AI agent reliability platform, now part of Splunk (Cisco)
Galileo, now sold as Splunk Agent Observability, is an AI agent reliability platform for enterprise teams building and operating LLM applications and autonomous multi-agent systems.
AI Panel Score
6 AI reviews
Reviewed
AI Editor ApprovedApproved and published by our AI Editor-in-Chief after full panel analysis.Galileo AI, now sold as Splunk Agent Observability, is an AI agent reliability platform for enterprise engineering teams building and operating LLM applications and autonomous multi-agent systems. Cisco completed its acquisition of Galileo Technologies, Inc. on May 22, 2026, and the platform now carries the Splunk name. It combines offline evaluations, production monitoring, and runtime guardrails in a single platform so teams can detect, diagnose, and prevent failures in AI agents. Enterprise-tier Luna-2 small language models power evaluations at sub-200ms latency and roughly 98 percent lower cost than LLM-as-judge approaches, enabling 100 percent production traffic coverage. A free plan is available, and paid pricing starts at $100 per month with a custom Enterprise tier. Key capabilities include auto-tuned evals, an eval-to-guardrail pipeline, synthetic data generation, and live feedback integration. TopReviewed's six-seat AI review panel scored it 7.7/10, praising the full reliability surface of evals, monitoring, and guardrails while noting the $100 entry price signals enterprise positioning that can deter small teams. It fits engineering organizations scaling agent traffic past sampling-based observability.
In practice, teams instrument their AI agents and LLM applications using Galileo's SDK or OpenTelemetry-compatible integrations, then use the platform to run offline evaluations before deployment, monitor live production traces, and enforce runtime guardrails inline. An Agent Graph Visualization shows the full execution path of multi-step agents, while automated Signals surface anomalies and failure patterns — such as hallucinations, prompt injections, or infinite loops — before they escalate.
Distinctive platform capabilities include Agent Control, a runtime guardrail layer that blocks unsafe outputs in under 200ms and replaces the deprecated Protect product, and Signals, which proactively detects unknown failure modes in production without requiring teams to define every rule in advance. The platform's Luna-2 models are purpose-built for evaluation tasks, return a verdict in about 152ms on average, and are offered only on the Enterprise tier. Offline evaluation results can be promoted directly into production safety controls without writing additional code — a workflow Galileo calls turning evals into guardrails.
Cisco completed its acquisition of Galileo Technologies, Inc. on May 22, 2026, and the platform is now sold as Splunk Agent Observability inside Splunk's observability portfolio, aimed at enterprise AI engineering teams deploying autonomous agents in production. Named customers include Verizon, Comcast, HP, NTT, Five9, and ServiceTitan. Pricing starts at a free tier (up to 5,000 traces), a Pro plan at $100 per month billed yearly (50,000 traces), and an Enterprise tier with unlimited traces and private deployment options. It competes with Arize, Braintrust, LangSmith, and general-purpose ML observability tools.
The platform supports SaaS, on-premises, and in-VPC deployment and is SOC 2 Type 2 certified. Integrations cover major agent frameworks including CrewAI, LangGraph, OpenAI Agents SDK, Google ADK, LlamaIndex, and Amazon Strands, as well as an MCP server for use with AI-enabled IDEs like Cursor and VS Code.
Visualizes multi-agent workflows and interactions to give teams instant insight into how autonomous agents behave and interact.
Automatically and proactively detects unknown failure patterns in production AI agents before they escalate into incidents.
Purpose-built small language models that power evaluations at sub-200ms latency and 98% lower cost than LLM-as-judge approaches, enabling 100% production traffic coverage; available on the Enterprise tier only.
Runs pre-deployment evaluations of AI agents and LLM applications to catch issues before they reach production.
Ranks top AI agents based on real-world performance benchmarks and enterprise scenarios.
Benchmarks how well leading LLMs adhere to given context, measuring factual accuracy using Galileo's Context Adherence metric.
