ServiceChainGraph builds a living, real-time graph of your entire infrastructure — from network devices and microservices to third-party API integrations and AI traffic flows. When anything breaks, you know exactly what breaks with it.
Live graph sync active — 847 nodes, 2,341 edges detected across 12 service clusters in real time.
What We Do
The Infrastructure Graph Platform Built for Complex Environments
Modern infrastructure is not a list — it is a graph. Services depend on services, APIs depend on APIs, and a single failed network hop can cascade through dozens of critical workflows. ServiceChainGraph ingests telemetry from every layer of your stack, constructs a continuously updated directed dependency graph, and gives your engineering and operations teams a single authoritative source of truth for understanding how your systems are connected, where risk concentrates, and what the downstream impact of any change or failure will be.
500k+
Nodes Mapped in Production
14ms
Median Impact Calculation
3,200+
Service Chain Templates
99.9%
Platform Uptime SLA
Core Capabilities
Everything You Need to Model Dependency Risk
Eight foundational capabilities — each designed to surface the information your teams need when a change, incident, or audit demands answers immediately.
Automated Dependency Discovery
ServiceChainGraph continuously monitors network traffic, API call logs, and service mesh telemetry to build and update your dependency graph without requiring any manual annotation. Every new service, integration, or device that appears in your environment is detected and mapped within seconds of its first network activity, giving your inventory an accuracy rate that static CMDB tools simply cannot match.
Blast Radius Modeling
Before you deploy a change, promote a release, or decommission a component, run a blast-radius simulation. The platform traces every downstream dependent — direct and transitive — and ranks them by impact severity, user exposure, and SLA criticality. Teams reviewing change requests gain a quantified risk score and a ranked impact list rather than relying on institutional memory or manually maintained runbooks.
AI Traffic Flow Analysis
Modern infrastructures increasingly route significant traffic through large language model APIs, embedding services, and GPU inference endpoints. ServiceChainGraph classifies AI-originated traffic separately, maps its path through your infrastructure, and surfaces latency anomalies, token-budget violations, and unexpected routing deviations. AI dependencies become first-class citizens in your graph, not invisible blind spots.
Critical Service Chain Detection
Not all paths through your dependency graph carry the same weight. The platform's critical-chain algorithm identifies the sequences of services where a failure would most severely impact revenue, user experience, or regulatory compliance. These chains are highlighted in the graph view, monitored with tighter alert thresholds, and included in automated incident briefings so that on-call engineers have immediate situational awareness.
Application Layer Mapping
Infrastructure graphs are only half the picture. ServiceChainGraph correlates application-layer spans with infrastructure nodes, mapping each microservice, monolith, or function to its precise physical and virtual runtime components. This correlation means that when an application team files an incident, the platform can immediately surface the underlying infrastructure path — and vice versa — eliminating the expensive coordination overhead between development and operations.
Third-Party API Integration Tracking
External dependencies are often the least-monitored links in any service chain. ServiceChainGraph automatically inventories every outbound API call your services make, tracks their latency and availability over time, and integrates with popular API management platforms to pull quota and rate-limit data. When a payment gateway, authentication provider, or data enrichment service degrades, your team sees the impact on your own services in context — not as an isolated alert.
Network Device Topology
Routers, switches, load balancers, firewalls, and VPN gateways are first-class nodes in the ServiceChainGraph model. The platform polls SNMP, ingests NetFlow data, and integrates with leading network management platforms to maintain an accurate physical and logical topology. Software-defined networking overlays, VLAN boundaries, and BGP route changes are reflected in the graph within the same sub-two-second update window as application-layer changes.
Integrated Monitoring & Alerting
ServiceChainGraph does not replace your existing monitoring stack — it enriches it. Native integrations with Prometheus, Datadog, Grafana, PagerDuty, and OpsGenie allow alerts to arrive with full upstream and downstream context from the dependency graph automatically attached. On-call engineers see not only that a service is degraded, but which dependencies may have caused it and which downstream services are already being affected, turning alert triage from minutes to seconds.
How It Works
From Raw Telemetry to Actionable Graph in Five Steps
The platform ingests, normalizes, relates, and surfaces — turning disparate signals from across your stack into a coherent, queryable dependency model.
1
Connect Data Sources
Install lightweight collectors or configure API-based integrations for your cloud provider, service mesh, network devices, APM tools, and log aggregators. No agents required for major cloud and SaaS platforms.
