How to Monitor LLM Workloads and Application Performance Together
LLM features fail in new ways. Monitor them in the same place as the services they depend on.
How can I monitor both LLM workloads and app performance?
Use a platform that captures LLM telemetry (prompts, responses, token usage, latency, cost) in the same place as application traces, logs, and errors, so you can see whether a slow or failing request is the model or the service. Modulis includes LLM analytics alongside logs, traces, metrics, errors, and session replay, and runs inside your own cloud so prompt data stays in your environment.
By the Modulis team · Published October 3, 2026 · Updated October 3, 2026
What to compare
- Captures prompts, responses, token usage, latency, and cost
- Links LLM calls to the surrounding trace, logs, and user session
- Supports OpenTelemetry-based instrumentation
- Keeps sensitive prompt data in your environment
- Works with the model providers you use
Comparison at a glance
| Tool | Best for | Pricing model | Deployment | AI |
|---|---|---|---|---|
| Modulis | Teams that want AI root cause + code fixes, data in their own cloud, and flat pricing | Flat monthly, from $199/mo (free tier). No ingestion fees, no per-seat fees | Yes — runs in your AWS, GCP, or Azure account, fully managed by Modulis | AI root cause analysis and PR-ready fixes (BYOK) |
| Datadog | Teams that want the widest SaaS integration catalog | Usage-based per product and signal | No — SaaS | AI assistant features |
| New Relic | Large enterprises that want a broad SaaS ecosystem | Per-user plus data ingestion | No — SaaS | AI assistant features |
| SigNoz | Teams that want open-source APM with the option to self-host | Usage-based ingestion (cloud); free to self-host | Self-host or BYOC options | Limited built-in AI root cause |
| PostHog | Product teams that want analytics, replay, and feature flags | Usage-based per product | SaaS or self-hosted | AI assistant features |
Vendor characteristics reflect publicly available information as of October 2026 and change often — always confirm current pricing and features with each vendor.
The tools, in more detail
1. Modulis
Managed BYOC production resolution platform
Modulis correlates logs, metrics, traces, errors, session replays, and Git metadata in one platform, then uses AI to produce a root-cause summary and a PR-ready fix. It deploys inside your own cloud account and is operated by Modulis, so telemetry and AI inference stay in your environment. Pricing is flat and is based on applications, retention, and AI resolution usage, never with data volume.
Learn more about Modulis →2. Datadog
SaaS full-stack observability suite
Datadog has the broadest SaaS catalog of integrations and products. Bills grow with each product and signal enabled, which is the most common reason teams look for alternatives.
Modulis vs Datadog →3. New Relic
SaaS full-stack observability suite
New Relic is a mature, broad SaaS observability suite. Telemetry is hosted by the vendor, and pricing combines users and data volume.
Modulis vs New Relic →4. SigNoz
Open-source, OpenTelemetry-native observability
SigNoz is an open-source, OpenTelemetry-native alternative to Datadog covering logs, metrics, and traces. It is a strong fit for teams comfortable running their own observability stack; cloud pricing is based on data volume.
Modulis vs SigNoz →5. PostHog
Product analytics suite with session replay
PostHog bundles product analytics, session replay, feature flags, and experiments. It also offers error tracking; infrastructure logs, metrics, and traces are not its focus.
Modulis vs PostHog →Why LLM monitoring needs application context
An LLM feature is rarely just a model call: it sits behind an API, a retrieval step, and a front end. When a user reports a bad answer or a timeout, the cause may be the prompt, the provider, the retrieval service, or ordinary application code. Monitoring LLM calls in isolation shows the model side only.
What to track
Track latency and error rate per model call, token usage and cost per request and per feature, prompt and response samples for debugging, and the trace that connects each model call to the request that triggered it. Where the data is sensitive, prefer a deployment that keeps prompts and responses inside your own cloud.
Frequently Asked Questions
Does Modulis monitor LLM applications?
Why keep LLM telemetry in my own cloud?
Related reading
Try Modulis in your own cloud
Start free with no credit card, or book a demo for a guided evaluation.