Continuously monitors production AI agent behavior and sends alerts when issues such as hallucinations or looping occur.
Provides insights, evaluation, and debugging tools to ensure the reliability of autonomous AI agents in production.
Integrates with agent frameworks such as CrewAI, LangGraph, OpenAI Agents SDK, Google ADK, LlamaIndex, and Amazon Strands via OpenTelemetry.
Connects Galileo to AI-enabled IDEs like Cursor and VS Code via the Model Context Protocol for in-editor evaluation workflows.
Blocks hallucinations, prompt injections, and data leaks in real time with sub-200ms response, turning evaluations into production safety controls; it replaces the deprecated Protect product.
Supports SaaS, on-premises, and in-VPC deployment models to meet enterprise security and compliance requirements, backed by SOC2 Type 2 certification.
Entry-level tier for individuals or small teams getting started with AI evaluation and observability
For growing teams needing higher trace volume and more robust evaluation capabilities; $100 per month billed yearly, scaling with trace count
For large organizations requiring unlimited scale, deployment flexibility, and dedicated support; the only tier that carries Luna-2 metrics, with custom pricing via contact sales
AI observability that survives the agent era — now inside Cisco's Splunk portfolio, real differentiation against LLM-as-judge.
“Founded 2021. Acquired by Cisco in May 2026 and now sold as Splunk Agent Observability. Luna-2 small models do evaluation in 200ms at 97% lower cost than LLM-as-judge. The agent reliability story is real and the timing is right.”
Founded 2021. Acquired by Cisco in May 2026 and folded into the Splunk observability portfolio as Splunk Agent Observability. Two signals that say this is real infrastructure with a balance sheet behind it.
Two things matter. One: as your team ships agents to production, you need to know when they fail. The LLM-as-judge approach costs $0.20-0.50 per evaluation — Galileo's Luna-2 small models claim 97% lower cost at sub-200ms. That math actually scales. Two: the runtime guardrails sit between detection and prevention, which is the right architectural place for a 2025 agent platform.
Pilot it on one production agent. Measure detection rate against your existing logging. If Galileo catches failures your engineers don't already see, you have a buy signal. If not, you're overpaying for a dashboard.
Sits between Arize on traditional ML observability and LangSmith on lightweight LLM tracing — defensible middle.
Cisco and Splunk are the credible board-level answer; category is new enough to flag in materials.
Connect a production agent, see eval results inside a day — fast time-to-signal for a platform purchase.
Production AI agent reliability is a budget line that didn't exist 18 months ago and is growing fast.
Founded 2021, acquired by Cisco in May 2026 — vendor-mortality risk is off the table for this layer.
Engineering teams running production AI agents that need failure detection at scale without LLM-as-judge cost.
You have one agent in beta and a small enough request volume that LangSmith free tier covers your needs.
Small-model evaluation at 200ms changes the architecture of production AI observability.
“Galileo's bet: judge models can be small if you build them for one task. Luna-2 at 200ms is the architectural move that makes 100% production traffic coverage actually possible.”
The architectural call is the entire bet. LLM-as-judge approaches — using a frontier model to evaluate frontier-model outputs — break down at production scale because the cost and latency compound. Galileo trains task-specific small models (Luna-2 family) that run sub-200ms at orders-of-magnitude lower cost. That's the right shape for evaluating 100% of traffic, not 1% sampling.
If we adopt this, in 3 years our agent observability is structurally different — every production response goes through evaluation, not a sampled subset. The lock-in lives in the trained custom evaluators, which are derivative of our prompts and outputs. Replaceable, but the labeling investment travels with the data.
Integration surface is REST plus SDKs in Python and TypeScript. Standard for the category. The runtime guardrails feature — blocking outputs at inference time — is a different engineering shape than the eval-only competitors and harder to replicate.
Sits ahead of LangSmith on production-scale evaluation, ahead of Arize on agent-specific patterns.
