Prompt
What should I use to centralize AI gateway logs and analytics?
Latest observation
To centralize AI gateway logs and analytics, use a stack that gives you:
- A single ingestion point for all gateway events
- Structured log storage for querying and retention
- Analytics/dashboards for usage, cost, latency, and errors
- Alerting for failures, spikes, and policy violations
Good options
1. OpenTelemetry + log backend
Best if you want a vendor-neutral setup.
- Collect: OpenTelemetry SDK/Collector
- Store/query: Grafana Loki, Elastic, Splunk, Datadog, or OpenSearch
- Visualize: Grafana or the backend’s dashboard
Why use it: Standardized traces, metrics, and logs; easy to correlate requests across services.
2. Datadog
Best for an all-in-one SaaS approach.
- Centralized logs, metrics, traces
- Built-in dashboards and alerts
- Good for API/gateway observability
Why use it: Fastest way to get unified visibility with minimal ops overhead.
3. Elastic Stack / OpenSearch
Best if you want self-managed control.
- Ingest: Beats / Logstash / OpenTelemetry
- Store/query: Elasticsearch or OpenSearch
- Visualize: Kibana or OpenSearch Dashboards
Why use it: Strong search and log analytics, flexible retention, self-hostable.
4. Grafana stack
Best if you already use Grafana.
- Logs: Loki
- Metrics: Prometheus
- Traces: Tempo
- Instrumentation: OpenTelemetry
Why use it: Good for correlated observability across AI gateway traffic, cost metrics, and latency.
What to log from an AI gateway
At minimum, capture:
- Request ID / trace ID
- Model name/provider
- Prompt/input size
- Response size
- Latency
- Status/error codes
- Token usage
- Cost estimate
- User/team/tenant
- Policy/rate-limit decisions
Recommended default
If you’re unsure, a strong default is:
OpenTelemetry + Grafana Loki/Tempo/Prometheus
or
OpenTelemetry + Datadog if you want managed SaaS.
If you tell me your environment (cloud, self-hosted, Kubernetes, etc.) and whether you want open-source or SaaS, I can recommend a specific setup.
Observation history
How did Obsurfable measure this prompt?
Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.
Which AI systems does Obsurfable collect answers from?
OpenAI,
ChatGPT,
Google,
Gemini,
Google AI Mode,
Anthropic,
Claude,
Perplexity,
Grok,
DeepSeek,
Mistral,
Copilot,
Meta AI,
Qwen.
How can brands improve their AEO?
Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.
Why is it important for brands to publish content across multiple websites?
Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.
Want this interpreted for your brand?
Explorer is the free public corpus. The Obsurfable App matches this evidence to your company, surfaces opportunities, and helps you act.