How Much Are Free Observability Credits Worth?
Five monitoring and observability vendors run startup credit programs worth between $2,500 and $100,000 each, and because every one of them is a separate vendor invoice, they stack rather than overlap.
AI Perks tracks them alongside $7.7M in credits across 194 companies.
| Program | Credit value | What it covers | What it bills on |
|---|---|---|---|
| Datadog | Up to $100,000 | Infrastructure monitoring, APM, logs, frontend analytics | Hosts, log ingest, custom metrics |
| PostHog | Up to $50,000 | Product analytics, session replay, error tracking, flags | Events |
| New Relic | $5,000 | APM, infrastructure, logs, error tracking | Data ingested, full-platform seats |
| Elastic | $5,000 | Log analytics, search, Kibana dashboards | Provisioned resources, retained data |
| PagerDuty | $2,500 | On-call scheduling, escalation, alert routing | Seats |
Two things distort that table. The headline figure is a ceiling, not a quote, and the route changes the number: the Datadog offer routed through a cloud partner program is worth $5,000, not $100,000, for the same product. Eligibility varies by program, and the current terms for each route are listed on getaiperks.com.
What Observability Actually Buys You
Observability is not metrics, logs and traces individually. All three are available for free. What you are paying for is the join between them, so that at 3am one person can go from a latency spike to the specific trace to the specific log line without changing tools.
Every layer has a free equivalent. Prometheus and Grafana cover metrics. OpenTelemetry with Jaeger or Tempo covers traces. Loki or your cloud provider's native logging covers logs. Open-source error tracking covers exceptions.
What you cannot get for free is correlation and the absence of an operator. Running that stack yourself is roughly a quarter of an engineer once retention, cardinality and version upgrades are real work. At a ten-person company, that quarter is more expensive than the software.
The honest test of whether you need a paid platform yet: do you have an on-call rotation, and has anyone been paged twice for something they could not explain from one dashboard? If the answer is no, error tracking plus your cloud's built-in metrics genuinely covers you, and the credit is worth more later.
How Observability Pricing Behaves at Scale
Observability bills do not scale with revenue. They scale with how chatty your code is, which is a decision an engineer makes mid-sprint without ever seeing a price.
Each vendor meters on a different axis, and that axis decides how fast a given credit drains:
- Host-based (Datadog infrastructure): predictable, until autoscaling groups count short-lived nodes at peak
- Ingest-based (New Relic, Elastic, most log products): predictable, until someone leaves debug logging on after an incident
- Event-based (PostHog): grows with traffic and instrumentation, not headcount
- Seat-based (PagerDuty, part of New Relic): the only meter you can actually forecast a year out
The failure mode nearly everyone hits is tag cardinality. Adding something like user_id or request_id as a tag on a custom metric is a one-line change that can multiply a single metric into six figures of stored time series. It is the most common cause of a surprise observability invoice by a wide margin.
The scale of what can go wrong is not theoretical. Coinbase was reported in 2023 to be running a Datadog bill in the tens of millions of dollars, an order of magnitude above internal expectations. Nobody approved that. It accumulated one tag and one log level at a time.
How to Choose Between Observability Providers
Choose on the billing meter and on what your team can operate, not on the headline credit. A $100,000 grant against a system you have not instrumented is worth less than a $5,000 grant against a real on-call rotation.
Start from the shape of your team rather than the vendor list:
| Team shape | Minimum viable observability | What to skip for now |
|---|---|---|
| 1-3 engineers, one service | Error tracking plus your cloud's built-in metrics | APM, session replay, log indexing |
| 4-10 engineers, on-call exists | Error tracking, centralised logs, alert routing | Full-platform APM on every service |
| 10+ engineers, many services | Correlated metrics, traces and logs in one place | Nothing, this is where the category earns its price |
The second decision is single platform versus best-of-breed. One vendor for everything buys you correlation and one invoice, and costs you leverage at renewal. Separate tools for errors, logs and on-call are individually cheaper and usually better at their one job, and you pay for that in context-switching during an incident.