Maps to how production ML teams think about observability — sampling is a compromise, not a goal.
Python and TypeScript SDKs plus REST cover the standard ways agent code is written today.
100% traffic coverage at viable cost is the architectural shape every team will need by 2026.
Small-model task-specific evaluation is genuine ML engineering depth — not a wrapper around a foundation model.
Engineering orgs scaling agent traffic past sampling-based observability and ready for 100% coverage architecture.
You have a single agent in light traffic and observability is not yet a real architectural concern.
$100/month entry, enterprise on contact-sales — but the 97% cost claim against LLM-as-judge is the real number.
“Galileo's pricing isn't the headline — the cost it replaces is. LLM-as-judge at scale runs $50K+/year per agent; Galileo enterprise replaces that for a fraction.”
Starter tier: $100/month. Enterprise on contact-sales — assume $25K-100K/year band based on category norms.
The replacement math is the case. A team running 10M evaluations/month via a frontier-model judge at $0.005/eval = $50K/month. Galileo Luna-2 at the claimed 97% reduction = $1K-2K/month for the same coverage. Year 1 savings on a single high-traffic agent: $400-500K. Compare LangSmith which doesn't aim for 100% production coverage — different category, different math.
The risk: Luna-2 evaluator quality vs GPT-4 judge quality is the open question. False negatives in production cost real reputation. Pilot with shadow eval against an LLM judge for 30 days. Compare disagreement rate. If under 5%, the cost case is clean. If higher, the cost story breaks.
Starter tier credit card; enterprise tier follows standard procurement motion with sales involvement.
Self-serve starter tier; enterprise contracts assumed annual with volume-tier eval pricing.
Entry tier price is published; enterprise pricing is contact-sales — category norm but not transparent.
Cost replacement math is direct; quality-equivalence claim requires shadow-eval validation in production.
Replaces a much larger LLM-as-judge bill at scale; net TCO is dramatically lower for high-traffic agents.
Companies running 1M+ monthly agent evaluations where LLM-as-judge cost has become a real budget line.
Your evaluation volume is small enough that LangSmith free tier or self-built scripts still work.
Sub-200ms eval latency means you can finally evaluate every production response, not a sample.
“Day three you're wiring the SDK into your inference pipeline. Day thirty the dashboard is the daily-standup screen for agent reliability.”
The Python SDK ships clean — typed, idiomatic, the right shape for inserting into an existing FastAPI inference layer. Day three you're instrumenting calls and watching evaluations flow into the dashboard. Compare LangSmith: the SDK is fine, but the eval is sampling-based — different design shape entirely.
Day-thirty fight is the false-positive rate. Out-of-the-box Luna-2 evaluators flag too many borderline outputs as 'ungrounded' or 'off-topic' until you tune them on your team's labeled examples. That's 2-3 weeks of work, and it's not glamorous.
The runtime guardrails feature is the practitioner-relevant unlock. Blocking a hallucinated tool call at inference time is the difference between a customer ticket and a non-event. The 200ms latency budget makes that actually viable in production. Documentation is engineer-shaped — code-first, with real fastAPI and Modal examples.
SDK integration is clean; instrumentation lands in a day; dashboard becomes useful within a week.
Real code examples, FastAPI and Modal-specific guides — written by engineers running production AI.
Out-of-box false-positive rate on small-model evaluators requires 2-3 weeks of team tuning.
Custom evaluator training plus runtime guardrails plus offline evals scale from prototype to production.
Python and TypeScript SDKs slot into standard inference pipelines without architectural changes.
ML engineers running production agents who need 100% eval coverage and willing to spend two weeks tuning evaluators.
Your agent is in beta with sampled traffic and a print-statement-based observability story still works.
AI agent observability built for teams that have already shipped — not for the demo-stage crowd.
“The product makes sense once you have a real agent breaking in production. Before that, the value is theoretical and the price is real.”
