Topology-aware observability for production LLMs, RAG systems, and agentic workflows. Monitor costs, performance, compliance, quality, and behavior — from one AI-native platform.
14-day free trial. No credit card required. Limited to dev/staging environments.
We reduced our LLM monitoring overhead 40% in the first month. Finally, we can see where every token is going.— VP of AI Engineering, Enterprise SaaS Company
Your production LLMs, RAG systems, and agentic workflows are generating costs you cannot track, compliance gaps you cannot prove, and performance regressions you cannot diagnose. You are flying blind — and the board is asking questions you cannot answer.
GenAI inference costs are growing 50-100% month over month with zero visibility into where the spend is going. You cannot answer the most basic question: did you pick the right model? Is your prompt strategy efficient? Why is token usage 3x what it was last quarter?
See solutionYour LLMs are running in production, but you have no unified view of cost, latency, quality, or compliance. Semantic search is slowing. RAG retrieval quality is declining. Inference latency is creeping upward. And you are diagnosing with guesswork.
See solutionYour AI systems have no audit trails. Regulators are demanding accountability for GenAI use cases, and your data lineage is unclear. When the auditor asks how your LLM handles PII, you need a better answer than “we think it is fine.”
See solutionObservability for AI workloads requires different expertise than traditional infrastructure monitoring. Your team is skilled at debugging microservices — but when an LLM hallucinates or a RAG retrieval fails, they cannot tell if it is a model issue, a data issue, or an infrastructure issue.
See solutionThe five-perspective framework gives your team a topology-aware view of every AI workload — cost, performance, compliance, quality, and behavior — in a single unified platform built on Elasticsearch.
What It Monitors:
Token spend per model, API costs, inference cost-to-latency ratio, prompt efficiency metrics.
“Claude 3.5 is 2.3x more expensive per token but 1.4x faster — ROI-positive for latency-sensitive queries.”
Learn moreWhat It Monitors:
Inference latency, throughput, model response time, queue depth, cache hit rates.
“OpenAI API latency grew 40% at 2am — correlated with cache hit rate drop.”
Learn moreWhat It Monitors:
Audit trails, data lineage, PII detection, regulatory flags, data residency status.
“This RAG retrieval contains HIPAA-regulated data — flagged for review before inference.”
Learn moreWhat It Monitors:
Hallucination rates, semantic drift, output consistency, embedding quality scores.
“Hallucination rate increased 0.3% week-over-week — suggests training data shift.”
Learn moreWhat It Monitors:
Error rates, retry patterns, user feedback correlation, model drift indicators.
“Users downvote 12% of Claude responses as off-topic — suggests prompt refinement needed.”
Learn moreUnlike traditional observability tools that treat AI systems as black-box API calls, the LLM Observability Platform understands your complete LLM call topology: which application called which model, which vector database served which retrieval, and which compliance rule applies at each node. This topology awareness enables accurate cost allocation, targeted performance debugging, and compliance proof that stands up to audit.
Start collecting data in 15 minutes. See your first insights in 24 hours. Optimize your AI spend within 1 week.
Install the SDK (npm or pip) or configure your API key. The platform auto-detects your models, APIs, vector databases, and caches. Data collection begins immediately — no manual configuration needed.
View SDK DocsData flows into your Elasticsearch cluster in real time. The topology map auto-generates. Cost attribution begins. Your first insights appear within 24 hours: token spend by model, latency patterns, compliance checks — all without manual configuration.
See Dashboard DemoUse five-perspective insights to make data-driven decisions. Reduce unnecessary API calls with intelligent caching. Switch models based on cost-to-performance tradeoffs. Enforce compliance guardrails. Most teams identify their first optimization opportunities within the first week.
View Optimization GuideSchedule a 30-minute onboarding session with our engineering team. We handle setup, integration, and your first round of optimizations — so your team can focus on building.