A useful rule: fund the single-platform bet with the largest credit you are approved for, and keep on-call on a separate vendor regardless. Alerting is the one system you do not want going down with the thing it is watching. AI Perks groups these programs together so the amounts and terms sit side by side instead of scattered across a dozen vendor pages.
What Order to Activate In
Approval and activation are different decisions, and conflating them is how most of a large credit gets wasted. The sequencing that matters is not which program you reach for first, it is which one you switch on first.
These programs and their current terms sit together under the analytics and observability categories on getaiperks.com, which is where the monitoring, logging, error tracking and incident tooling live.
Once more than one is available to you, turn them on in ascending order of what you can actually consume.
Small, seat-based grants first. On-call and error tracking deliver value the day you turn them on and consume credit at a rate you can predict.
The largest grant last, once your fleet is real. Credit burns against what you have instrumented, so a big grant switched on against three services burns on nothing.
Re-check the category regularly. Infrastructure programs change terms and routes often, and the same vendor can be worth twenty times more through one route than another.
What Founders Get Wrong About Observability Credits
The most expensive mistake is treating a large credit as free monitoring instead of as a fixed window to build cost habits you can afford at list price afterwards.
Assuming cloud credits cover it. They do not. Datadog, New Relic and PagerDuty are third-party vendors whose invoices sit outside your AWS or Google Cloud balance. That is exactly why they stack cleanly, and why holding cloud credits, model credits and observability credits together covers three different bills rather than one bigger version of the same one.
Instrumenting everything on day one. A credit removes the price signal at the precise moment your team is forming defaults. Whatever you switch on while it is free is what you inherit at list price later.
Indexing logs nobody will search. Ingest is cheap, indexing is not. Ingest broadly, index narrowly, archive the rest to object storage.
No usage alert. Every one of these platforms can monitor its own consumption. Configure that on day one, not once the balance is nearly gone.
Planning the exit too late. Credits are denominated at list price, so the end of one is a cliff rather than a ramp. Decide at 70% consumed what you will cut. Other programs in the same category that cushion that transition are tracked at getaiperks.com.
Frequently Asked Questions
What are the biggest free observability credits for startups?
Datadog is the largest at up to $100,000, followed by PostHog at up to $50,000, then New Relic and Elastic at around $5,000 each and PagerDuty at $2,500. Amounts vary by route, so the same vendor can be worth very different sums. Current terms are tracked at getaiperks.com.
Do AWS or Google Cloud credits cover Datadog or New Relic?
No. These are third-party SaaS vendors that bill separately from your cloud provider, so a large cloud grant leaves your monitoring invoice untouched. That separation is what makes them stack cleanly, and it is why holding both matters more than holding a larger amount of either one.
Can I stack observability credits from multiple vendors?
Yes. Each program is a distinct vendor invoice, so nothing prevents running error tracking on one grant, logs on another and on-call on a third. The practical limit is operational, not contractual: more tools means more context-switching mid-incident. See which combinations exist at getaiperks.com.
Which observability tool should a pre-seed startup use?
With one or two services and no on-call rotation, error tracking plus your cloud provider's built-in metrics covers nearly everything for close to zero. A full platform earns its price once you have multiple services, a queue, and incidents that cannot be explained from a single dashboard.
Why is my observability bill growing faster than my traffic?
Almost always custom metric cardinality. Adding a high-cardinality tag like a user or request ID to a metric multiplies it into enormous numbers of stored time series. Log indexing is the usual runner-up. Auditing those two routinely halves a bill without losing any real visibility.
What happens when observability credits run out?
You inherit a bill sized by the habits you formed while it was free, at list price rather than a negotiated rate. The fix is to write down the monthly spend you can carry unsubsidised, then configure retention, indexing and APM coverage so that is where you land before the credit ends.
Instrument the system. Let someone else pay for watching it.