Galileo isn't for the team building their first agent. The whole product assumes you already have something in production that you're afraid will fail at 3am. If you're past that line, the dashboard finally gives you the metrics you've been faking with print statements.
The Luna-2 small models claim — sub-200ms evaluation at 97% lower cost than LLM-as-judge — sounds like marketing. Until you do the math on a 10M-eval/month agent and realize the LLM-as-judge bill alone is $50K. Then it stops sounding like marketing.
The friction is the tuning. Out-of-box evaluators are too sensitive — they'll flag perfectly fine responses as 'ungrounded' for the first two weeks. That's normal for this category, not a Galileo-specific issue. Compare LangSmith, which mostly avoids the problem by sampling — different tradeoff. $100/month entry is fair for a tool that earns its keep at scale.
Dashboard is functional and engineer-shaped; not as visually polished as Arize but readable under pressure.
First hour is fine for engineers, week two is where the eval-tuning work pays off.
Web dashboard works on mobile but the workflow is desktop-shaped — debugging at scale is laptop work.
SDK setup is fast for engineers; first-week eval noise makes the early experience feel busy.
Evaluations land consistently; latency claim holds up; dashboard refreshes feel real-time.
Engineering teams past first agent into production traffic who feel the LLM-as-judge cost line.
Your agent is in pre-launch and observability concerns are still theoretical for your team.
Real engineering, real market, new owner: AI observability consolidated and Galileo landed inside Cisco.
“Galileo has the technical depth and now Cisco's balance sheet. Arize has the head start. LangSmith has the developer mindshare. Three vendors, one survives at scale.”
Three green flags. Cisco bought the company in May 2026, so the vendor-risk question is answered. A real ML engineering bet — task-specific small models, not a wrapper. Documentation that shows the product team has shipped production AI before.
Two yellow flags. The AI observability category is crowded — Arize, Patronus, LangSmith, Comet ML, Weights & Biases all extending into this space. Galileo is the one that got acquired — the rest of that list is still in play. Galileo's differentiation is the small-model eval architecture, but Arize can ship the same approach if the market demands it.
The other yellow: production AI agent observability is a 24-month-old category. The shape of the buyer hasn't fully crystallized — is it the platform team, the ML team, the data team? Galileo has bet on platform-team buyer; under Splunk the GTM motion now runs through an observability sales team. Founded 2021. Time will tell.
Small-model evaluation architecture is real differentiation; Arize and LangSmith can copy if market demands.
Evaluator training data is yours, but custom-tuned Luna-2 evaluators are Galileo-specific artifacts.
Funding question is settled by Cisco ownership; the category is consolidating around large observability vendors.
Cost-comparison and latency claims hold up under scrutiny; positioning is direct without category-savior tone.
Shipping cadence matched early-survivor patterns; founded 2021 and acquired by Cisco in May 2026.
Teams comfortable buying an eval layer that now ships under Splunk in exchange for the strongest small-model eval architecture.
You need a category-leader pick today and would rather wait for shakeout to be obvious.
Common questions answered by our AI research team
The Free plan is $0/month and includes 5,000 traces per month, unlimited users, and unlimited custom evals — built for developers and small teams who want to experiment and iterate.
The Pro plan includes 50,000 traces per month, with pricing that scales based on trace volume.
Enterprise supports Hosted, VPC, or on-prem deployment, along with low-latency dedicated inference servers.
Luna-2 small language models, offered on the Enterprise tier, power evaluations at sub-200ms latency, at 98% lower cost than LLM-as-judge approaches, enabling 100% production traffic coverage.
Yes, Enterprise includes enterprise-grade security with RBAC and SSO, plus a dedicated CSM and real-time guardrails.
Company
Galileo.AIFounded
2022Pricing
From $100/moFree Plan
Available
Galileo's AI observability and evaluation platform empowers AI teams to evaluate, monitor, and protect GenAI applications and agents at enterprise scale.