2
Ingest & Normalize
The ingestion pipeline processes millions of events per second, normalizes identifiers across heterogeneous naming schemes, and resolves service identities using DNS, process metadata, and container labels in real time.
3
Build the Graph
A streaming graph engine constructs directed weighted edges between every observed communicating pair. Edge weights encode latency percentiles, error rates, traffic volumes, and dependency criticality scores derived from historical behavior.
4
Score & Classify
Machine learning models run over the graph to identify critical paths, singleton dependencies (single points of failure), cyclical dependencies, and anomalous traffic patterns. Each node and edge receives a continuously updated risk score.
5
Explore & Act
Query the graph through an interactive UI, REST API, or GraphQL endpoint. Run impact simulations, generate change-request reports, trigger runbook automations, and export topology snapshots for compliance documentation — all from a single pane.
What's Included
A Complete Dependency Intelligence Stack
Every tier of the platform is purpose-built for the problems that arise when infrastructure complexity outpaces human memory.
Real-Time Graph EngineThe graph is never stale. A streaming pipeline processes ingested events and updates node and edge state within two seconds of observation, ensuring that any topology change — a new deployment, a failing node, a rerouted BGP path — is reflected before the next alert fires.
Historical Graph ReplayScrub the graph back to any point in time up to 13 months. Post-incident reviews and compliance audits can reconstruct exactly what the topology looked like when an event occurred, compare it to current state, and identify the precise topology change that preceded a failure.
Multi-Cloud & Hybrid SupportWhether your workloads run on AWS, Azure, GCP, or an on-premise data center — and in any combination — ServiceChainGraph's unified graph model spans all environments. Cross-cloud dependencies, transit gateway relationships, and hybrid VPN paths are resolved and visualized without requiring separate tools per cloud.
Policy-Based Impact RulesDefine custom impact scoring rules based on your organization's priorities: revenue exposure, regulatory classification, customer tier, or SLA tier. The platform applies these rules at query time so that impact rankings reflect your actual business context, not a generic severity heuristic.
GraphQL & REST API AccessThe full dependency model is available via a versioned GraphQL API and a REST API with webhook support. Platform teams embed graph queries directly into CI/CD pipelines, internal developer portals, and incident management tooling, so dependency intelligence flows to every workflow that needs it.
Plans & Packages
Choose the Level of Graph Depth Your Team Needs
Three deployment tiers — each with progressively deeper dependency modeling, larger graph scale, and more advanced impact analysis capabilities. All plans include direct phone and email support.
ServiceChainGraph Core
Designed for growing engineering teams that need accurate dependency visibility across a single environment or cloud account. Core gives you the full real-time graph engine with up to 5,000 nodes, automated discovery, blast-radius modeling, and seven days of historical graph replay. It is the right starting point for teams moving from static CMDB to live topology.
Up to 5,000 nodes; 25,000 edges; single cloud account
Built for mid-size engineering organizations managing multi-cloud or hybrid infrastructure. Pro unlocks up to 50,000 nodes across unlimited cloud accounts, 90-day historical replay, AI traffic flow analysis, third-party API inventory, and full GraphQL API access with webhook support. Most enterprise pilots start here before scaling to the Enterprise tier.
Up to 50,000 nodes; unlimited cloud accounts; multi-cloud
90-day historical replay; AI traffic flow analysis
Third-party API inventory; policy-based impact rules
GraphQL API + webhooks; PagerDuty & OpsGenie integration
Designed for large organizations managing hundreds of thousands of nodes across complex multi-cloud, on-premise, and edge environments. Enterprise includes unlimited scale, 13-month historical replay, a dedicated customer success engineer, custom integration development, on-premise or private-cloud deployment options, and SOC 2 Type II compliance documentation packages for regulated industries.
Unlimited nodes; on-premise or private-cloud deployment
13-month historical replay; SOC 2 Type II documentation
Dedicated success engineer; custom integration dev
The Operational Advantages of a Live Dependency Graph
Three distinct improvements your teams will feel within the first week of deployment — each backed by production data from current customers.
Incident Response
Cut Mean Time to Root Cause by More Than Half
When an alert fires, your on-call engineer needs to know two things immediately: what is broken and what caused it. Without a dependency graph, that answer requires manual correlation of logs, metrics, and tribal knowledge — a process that routinely takes 30 to 90 minutes. With ServiceChainGraph, the alert arrives with upstream dependency context already attached. Engineers see the probable cause chain, the affected downstream services, and the blast radius in the same notification. Customers routinely report MTTR reductions of 55 to 70 percent in the first month after deployment.
