Nobl9 AI is an SLO reliability platform for SRE and engineering teams, officially positioned as "The reliability trust layer for AI-built software". It embeds reliability management into AI-coded workflows through AI-driven SLO discovery, error budget alerts, composite SLOs, SLO Oversight governance, and MCP server integration, enabling services to have observable reliability coverage from day one.

Nobl9 AI’s Reliability Trust Layer: When AI writes code, who guarantees reliability?

Core parameters and statistics

Nobl9 AI is an SLO reliability platform for SRE (Site Reliability Engineering) and platform engineering teams. It is officially positioned as "The reliability trust layer for AI-built software". It builds a unified SLO management layer on top of the existing monitoring stack, enabling teams to transform observable signals scattered across monitoring tools into customer experience-focused reliability metrics.

Projects	Public Information
Official positioning	The reliability trust layer for AI-built software
Product form	SaaS platform (multi-tenant cloud + single-tenant SaaS + self-hosting optional)
Core capabilities	AI SLO Discovery, SLO Oversight, error budget alarm, composite SLO, SLOs as Code
Data source integration	Datadog, Prometheus, New Relic, Dynatrace, Amazon CloudWatch, Grafana, and more
AI ecosystem integration	Access Claude, Codex (OpenAI), Cursor, GitHub Copilot, and Gemini through MCP Server
Customer industries	Finance, technology, e-commerce, manufacturing (Ford, OutSystems, Flexera, LIT, etc.)
Team size	American company, GitHub organization 25 followers, 43 public repositories
Latest version	2026.07 (continuous SaaS iteration)
Supported platforms	Web, API, CLI (sloctl)

Deployment form: Nobl9 uses SaaS as the main delivery method, and also provides single-tenant SaaS and self-hosted options for enterprises with high compliance requirements. For teams that want to verify first, Sandbox is officially provided, and you can experience the entire SLO management process without configuration.

Data source neutrality: The platform does not replace existing monitoring tools, but serves as an SLO abstraction layer on top of them. This means that teams that already have Datadog/Prometheus deployed don't have to "reinvent the wheel", Nobl9 directly reads the existing metric sources and maps them to SLOs.

AI integration depth: The introduction of MCP Server turns Nobl9 from a "passive dashboard" to an "Agent-readable runtime state layer". The AI ​​Coding Agent can query the service's SLO status and error budget burn rate while generating code, which is a key difference from traditional SRE tools.

User and market recognition

Nobl9’s market recognition mainly comes from enterprise-level customer adoption, industry awards and ecological integration, rather than public revenue or active user numbers (the latter is not officially disclosed).

Enterprise customers: Customers displayed on the official website include Ford (Ford Motors), OutSystems (low-code platform), Flexera (software asset management), LIT, Cincom, ANZ Bank, etc., covering the automotive, finance, technology and manufacturing industries. This customer distribution shows that Nobl9’s SLO solution has entered the purchasing list of medium and large enterprises.

Industry Recognition: Nobl9 has been selected into the CRN Cloud 100 list nominated for the CRN 2024 Technology Innovation Award, and has been recognized by the Enterprise Management Associates (EMA) research report. These third-party endorsements provide a credibility reference independent of official website promotion.

Open Source Ecosystem: GitHub organization nobl9 has 43 public repositories. Core open source projects include:

• sloctl (39 stars): CLI tool that supports code-based creation and management of SLOs
• terraform-provider-nobl9 (26 stars): Terraform Provider that brings SLOs into Infrastructure as Code (IaC) pipelines
• nobl9-go (29 stars): Go SDK for programmatic manipulation of Nobl9 resources
• nobl9-backstage-plugin (21 stars): Backstage developer portal plugin that enables SLOs to be directly embedded into internal developer platforms
• govy (52 stars): Go verification library based on generics, used by multiple Nobl9 projects
• ekg (83 stars): Essential Kubernetes Gauges, K8s basic indicator export tool

Prerequisites for implementation: SLO-driven reliability management has threshold requirements for team capabilities. Teams need to already have basic monitoring coverage in place (at least capturing SLI metrics such as latency, error rates, throughput) and have an SRE or platform engineering role driving standardized definitions of SLOs.