Schedule Setup CallTraditional observability tools were designed for infrastructure metrics. Your AI workloads require a fundamentally different approach — topology-aware, AI-native, and built for the five dimensions that matter to production LLMs.
| Capability | LLM Observability Platform | Datadog APM | Splunk | New Relic | Honeycomb |
|---|---|---|---|---|---|
| Topology-Aware | Understands LLM call topology | Generic service topology | Log-based, not topology | Generic APM | Generic events |
| Cost Per Token | Token spend by model and provider | Traces and log volume only | Log volume only | APM traces only | Event volume only |
| Multi-Provider | OpenAI, Anthropic, Ollama, and more | Vendor-agnostic (generic) | Vendor-agnostic (generic) | Vendor-agnostic (generic) | Vendor-agnostic (generic) |
| Compliance Audit | Built-in audit trails | Requires custom configuration | Requires custom configuration | Requires custom configuration | Requires custom configuration |
| Hallucination Detection | LLM-specific quality metric | Not available | Not available | Not available | Not available |
| Five-Perspective View | Cost, Performance, Compliance, Quality, Behavior | Performance only | Logs and metrics only | Traces only | Events only |
| Setup Time | 15-minute SDK integration | 1-2 hours | 4-8 hours | 2-4 hours | 2-3 hours |
| LLM-Specific Alerts | Cost spike, hallucination rate, latency | Generic metrics only | Log pattern matching | APM baselines only | Custom threshold rules |
Their Strength: Broad platform coverage with transparent pricing across infrastructure, APM, and logs.
Datadog APM monitors infrastructure metrics. It was not designed for token-level cost tracking, hallucination detection, or LLM call topology. The LLM Observability Platform was built from the ground up for AI workloads — not retrofitted from infrastructure monitoring.
Their Strength: Deep log aggregation and search capabilities trusted by large enterprises.
Splunk gives you logs to parse. We give you cost-per-token by model, compliance audit trails by data flow, and hallucination rates by deployment — surfaced automatically, not buried in log queries.
Their Strength: Established APM platform with synthetic monitoring and broad ecosystem integrations.
New Relic monitors application performance. We monitor what matters to AI teams: token spend, model latency, compliance flags, hallucination rates, and behavioral drift — the five perspectives that generic APM does not capture.
Their Strength: Developer-friendly, event-based observability platform with strong community adoption.
Honeycomb is a powerful DIY toolkit. The LLM Observability Platform is a production-ready, elastic-first platform with enterprise deployment support, compliance playbooks, and a 24-hour SLA — reducing adoption risk and time-to-value from months to days.
Optimize faster with production-tested accelerators built to solve the most persistent operational challenges in AI observability: alert noise, incident response overhead, compliance burden, and storage costs.
ML-powered alert suppression that eliminates 80-90% of false positives. Your on-call engineers respond to real LLM issues — not noise.
Reduced alert fatigue 85%. Team satisfaction measurably improved.
See DemoLLM-powered automatic alert triage and remediation suggestions. Reduces mean time to respond by 50-70% and gives your team actionable next steps — not just alerts.
$200K saved in incident response overhead.
View Case StudyAutomated audit trail generation for SOC2, HIPAA, and PCI compliance. Prove AI system accountability to auditors with evidence, not explanations.
12-week path to audit readiness.
Schedule DemoIntelligent log sampling and cardinality reduction that cuts storage costs 50-70% without losing the signals your team depends on.
Reduced storage 60%. Saved $300K per year.
View ROI CalculatorAll four accelerators are included in SquareShift consulting engagements. Deploy individually or as a bundled suite — no additional licensing required.
Tell us about your environment. We will scope accelerators to your specific operational needs.
Production-ready compliance controls, audit trails, data residency options, and a 24-hour SLA. Built for organizations where security review is not optional.
Data masking, PHI detection, encryption at rest and in transit, and comprehensive audit logs. Deploy with confidence on healthcare AI workloads — with controls that satisfy compliance officers, not just engineers.
Learn moreFull SOC2 Type II certification — not just Type I. Your auditors get the depth of evidence they require. Certification documentation available on request.
Learn moreRole-based access control with admin, viewer, and auditor roles. Single sign-on via Okta, Azure AD, and Google Workspace. Your IT team controls who sees what — without custom configuration.
Learn moreEvery action logged — who did what, when, and why. Full data provenance tracking across your AI pipeline. When auditors ask for evidence, you have it.
Learn moreThe LLM Observability Platform supports fully on-premise deployment within your existing Elasticsearch environment. Zero data leaves your network. Your compliance team approves. Your security team sleeps.
