How is Altor different from a docs chatbot?

Docs chatbots answer 'how does this work?' from your knowledge base. Altor answers 'why is this broken for this customer right now?' by querying their actual API logs, checking your bug tracker, and verifying their billing status — across 6+ systems in under 2 minutes.

How is Altor different from our support platform?

Pylon, Plain, Zendesk — they route and manage tickets. Altor investigates them. We plug into your existing support tool as the investigation layer. You keep your workflows, we add the diagnosis.

Will Altor take actions without asking?

Not by default. Altor starts as a read-only investigator — it surfaces a diagnosis for your team to review. You control which action types graduate to auto-approval. Destructive actions are never automated.

What if our stack isn't listed?

If an API exists for your system, we can integrate it. The architecture composes tools — it doesn't hardcode connectors. We've yet to encounter a B2B stack we can't connect to.

What does Altor pricing look like?

Usage-based — you pay per investigation, not per seat. Pricing is quoted in USD ($) with no minimum commitment. We'll scope pricing during the demo based on your ticket volume and systems.

Your customers monitor everything — except your support queue.

When customers report missing metrics, alert storms, or ingestion failures, the root cause lives in agent configs, collector pipelines, and your ingest infrastructure. Altor investigates all three layers in 2 minutes.

Observability platform tickets that require deep investigation

These are the tickets that sit in queue until an engineer has time to investigate:

•"Metrics stopped flowing from 3 of our 200 hosts since the agent upgrade" — agent crash, network policy change, or a collector config incompatibility?
•"Alert fired 47 times in one hour for a metric that looks stable" — threshold too sensitive, evaluation window too short, or a data gap causing false positives?
•"Our ingestion bill jumped 3x this month but our infrastructure has not changed" — cardinality explosion from new labels, custom metrics growth, or a metering bug?
•"Dashboard query takes 45 seconds — it used to load in 2" — query hitting cold storage, cardinality bloom, or tenant resource contention?

What Altor queries for observability companies

Altor connects to the systems where your investigation data actually lives:

•Ingestion pipeline — per-tenant data volume, cardinality metrics, rejected/dropped samples
•Agent/collector logs — version, config diffs, connection status, sample delivery rates
•Query engine — slow query logs, resource utilization, storage tier access patterns
•Billing / usage metering — per-metric-series costs, cardinality breakdowns, plan limits
•StatusPage — infrastructure incidents, maintenance windows, regional capacity

30 min

average investigation time for agent/pipeline tickets

2 min

Altor's investigation with full pipeline trace

ingestion billing disputes involve metering + usage data cross-referencing

60%

of escalations trace back to config drift or cardinality growth

"Our tickets are investigations, not FAQs. Nobody else could even attempt to answer them automatically. Altor can because it queries our actual production data."
— Engineering lead, Portkey

Why observability companies adopt Altor

Observability companies handle ironically difficult support problems: customers who expect instant answers (they're monitoring experts) about deeply technical issues (data pipelines, query engines, distributed agents).

Altor matches this expectation. It investigates by querying your ingestion pipeline data, agent fleet status, and billing metering — delivering root causes with the same precision your customers use to monitor their own infrastructure.

Altor for data infrastructure Altor for Datadog teams Portkey case study

See Altor investigate a real ticket from your queue

We'll connect to your systems and run a live investigation. Your data, your ticket, diagnosed in 2 minutes during EST or PST hours.

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