Cost advantage

Nobl9’s cost structure adopts a tiered model with free C-side trials and enterprise subscriptions based on SLO units. It is broken down from three levels below:

C-side/individual

• Sandbox free trial: The official Sandbox environment does not require registration, and you can experience the entire SLO management process, but it is not used for production.
• Individual/Small Team Startup: There is no public permanent free plan, the minimum threshold is Teams Edition ($14,950/year), suitable for small-scale pilots.

Developer/API

• API Calls: Nobl9 provides REST API, sloctl CLI and Go SDK, API calls are included per platform subscription, there is no separate API pricing tier.
• MCP Server: MCP integration is available to all paid plan users at no additional cost.
• Terraform Provider: Open source and free to use for bringing SLOs into IaC management.

Enterprise/Privatization

• Teams Edition: $14,950/year, includes 50 SLO units, 10 data sources, 2-year data retention for all alarm methods, and work order support.
• Professional Edition: Contact Sales for pricing, includes 200 SLO units, unlimited data sources, dedicated support SSO, custom onboarding.
• Enterprise Edition: Contact sales for pricing, includes 500 SLO units, usage protection credits (minimum spend required), custom MSA + volume discounts 24x7 support + dedicated SLO expert, single tenant and high availability option SCIM sync.

Hidden Cost: The way SLO units are calculated needs to be noted - each SLO can contain multiple objectives (objectives), and each objective is counted as one SLO unit. For example, an SLO with 3 targets consumes 3 SLO units. This means that if there are a large number of services and multi-dimensional goals are defined for each service, consumption may occur faster than expected.

Hidden cost comparison:

Cost dimension	Self-built SLO system	Nobl9 Teams	Nobl9 Enterprise
Initial setup	Several man-months of engineering research and development + Prometheus/Thanos and other infrastructure	Zero setup, SaaS ready to use	Single-tenant deployment requires a small amount of configuration
Operation and maintenance costs	Continuous maintenance of alarm rules, dashboards, and data pipelines	Platform maintenance is handled by Nobl9	Single tenants require coordinated operation and maintenance by a team
SLO governance cost	Requires self-developed review process, ownership management	SLO Oversight built-in governance workflow	Same as above + dedicated CRE support
Scaling costs	Manual configuration required for each additional set of SLOs	Billed by SLO unit, flexible and predictable	Volume discounts + usage protection credits

Main functions

• AI SLO Discovery: Nobl9's AI layer automatically generates SLIs (Service Level Indicators), target values and error budgets for services through natural language dialogue with existing telemetry data, along with reasoning logic for each recommendation. Engineers can describe service behavior in natural language (such as "This is a payment processing service and users expect 99.9% of requests to be completed within 2 seconds"), and the AI ​​will output the corresponding SLO definition. This capability solves the engineering pain point of "defining SLO from scratch is too difficult" - official data shows that traditional manual definition of SLO takes weeks, while AI Discovery can shorten the first definition to minutes.

• SLO Oversight (SLO Governance Dashboard): Automatically identify "zombie SLOs" (not updated for a long time), "burning SLOs" (error budgets are being consumed rapidly), and indicator fluctuations caused by data anomalies. The agentic layer reads running status in real time, flags SLOs that require manual intervention, and explains the root cause of budget burn. Example: In the official measured scenario, SLO Oversight automatically detected an anomaly with a burning rate of 285.7×, and located the root cause of the abnormal increase in the response body from about 47KB to 157KB.

• Error Budget Alerting: Triggers alerts based on error budget burning rate rather than fixed thresholds, and supports integration with PagerDuty, Slack, OpsGenie, VictorOps, Webhook and other tools. Preset multiple burning rate alarm strategies (fast burning, slow burning) to avoid alarm fatigue where "every alarm is a false alarm".

• Composite SLO: Combine multiple basic SLOs into a top-level SLO based on user influence weight, suitable for full-link service dependency scenarios. For example, an e-commerce ordering process involves the front-end, payment gateway, inventory service, and notification service. The composite SLO can aggregate the reliability of each sub-service into a "order success rate" business-level SLO. The weight is adjustable to reflect the actual impact of different sub-services on user experience.