Schedule Architecture ReviewStart free. Scale when you are ready. Every plan includes the five-perspective framework, topology-aware monitoring, and the support your team needs to succeed.
| Feature | Developer | Professional |
Enterprise |
|---|---|---|---|
| Price | Free | Custom Pricing | Custom Pricing |
| Best For | Dev/staging evaluation | Production, single application | Multi-app, on-prem, dedicated SLA |
| LLM Apps | 1 (staging only) | 1 | Unlimited |
| Team Size | 1 | 5 | Unlimited |
| Data Retention | 7 days | 30 days | 90 days |
| Data Residency | Multi-tenant cloud | Multi-tenant cloud | On-prem or VPC |
| Support | Community forum | Email (24-hour SLA) | Dedicated account manager + 24-hour phone SLA |
| Onboarding | Self-serve documentation | 1 guided session included | 5+ guided sessions included |
| SSO | Included | Included | Included |
| SLA Uptime | 99.5% | 99.9% | 99.95% |
| Audit Logs | Included | Included | Included |
No credit card required.
Talk to our team about production pricing.
Custom deployment. Custom pricing. 24-hour response.
Yes. Plan changes take effect at your next billing cycle. No penalties, no lock-in contracts.
Professional supports a single production application with email support. Enterprise supports unlimited applications, on-prem or VPC deployment, dedicated account management, and custom SLAs. Most teams with multiple AI workloads or regulated environments choose Enterprise.
Yes. Annual plans include a 15% discount for Professional and Enterprise tiers. Contact our team for details.
Each unique production application using LLMs counts as one app. Staging and development environments on the Developer tier are free and do not count toward your app limit.
Start with the Developer tier at no cost. When you are ready for production, our team will work with you on Professional pricing that fits your deployment. All demo and pricing requests are answered within 24 hours.
Upgrade to Enterprise for unlimited applications, on-prem deployment options, and dedicated support. Our team will scope the right plan for your environment.
Pricing shown is per month (monthly billing). Annual plans available with 15% discount. Enterprise pricing is custom based on deployment model (cloud, VPC, or on-prem), team size, and compliance requirements. All tiers include free usage data attribution for the first 30 days.
See how AI-native organizations use the LLM Observability Platform to take control of their AI systems — and prove the value to their boards.
Enterprise SaaS Company
SaaS / AI Infrastructure
Challenge: “Deployed 5 LLM models in production across 3 teams with zero visibility into costs, compliance status, or performance degradation.”
Solution: “Integrated the LLM Observability Platform in 2 hours. Enabled five-perspective monitoring across all models and teams.”
Answers to the most common questions from AI leaders evaluating LLM observability for production workloads.
The SDK adds less than 5ms latency per LLM call at the 99th percentile. For most production applications, this is imperceptible. We also offer an optional async tracking mode with zero latency impact and eventual consistency — for workloads where every millisecond matters.
View Performance BenchmarksYes. The LLM Observability Platform supports OpenAI, Anthropic Claude, LLaMA, Ollama, and custom models — all in one unified topology-aware view. Multi-provider support is a core design principle, not an afterthought. No vendor lock-in.
View Supported ProvidersYou control your data. The platform provides four levels of privacy: (1) data masking to strip PII before tracking, (2) on-prem deployment where zero data leaves your infrastructure, (3) data residency options across US, EU, and APAC regions, and (4) configurable data retention policies (delete after 30, 60, or 90 days). Choose the privacy model that fits your compliance requirements.
View Data Privacy GuideMost teams complete SDK integration in 15 minutes. First insights — cost attribution, latency patterns, compliance checks — appear within 24 hours. The full optimization cycle (identifying and implementing cost savings) typically takes 1-4 weeks depending on your stack complexity. Enterprise tier includes managed implementation for zero-friction onboarding. All implementation requests are answered within 24 hours.
View Implementation GuideDIY observability for LLMs typically requires 4-8 weeks of dedicated engineering effort and significant ongoing maintenance — debugging, feature updates, compliance changes, provider API updates. The LLM Observability Platform gets you to production visibility in 1-2 days, costs a fraction of DIY development, and includes compliance and security updates automatically. Most teams see positive ROI within the first month.
View TCO ComparisonNo specialized training required. The dashboard organizes around three intuitive views: Cost, Performance, and Compliance. The platform auto-generates dashboards on first data arrival — no manual setup. We also provide self-serve documentation, video tutorials, and a 30-minute guided onboarding session included with every plan. Most teams are productive on day one. And if your team has questions, our 24-hour SLA means they get answers fast.
View Documentation and TutorialsStill have questions?
Ask Our TeamTry free for 14 days. No credit card. No commitment. Demo available within 24 hours.
14-day free trial. No credit card required. Limited to dev/staging environments.
Enterprise deployment? White-glove onboarding available. Contact Sales