Change Management
Deploy with Confidence Using Pre-Flight Impact Analysis
Every change to production carries risk, but most teams lack a systematic way to quantify that risk before applying it. ServiceChainGraph's pre-flight simulation traverses the dependency graph from the target component outward, identifies every service that could be affected by a failure or behavior change, and produces a structured risk report that your change review process can consume directly. Teams have reported rejecting an average of one high-severity change per sprint that their previous process would have approved — preventing an incident before it happened.
Compliance & Audit
Answer Architecture Questions Instantly During Any Audit
Compliance auditors and security reviewers routinely ask questions that take weeks to answer manually: which services process payment card data, which services can reach the authentication store, which components have unreviewed external dependencies. ServiceChainGraph maintains the answers in real time. Point-in-time graph exports can be generated on demand and formatted to satisfy SOC 2, ISO 27001, HIPAA, and PCI-DSS inquiry templates. Your architecture review team stops writing one-off topology diagrams and starts serving audit requests from a live, always-accurate source.
Case Studies
Results from Production Deployments
Three engineering organizations that replaced manual dependency tracking with a live graph model — and the measurable outcomes that followed.
Global Payments Platform
Reducing P1 Incident Duration Across a 40-Service Payment Chain
Challenge
A mid-size fintech operating a payment processing platform had 40+ interdependent microservices spanning two cloud regions. When a P1 incident occurred, identifying the root cause required manual log correlation across 6 teams, averaging 74 minutes per incident. Frequent false escalations were burning out on-call engineers.
Solution
ServiceChainGraph Pro was deployed with integrations into Datadog, PagerDuty, and AWS VPC Flow Logs. Within 72 hours, a complete dependency graph of all 40 services was auto-generated, with critical payment chains highlighted and monitored under tighter alert thresholds. On-call alerts were enriched with upstream root-cause candidates and downstream blast-radius summaries.
68%reduction in mean time to root cause, from 74 minutes to 24 minutes, within 30 days of deployment
Enterprise SaaS Provider
Eliminating Surprise Outages from Third-Party API Dependency Failures
Challenge
A B2B SaaS company had grown its product to depend on 34 external API providers — payment processors, identity providers, enrichment services, and communication platforms. When any one of these providers degraded, the impact on internal services was invisible until customers filed support tickets. The operations team had no inventory of which internal services depended on which external APIs.
Solution
ServiceChainGraph Pro's outbound API inventory feature automatically discovered all 34 external dependencies within 4 hours of activation. Latency baselines were established for each. Webhook alerts were configured to notify on-call teams the moment an external API degraded, with an automatically generated impact map showing which internal services were affected and how severely.
91%of third-party API incidents now detected proactively before customer impact, compared to 0% previously
What Teams Say
From the Engineers Using It Daily
"
We had been asking "why is this service slow?" for months and the answer always required three different team members comparing three different dashboards. ServiceChainGraph showed us, on the first day, that it was a transitive dependency on an authentication service we did not even know our order service was calling. That single discovery paid for the subscription.
"
Our compliance team used to spend two weeks before every SOC 2 audit manually drawing service topology diagrams that were out of date by the time they were delivered. Now they export a point-in-time graph snapshot in about 30 seconds. The auditor review cycle dropped from four weeks to ten days.
"
The AI traffic flow analysis feature was the unexpected win. We started routing a significant volume of requests through an LLM inference endpoint and had no visibility into its impact on our latency budget. ServiceChainGraph surfaced it as a high-criticality dependency within hours of our first production deployment and prompted us to add a fallback path before we saw real user impact.
Integrations
Works with the Tools Your Team Already Uses
ServiceChainGraph connects natively to over 60 data sources and monitoring platforms. No custom exporters needed for the most common stacks.
Technical and commercial questions we hear from every evaluation team — answered directly so your team can make an informed decision quickly.
How long does initial dependency graph population take after installation?
For most environments, an initial graph that captures 90 percent or more of active service-to-service communication is available within 30 to 90 minutes of collector deployment, depending on traffic volume. A more complete picture including lower-frequency dependencies and network device topology typically stabilizes within 24 to 48 hours. The graph is queryable from the moment the first edges are observed — you do not wait for a batch process to complete.
Do we need to instrument our application code to build the dependency graph?