• SLOs as Code: Incorporate SLO definitions into GitOps workflows through the sloctl CLI, Terraform Provider, OpenSLO-compatible formats, and GitHub Actions. SLOs are stored in the code base as YAML files and are automatically synced to the Nobl9 platform after PR review. This solves the problem of "disconnection between configuration and code" in traditional SLO management - when services change, SLO definitions can be reviewed and deployed together with the code.

Hidden synergy: AI SLO Discovery + SLOs as Code + MCP Server The three form an "AI closure" - the AI ​​coding agent reads the SLO status of existing services through MCP, generates SLO definitions through AI Discovery when creating new services, and then submits SLOs to the warehouse in code form through sloctl/Terraform. The entire process does not require manual opening of the dashboard, and reliability coverage grows simultaneously with the code.

Model and version evolution

Nobl9 is delivered continuously in SaaS form without the concept of "large version number". The following is a summary of the version history based on publicly released milestone nodes:

2025.11: AI SLO Discovery Beta

• For the first time, AI-driven SLO definition generation function is introduced to support natural language interaction
• Verify the rationality of SLO definition based on existing telemetry data playback
• Beta phase is limited to some customers.

2026.02: MCP Server and AI Agent integration

• Release Model Context Protocol (MCP) Server so that AI coding agents such as Claude, Codex, Cursor, Copilot, and Gemini can directly read SLO and error budget status
• All MCP operations are designed to be idempotent and efficient to avoid Agent falling into redundant calls and Token costs getting out of control
• Simultaneously released major updates to sloctl CLI to enhance SLOs as Code capabilities

2026.05: SLO Oversight officially released

• Launched SLO governance dashboard to automatically detect expired SLOs, missing ownership and burn budgets -Introducing the Agentic interpretation layer to automatically locate the root cause of error budget burning
• Published SLO Framework best practice guide and automated review workflow

2026.07: The platform continues to iterate

• Enhanced weight configuration and visualization of composite SLO
• Expanded data source integration list (continuously increasing)
• Optimize Sandbox experience and enterprise-level management functions

Release Features: Nobl9's version rhythm is "feature-driven" - launching an important capability module every 2-3 months instead of a fixed calendar version. There is no commitment to destructive changes between versions, SaaS upgrades are automatically completed by the platform, and users do not need to migrate manually.

Technical advantages

Data source abstraction and SLO engine: Nobl9’s core technology is to uniformly convert indicators from different monitoring systems (Datadog’s metrics, Prometheus’ queries, CloudWatch’s logs, etc.) into SLI indicators, and then calculate error budgets based on predefined or AI-generated target values. This "Metric -> SLI -> SLO -> Error Budget" pipeline is a multi-layer calculation engine that supports data backfilling (Backtesting), query delay (Query Delay) to ensure SLI consistency, and data export (Export) to AWS S3 or GCS for long-term archiving.

Inference mechanism of AI SLO Discovery: AI SLO Discovery is not a simple large model prompt word template. It combines (1) historical analysis of service telemetry data, (2) a rules engine for SRE best practices, and (3) the natural language understanding capabilities of a large language model. AI first "interviews" engineers to describe service behavior, then verifies the rationality of the definition against existing indicator data, and finally outputs SLO recommendations with a reasoning chain and marks potential edge cases (such as error budget fluctuations during burst traffic).

MCP integrated Agent efficiency design: Nobl9's MCP Server is specifically optimized for AI Agent usage scenarios - each operation is idempotent (multiple calls to the same query will not have side effects), the response data is compressed and tailored to avoid context overload, and redundant call detection is implemented to prevent the Agent from falling into an infinite loop. Tool behaviors exposed by Server include read-only operations such as query_slo, get_error_budget, list_alerts, and write operations such as create_slo and update_slo (the latter relies on platform RBAC permission control).

Multi-layer interface of SLOs as Code: From bottom to top, they are (1) REST API - programmatic access in any language, (2) Go SDK (nobl9-go) - deep Go ecosystem integration, (3) Terraform Provider - infrastructure as code, (4) sloctl CLI - daily SLO operation and maintenance, (5) Backstage Plugin - developer portal embedding. This layering allows different roles (SREs, platform engineers, developers) to operate SLO in their own customary way without having to learn the same set of interfaces.

