Sold by: Causely
Give your AI ops agents a continuously updated causal model of your system, delivered via MCP, so they stop guessing, consume fewer tokens, and remediate faster.
Overview
AI ops agents are only as reliable as the context they receive. Raw telemetry tells an agent what is happening. It cannot tell the agent what caused it, which services are at risk, whether a planned change is safe, or when the system is approaching its limits. Agents reasoning from telemetry alone scan broadly, accumulate context, and produce inconsistent answers.
Causely solves this by building and maintaining a continuously updated causal model of your applications including what normal looks like, what caused what, how changes ripple through your system, and what safe looks like. This model is exposed to your agents via MCP as structured, actionable context, delivered the moment the agent needs it, specific to your environment.
The results are measurable. In a benchmark across 72 experiments covering Claude Code, Gemini, and Codex configurations, agents with Causely reached 100% accuracy across every configuration, reduced average token consumption by 48%, cut mean query time by 63%, and eliminated the 67% false positive rate.
Causely connects to your existing observability sources including CloudWatch, Prometheus, Datadog, and OTel via native integrations and exposes causal context through a standard remote MCP server. Any MCP-compatible agent framework connects immediately. Causely deploys with either a SaaS or BYOC architecture, so your data stays within your environment. No new instrumentation. No model training. No rip and replace.
Highlights
- Agents that diagnose correctly, every time - AI ops agents without a causal model construct narratives from ambiguous telemetry. In our benchmark, 75% of configurations missed at least one fault diagnosis, and two of four produced a 67% false-positive rate, generating incidents that did not exist. With Causely, every configuration achieved 100% fault accuracy, and false-positive rates dropped to zero. Agents receive a structured causal model, so diagnosis is deterministic, not probabilistic.
- Fewer tokens, lower cost per investigation - Open-ended environment scanning is how agents run up inference costs. Causely replaces scanning with targeted causal queries: agents request the context they need and receive it in a compact, structured MCP response. In our benchmark, this reduced average token consumption by 48% and worst-case token exposure by 81% for the most expensive configuration. Fewer tool calls, less context accumulation, lower cost per correct diagnosis.
- From alert to remediation in a fraction of the time - Agents reasoning from raw telemetry are slow by design. They scan broadly before they can act. Causely delivers pre-computed causal context the moment an agent needs it, cutting the scan phase to zero. In our benchmark, mean query time dropped 63% on average and up to 83%. Faster triage during active incidents. Earlier detection before they escalate.
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Pricing
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
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Dimension | Description | Cost/12 months |
|---|---|---|
Service | A service represents one deployable application component, such as a microservice, API, or external dependency. Each service that Causely analyzes as part of your topology for causal inference counts toward your plan. This plan includes up to 500 services; additional services can be added by upgrading your plan. | $24,000.00 |
Dimensions summary
This contract prices on a single dimension: the service. A service is one deployable application component, such as a microservice, API, or external dependency. Causely counts each service it analyzes in your topology for causal inference. Your plan includes up to 500 services. To cover more than 500 services, you upgrade your plan. Pricing scales with how many services you run, so cost tracks the size of the environment Causely models. There is one billing measure to track, which keeps the structure simple.
Top-of-mind questions for buyers
A service is one deployable application component that Causely analyzes in your topology for causal inference. This includes microservices, APIs, or external dependencies such as databases, caches, queues, or messaging systems. Each distinct component Causely models counts as one service toward your plan limit of 500.
The plan covers up to 500 services. To analyze more than 500, you upgrade your plan. The service count grows as Causely discovers and models more components in your environment, so cost tracks the size of the topology it maps.
Causely continuously discovers and reconciles your topology from your telemetry sources. The number of services it analyzes reflects the components active in your environment. Your plan meters those services against the 500 included, so dynamic environments can shift the count over time.
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