No. ServiceChainGraph infers dependencies from network-layer telemetry — VPC flow logs, eBPF-based packet inspection, SNMP polls, service mesh sidecars, and API call logs — without requiring any changes to application code. If your services already emit distributed traces (OpenTelemetry, Zipkin, Jaeger), the platform can ingest those as an additional enrichment signal to add span-level precision, but they are not required for the core graph to function.
What network overhead do the collectors introduce?
The primary collector uses eBPF for packet metadata capture and introduces less than 0.5 percent CPU overhead on modern Linux kernels (4.15+) under typical traffic volumes. All collected metadata is compressed and batched before transmission to the ingestion pipeline, keeping collector-to-platform network overhead well under 1 Mbps per host at 10,000 events per second. Collector configuration can be tuned to reduce sampling rates on high-volume, low-criticality paths.
How does the platform handle service identities across different naming schemes?
ServiceChainGraph maintains a multi-source identity resolver that correlates service names, Kubernetes labels, DNS hostnames, process names, container image names, and cloud resource tags into unified canonical identities. When the same logical service appears under different names in different data sources — for example, "auth-service" in Kubernetes but "authentication-v2" in your APM tool — the resolver links them automatically and presents them as a single node in the graph with all sources attached.
Can we run ServiceChainGraph in our own private cloud or on-premise data center?
Yes — the Enterprise tier includes both SaaS and private deployment options. The private deployment package ships as a set of container images deployable on any Kubernetes cluster (1.24+) and is architecturally equivalent to the SaaS offering. A deployment guide and a reference Helm chart are provided. Dedicated onboarding support from the Enterprise team is included to assist with network egress policy, storage sizing, and integration configuration in isolated environments.
How is the data retained and what are the data residency options?
In the SaaS deployment, graph metadata and edge telemetry are retained for the period defined by your plan tier (7 days, 90 days, or 13 months). Data is stored in the US-West-2 region by default. Enterprise customers can select EU, AP-Southeast, or bring-your-own-storage configurations. All data at rest is encrypted with AES-256 and in transit with TLS 1.3. A full data processing addendum (DPA) is available for customers with GDPR obligations.
What happens to the graph when a service is decommissioned?
When edge activity from a node drops to zero for a configurable quiescence period (default: 2 hours), the node is automatically marked as inactive and visually distinguished in the graph. It is not deleted — the historical record is preserved for replay and audit purposes. Active queries and impact simulations exclude inactive nodes by default, though this filter can be disabled. Administrators can manually archive nodes with a confirmation step that records the action in the audit log.
Is there a free trial period available?
Yes. All tiers include a 21-day fully featured trial with no credit card required. During the trial, graph scale is limited to 1,000 nodes to prevent excessive resource consumption, but all features — including blast-radius simulations, historical replay, AI traffic classification, and API access — are fully enabled at that scale. Most teams complete a meaningful evaluation of their environment within the first three to five days. Contact our sales team to arrange a guided onboarding call during your trial period.
Insights
From the ServiceChainGraph Engineering Blog
Technical writing on dependency modeling, infrastructure observability, and the engineering decisions behind how the platform works.
Engineering
Why We Use eBPF Instead of Sidecar Proxies for Dependency Discovery
Sidecar-based service meshes are powerful but come with a meaningful operational overhead: every service needs a proxy injected, proxy configuration must be managed as a separate concern, and the sidecar itself becomes a dependency. We explain the design choices behind our eBPF collector, the tradeoffs we accepted, and the performance benchmarks that informed the decision.
May 14, 2026 — 11 min read
AI Infrastructure
The Hidden Dependency Problem in AI-Native Applications
When your application routes traffic through an LLM inference endpoint, that endpoint becomes a critical dependency — but traditional monitoring treats it as just another outbound HTTP call. This post explores how AI traffic flows differ from conventional API calls, why standard latency alerting fails for token-budget-sensitive workloads, and how dependency graph modeling changes the picture.
April 29, 2026 — 8 min read
Change Management
Replacing Change Advisory Boards with a Graph-Driven Risk Score
The traditional Change Advisory Board process was designed for an era when infrastructure changed slowly and human review was the only available risk filter. In a world where dozens of deployments happen per day, CABs become bottlenecks. This post describes how three of our customers restructured their change approval workflows around automated graph-based risk scores — and what they gave up and gained in the process.
April 8, 2026 — 14 min read
Contact Us
Talk to a Dependency Graph Expert
Whether you are evaluating the platform, running a proof of concept, or ready to discuss a production deployment — our team is ready to help.
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