Architecture Link:

AI Coding Agent (Claude/Codex/Cursor)
    │
    ├── MCP Protocol ──► Nobl9 MCP Server ──► SLO query/update
    │
    └── Code generation ──► GitHub PR ──► CI/CD ──► sloctl apply ──► Nobl9 API
                                                  │
                                            ┌─────┴─────┐
                                            │ Terraform │
                                            │ OpenSLO │
                                            └───────────┘
                    ┌──────────────────────────────────────┐
                    │ Nobl9 SLO Engine │
                    │ Datadog │ Prometheus │ CloudWatch... │
                    └──────────────────────────────────────┘
                    ┌──────────────────────────────────────┐
                    │ SLO Oversight │ AI Discovery │
                    └──────────────────────────────────────┘

Control flow: AI Agent reads the SLO status through MCP → adjusts code behavior according to the status → submits new SLO definitions through sloctl/Terraform → Nobl9 engine continuously evaluates and feeds back the status. Data backflow: Nobl9 pulls indicators from various data sources → calculates the error budget → exposes it to the AI ​​Agent through MCP → the Agent visually displays or triggers an alarm.

How to use

Nobl9 provides multiple entrances and usage paths. Based on different team roles and needs, the following methods are recommended:

Entrance	Applicable scenarios	Getting started steps
Web console (app.nobl9.com)	Daily SLO viewing and management	Registration/login → Connect to data source → Create the first SLO → Configure alarms
Sandbox (nobl9.com/sandbox)	Zero-cost experience and evaluation	Open Sandbox → Load sample data → Explore dashboard
sloctl CLI	Daily operation and maintenance of SRE/platform engineers	Install sloctl → Configure API Token → sloctl apply -f slo.yaml
Terraform Provider	IaC Pipeline Integration	Configure terraform provider → terraform apply Create SLO
MCP Server	AI Agent Integration	Configure MCP Server endpoint → AI Agent reads SLO via MCP protocol
Backstage Plugin	Internal Developer Portal Embed	Install nobl9-backstage-plugin → Configure connection information

Typical usage steps (for SRE teams):

1. Connect data sources: Add monitoring sources such as Datadog/Prometheus/CloudWatch to the Nobl9 console, and the platform will automatically discover existing indicators.
2. Create SLO: You can use AI SLO Discovery to automatically generate it through natural language description of service behavior, or you can manually configure SLI metrics, target values, and compliance windows.
3. Configure Alarm: Set an alarm policy based on the error budget burn rate, and associate it with notification channels such as PagerDuty/Slack.
4. Embedded in GitOps: Export the SLO definition as YAML through sloctl or Terraform, incorporate it into the GitHub warehouse, and implement PR-driven SLO change management.
5. Enable MCP integration: Configure Nobl9 MCP Server in Claude Code / Codex so that the AI ​​Agent can query the SLO status during the coding process.

MCP Server configuration example (take claude_desktop_config.json as an example):

{
  "mcpServers": {
    "nobl9": {
      "command": "npx",
      "args": ["@nobl9/mcp-server"],
      "env": {
        "NOBL9_CLIENT_ID": "<YOUR_CLIENT_ID>",
        "NOBL9_CLIENT_SECRET": "<YOUR_CLIENT_SECRET>"
      }
    }
  }
}

Note: The above configuration needs to be replaced with real credentials. The specific parameters are subject to the official MCP document.

Product Pricing

Nobl9 adopts a subscription-based billing based on SLO Unit. The pricing is completely geared towards B-side teams and there is no free plan for individuals.

Plans	Pricing	SLO Units	Data Sources	Support	Features
Teams	$14,950/year	50	10	Work order support	All alarm methods 2-year data retention 99.9% SLA
Professional	Contact Sales	200	Unlimited	12×5 Dedicated Support	SSO, Custom Onboarding, Dedicated CRE
Enterprise	Contact Sales	500	Unlimited	24/7 Enterprise Support	Usage Protection Credits, Custom MSA, Single Tenant/HA, SCIM

SLO Units Explained: One SLO unit = one unique error budget target. Each SLO must contain at least one objective, and each additional objective counts as an additional SLO unit. For example, if an SLO defines three levels of goals (warning line, critical line, and strict line), 3 SLO units are consumed.

