest distribution in observability.
Pisama is not trying to be Galileo. We do not ship a four-surface platform (Observe / Evaluate / Experiments / Protect), we do not run inline blocking guardrails, and we do not have an enterprise sales motion. What we do have is 87 detectors (6 externally validated at production grade) with per-detector F1 published, an open MAST-aligned taxonomy, and a benchmarked 59.9% on TRAIL.
The honest framing is that Galileo wins on breadth and distribution; Pisama wins on detector depth and reproducible calibration. They are different shapes of product.
- Four-surface suite: Observe, Evaluate, Experiments, Protect (inline runtime guardrails)
- Splunk Observability Cloud distribution after the Cisco close
- 20+ out-of-box metric families across Agentic, RAG, Safety, Multimodal, Text-to-SQL, and more
- Luna-2 SLM judges are published research with per-metric F1 tables (five eval metrics, 0.89 to 0.95), fast and cheap at scale
- OpenInference standard support, AGNTCY co-developer
- 87 detectors (6 externally validated at production grade) with per-detector F1 from a versioned calibration program; Galileo's published Luna-2 F1 covers five eval metrics on partly proprietary test sets, and its Insights failure-mode detection publishes no accuracy at all
- Behavior vs outcome, measured: on real production loops Pisama scored F1 1.00 where Galileo's agentic_workflow_success metric scored 0.00 (every loop passed as a success). Reproducible from the open repo
- TRAIL benchmark 59.9% joint accuracy (heuristic detectors only) vs 11.6% best frontier judge
- MAST-aligned taxonomy; reproducible calibration dataset (7,212 traces from 13 sources)
- Median trace cost under $0.01 (90%+ of detections resolve at T1 to T3 for free)
- Convergence detector: metric-aware failure detection. No surveyed competitor has this.
At a glance
| Dimension | Galileo | Pisama |
|---|---|---|
| Shape | Four-surface enterprise suite | Detector library plus diagnose API |
| Calibration | Luna-2 paper: F1 for 5 eval metrics, partly proprietary test sets | F1 per detector, versioned program, dataset open |
| TRAIL accuracy | Not published | 59.9% heuristic joint accuracy |
| Runtime guardrails | Protect: block, replace, human-route | Detect and surface; no inline blocking |
| Distribution | Splunk Observability Cloud (post-close) | Standalone API + framework adapters |
| Open detectors | Proprietary metrics | MIT, detector code in repo |
Externally validated at production grade: real-trace F1 0.80 or higher, precision 0.70 or higher, 30 or more external traces, external-grounded thresholds, and no per-difficulty blind spot (capability registry, external-only lane, 2026-06-14).
Benchmark note: the competitor LLM baselines we cite (for example 11.6% best frontier on TRAIL) were measured by Pisama in April 2026 against the published benchmarks, not self-reported by the vendors. Pisama's own 59.9% on TRAIL is the heuristic-only (Tier 1 to 3) result. Raw results are in the open-source repo.
Recommendation
If you are a Splunk customer running agents in production with enterprise compliance needs, the Cisco-Galileo path is the natural fit. If you want the calibrated detector layer that names which failure mode fired, with F1 numbers you can audit, run Pisama. The categories are not substitutes.
FAQ
- Does the Cisco acquisition change anything technical for Galileo customers?
- Cisco announced intent on 2026-04-09 with close expected in Q3 of Cisco fiscal year 2026. Galileo will become part of the Splunk Observability Cloud. The standalone Galileo product remains available during integration.
- Galileo publishes F1 numbers too. What is the difference?
- The Luna-2 paper publishes F1 for five evaluation metrics, measured partly on proprietary test sets. That is real disclosure and more than most vendors offer. It is a different artifact from a calibration program for failure-mode detection: Pisama publishes F1 for every production detector, on an open dataset of 7,212 traces with dataset fingerprints and a public changelog, including multi-agent failure modes. Insights, the Galileo feature that actually names failure patterns, publishes no accuracy numbers.
- Why not just use Galileo metrics if they cover more surfaces?
- Galileo metrics are evaluation metrics over outputs. Pisama detectors are process-level: they fire on trace structure (state recurrence, schema drift, cross-agent reference rate, persona similarity). These are different signals. Galileo plus Pisama is a real architecture; Galileo alone leaves the structural failures uncaught.
- Does Pisama plan to ship inline guardrails like Galileo Protect?
- Not in 2026. Pisama is a detection layer, not an enforcement layer. The detection signal can drive third-party guardrails (Forge, NeMo Guardrails, custom router middleware), and we publish detector outputs in a format those tools consume.