Datadog LLM Observability adds $0.10/1,000 spans on top of enterprise Datadog costs. Preto.ai is purpose-built for LLM cost reduction — proxy-based, no agents required, and the savings from recommendations typically pay for it 10–50x over.
No credit card. No Datadog agent. Works with your existing OpenAI code.
Datadog LLM Observability is a capable product for teams already deep in the Datadog ecosystem who want LLM data alongside their infrastructure metrics. Teams that look for alternatives either aren't Datadog customers, can't justify the per-span cost, or need something purpose-built for reducing LLM spend rather than just observing it.
Datadog charges $0.10 per 1,000 LLM spans. At 5M requests/month, that's $500/month in observability fees — on top of your base Datadog contract. You're monitoring the problem, not solving it. Preto's recommendations generate savings that far exceed its subscription cost.
Datadog LLM Observability requires the Datadog agent deployed alongside your app, plus SDK instrumentation. Preto requires zero agents. Change one base_url in your OpenAI client and you're live in minutes — no deployment pipeline changes, no agent management.
Datadog gives you beautiful infrastructure-style dashboards for LLM metrics. What it doesn't give you is "here are your top 5 cost reduction opportunities, ranked by projected monthly savings." Preto generates that list automatically — and updates it as your traffic patterns change.
An extension of Datadog's infrastructure monitoring platform for LLM workloads. Best for enterprise teams already on Datadog who want LLM metrics alongside APM, logs, and infrastructure data in one place.
Purpose-built for LLM cost optimization. No agents, no per-span fees. Works via proxy — one URL change. Surfaces ranked savings recommendations with dollar estimates and enforces budget limits.
| Feature | Datadog LLM | Preto.ai |
|---|---|---|
| Proxy-based integration (no agents) | ✗ | ✓ |
| Cost tracking per request | ✓ | ✓ |
| Infrastructure + LLM unified dashboard | ✓ | ✗ LLM-only |
| AI cost recommendations | ✗ | ✓ |
| Dollar savings estimates per finding | ✗ | ✓ |
| Savings dashboard (money recovered) | ✗ | ✓ |
| Budget enforcement (hard-block) | ✗ | ✓ |
| Per-request span/ingestion fees | $0.10/1K spans | None |
| Works without existing infrastructure setup | ✗ | ✓ |
| APM + log correlation | ✓ | ✗ |
Datadog LLM Observability brings LLM metrics into the same platform where you monitor servers, containers, and applications. If your team's mental model is "LLMs are just another infrastructure component," Datadog makes sense — you get trace correlation, latency percentiles, error rates, and model cost data all in one place. It's infrastructure observability applied to AI.
Preto doesn't care about your infrastructure stack — it cares about your LLM API bill. It runs five AI analysis rules against your traffic patterns and surfaces the changes with the highest dollar impact first. Each recommendation includes a projected monthly savings figure so you can prioritize without manual analysis. It also enforces hard budget limits that Datadog's alerting can't match.
Datadog would show you this as a spike in token usage. Preto identifies the pattern automatically, estimates the dollar impact, and tracks when you fix it.
Preto works at the HTTP proxy layer. No Datadog agent to deploy. No SDK to instrument. Change the base_url in your OpenAI client and you're live — cost data and first recommendations appear within hours.
No Datadog agent. No SDK. No deployment pipeline changes. No per-span fees.
Book a 30-minute demo. We'll show you what your OpenAI spend looks like through Preto — and what we'd recommend cutting first.
Book a Demo →Or email gaurav@preto.ai
What they said after switching.
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We're in private beta. Quotes coming soon — reach out if you want to be first.