Billing Notes:

• All plans include 2 years of data retention -Teams plan offers 99.9% availability SLA
• The Enterprise plan supports usage protection points (minimum consumption commitment required)
• Data export (S3/GCS) included in Teams and above plans
• SSO and SCIM synchronization only for Professional and above plans

Price comparison with competing products:

Comparison dimensions	Nobl9 Teams	Self-built SLO (Prometheus + self-made dashboard)	Competitor SLO platform
Annual Explicit Cost	$14,950	1-2 SRE monthly salary + infrastructure costs	Depends on vendor pricing
Lead time	Ready to use	Weeks to months	Typically 1-4 weeks
SLO governance capabilities	Built-in SLO Oversight	Requires self-development	Varies widely among vendors
AI Integration	MCP Server + AI Discovery	None	Supported by a few competing products

Note: The prices of competing products are constantly changing due to product changes. The above comparison is based on publicly available approximate data. Actual purchases should be based on the real-time quotations of each manufacturer.

Application scenarios

• Reliability governance of AI coding agents: When teams use AI coding agents such as Claude Code, Codex, or Cursor to generate code on a large scale, the reliability coverage of the service often lags behind the speed of code delivery. Nobl9 uses MCP Server to enable AI Agent to automatically generate SLO definitions while creating services and submit them to the warehouse through SLOs as Code. The effect is that "as much code as the Agent writes, the reliability will cover as many services as possible" instead of waiting to make up for the SLO after it goes online.

• SLO governance at scale for SRE teams: The core question facing medium to large SRE teams with hundreds of microservices is "Who is maintaining which SLOs? Which ones have expired? Which ones are burning?". SLO Oversight automatically flags zombie SLOs, identifies ownership holes, and explains root causes of budget burn. In the actual test case, the platform automatically located an error budget burning rate of 285.7× in an exception and narrowed its root cause to "abnormal growth of the response body".

• Internal Developer Portal Integration for Platform Engineering Team: Via the Backstage Plugin, Nobl9 embeds SLOs directly into the developer portal. Each service page automatically displays current SLO status, error budget remaining percentage, and historical compliance trends. Developers don’t need to jump to standalone monitoring tools to understand the reliability health of their services.

• Compliance audit and business-level reliability report: Composite SLO allows the aggregation of technical indicators into business indicators (such as "order success rate" and "search response compliance rate"), which is suitable for reporting to business parties and management. Cooperating with data export to S3/GCS, it can meet the audit data retention requirements of finance, medical and other industries.

• Reliability baseline during cloud migration and architecture changes: During service migration or architecture upgrade, Nobl9's SLO Backtesting function can verify whether the new environment reaches the same reliability level as the old environment based on historical data. The team can define the SLO baseline before migration and continuously compare it during the migration process to ensure that reliability does not deteriorate.

Not suitable for scenarios:

• Startup project without monitoring infrastructure: Nobl9 relies on existing monitoring data sources (Datadog/Prometheus, etc.). If the team has not established any observability capabilities, Nobl9 cannot provide reliability management alone.
• Traditional operation and maintenance teams that only focus on infrastructure availability: If the team only cares about "whether the server is online" and not about "whether the user experience is up to standard", the switching cost of the SLO methodology itself will be higher.
• Teams with zero knowledge of SLO concepts: Although AI Discovery lowers the threshold for SLO definition, concepts such as error budget strategies and burn rate alarms still require basic SRE knowledge to be used effectively.

Applicable people

• SRE and Platform Engineers: Core user group. Nobl9 provides a complete tool chain from SLO definition, alarm configuration to GitOps integration, which is especially suitable for SRE teams that are building or optimizing internal SLO systems.
• Users and managers of AI coding agents: Teams that use tools such as Claude Code, Codex, Cursor, etc. to generate code on a large scale. Nobl9's MCP Server gives the AI ​​Agent the ability to "know whether the service it writes is reliable", which is crucial for operating and maintaining AI-generated code.
• Technical Manager (VP Engineering/Director of Infrastructure): A manager who needs to understand the overall reliability posture from a business perspective. Composite SLO and service health dashboards provide a view of reliability from "service level" to "business level".
• Platform Engineering Teams: For teams that are building internal developer portals like Backstage, the Nobl9 Plugin can embed SLOs into existing developer workflows with zero friction.

Not suitable for the crowd:

• Individual developers or micro teams (<5 people): The starting price of $14,950/year is high for small teams. It is recommended to use Sandbox to verify the necessity first, or consider open source alternatives (such as Prometheus + self-built SLO scripts).
• Pure business development team with non-SRE background: If the team does not have SRE or operation and maintenance roles, it will be difficult for the SLO methodology to be continuously implemented. It is recommended to introduce SRE culture first and then evaluate tool procurement.
• Teams looking for a full-stack observability platform (Metrics + Logs + Traces): Nobl9 does not do metric storage or log analysis, it focuses on the SLO abstraction layer. If a team needs Datadog-style full-stack observability, Nobl9 should be used as a complement, not a replacement.

Summary and Outlook

Core Competencies: The core barrier of Nobl9 is not "another observability dashboard", but rather the automation layer that upgrades the SLO methodology from "manual regular maintenance" to "AI-driven, coded Agent readable". The trinity of MCP Server + AI SLO Discovery + SLOs as Code enables it to be embedded in the AI ​​coding workflow, which is a scarce position among current competing products.

Current Limitations:

• High pricing threshold: The Teams plan with a minimum of $14,950/year is not friendly to small and medium-sized teams and lacks self-service pay-as-you-go or lightweight plans.
• AI Functional Maturity: AI SLO Discovery is still in the continuous iteration stage. The SLO definition of complex services may require manual correction and cannot rely entirely on AI output.
• Not available in the Chinese market: As a US SaaS product, access delays and data compliance (such as data non-exit requirements) in mainland China may become barriers to adoption. Currently, Nobl9 has no plans to deploy data centers in China.
• Reliance on third-party monitoring: Nobl9 is an "SLO layer" rather than an "observability base". If the underlying monitoring data source changes or is interrupted, the SLO calculation will also be affected.

Follow-up observation points:

1. AI Function Deepening: Will AI SLO Discovery evolve from "suggestion generation" to "automatic adaptation" (SLO goals are dynamically adjusted with service behavior)?
2. Ecological Expansion: Can the adoption rate of MCP Server be expanded from the head AI coding tool to the broader Agent ecosystem?
3. Pricing Strategy: Will there be lightweight solutions or pay-as-you-go options for small and medium-sized teams to lower the entry barrier?
4. Compliance layout: Will it increase coverage of compliance certifications such as GDPR, SOC 2 Type II, HIPAA, etc. to expand the financial and medical industry?

Procurement/Adoption Risk Assessment: The team should verify the following key terms before deciding to purchase - (1) Whether the consumption model of the SLO unit is consistent with the team's actual service structure (multi-target SLO consumes faster); (2) Whether the default configuration of 2 years of data retention meets the enterprise audit requirements, and whether additional charges are required for overdue storage; (3) Whether the actual response time of Professional/Enterprise support is clearly written in the contract; (4) The operation and maintenance responsibility boundary and availability of the self-hosted/single-tenant option SLA. It is recommended to start with the Teams plan + Sandbox verification first, and then evaluate the ROI of expanding to Professional/Enterprise after covering 10-20 core services.

Related tools: , Cursor

Version Info

• Nobl9 AI Platform 2026.07 ：Continuously iterating SLO Oversight, AI SLO Discovery, MCP Server integration and composite SLO capabilities, there is no official precise date yet. （2026-07）
• Nobl9 SLO Oversight Launch ：Officially released SLO Oversight governance feature, supporting automatic review, ownership tracking and expired SLO marking. There is no official precise date yet. （2026-05）
• Nobl9 AI Platform MCP Integration ：Publish MCP Server so that AI coding agents such as Claude, Codex, and Cursor can directly read SLO and error budget status. There is no official precise date yet. （2026-02）
• Nobl9 AI SLO Discovery Beta ：Released beta version of AI SLO Discovery to support natural language conversational SLO definition generation. There is no official precise date yet. （2025-11）