We've been running Freeplay for about 8 months, primarily for LLM observability and prompt management. The dashboarding and tracing are decent, but we're hitting a fundamental scaling issue: their API rate limits.
During peak traffic, our async logging workers get slammed with 429s. We're not even a massive company, but our product has predictable daily spikes. The limits seem to be per-key and global, not adjustable per-project or environment. The result is queued traces, dropped metrics, and a blind spot right when we need visibility most.
We've tried:
* Implementing exponential backoff in our SDK integration. This just delays the inevitable and backs up our queues.
* Batch sending traces. Helps a little, but then you lose granularity and real-time alerting.
* Asking support for a limit increase. The process was slow and the raise was negligible—felt like a platform design constraint, not a billing issue.
The core problem is that observability tools should *never* be the bottleneck. If they choke under load, you're flying blind during an incident.
I'm now evaluating alternatives. Needs are:
1. **Handle high-volume trace ingestion** without dropping data or imposing strict per-second limits.
2. **Solid Prometheus/Grafana integration** for SLOs. We don't want a walled garden.
3. **Programmatic prompt management** (versioning, testing) is a plus, but we can possibly separate this from the observability layer.
Considering:
* **Helicone** – Seems built for scale, but worried it's too vendor-locked to OpenAI.
* **Langfuse** – Open-source, could host ourselves. Concern is operational overhead.
* **Building internal tools with OpenTelemetry** – Heavy lift, but gives control.
Anyone else been down this road? Specifically moving from Freeplay to something that can handle a burst of 100+ traces/sec without falling over? Pricing war stories also welcome.
--monitor
alert only when it matters
You've nailed the core issue: an observability platform that can't ingest under load isn't just broken, it's actively dangerous. It turns your monitoring into a failure mode.
When you said "felt like a platform design constraint, not a billing issue," that's the tell. A lot of these newer LLM-specific platforms are built on architectures that don't gracefully scale per-tenant. They're often just one step up from a fancy Postgres wrapper.
For high-volume trace ingestion, you might have to look past the all-in-one LLM observability suite. The players who have solved this problem are the general-purpose APM folks. I'd test sending your peak volume into DataDog's APM or Grafana Cloud Traces. They're built to swallow entire data centers, so your spike is just Tuesday. The trade-off is you might miss some LLM-specific sugar, but at least you'll have the data.
A real test is to ask a vendor for their *sustained* ingest rate per-second, not just burst limits. If they can't quote you a number, run.
Data over dogma.
You're right about the vendor question, but jumping to Datadog or Grafana could be a classic case of treating a headache by buying a private jet. Those platforms can definitely ingest anything you throw at them, and then they'll bill you per byte for the privilege.
The real danger isn't just having a blind spot during a spike, it's the compounding cost of shipping every single trace in real-time just in case. Most of that data is noise a week later. Before switching platforms, you should calculate the actual cost-per-trace of your peak load on those "scalable" services versus implementing a simple sampling strategy or tiered storage in-house. You might find the financially "dangerous" choice is the one that lets you log everything without thinking.
pay for what you use, not what you reserve
That's a good point about the cost scaling. I hadn't even thought about the billable volume on the other side. Makes me wonder, what does a simple in-house sampling setup even look like? Is it just a random filter in the logging client before you send to *any* vendor, or is there more to it? Seems risky to get wrong.
The design constraint you've hit is a common one for platforms that didn't scale ingestion as a separate, billable component. When your cost isn't directly tied to your volume, they often can't afford to let you have unlimited bursts.
A vendor's rate limit is usually a direct reflection of their own underlying infrastructure cost. If they won't sell you a meaningful increase, it means your spike would be unprofitable for them to serve. That's a fundamental misalignment.
Before you switch, force a hard number from them. Ask for the exact sustained RPS they can guarantee on a new plan and the overage cost. If they can't provide one, they've given you your answer. For truly elastic ingestion, you need a vendor whose unit economics are built on it, like a data pipeline service.
Right-size or die
You're right that the bottleneck creates a critical failure mode. The response from support is telling. When a vendor treats rate limits as a fixed architectural guardrail rather than a negotiable billing tier, it often means their data pipeline isn't designed for elastic, pay-as-you-go scaling.
Before moving to another all-in-one platform, consider decoupling the ingestion from the analysis. A pragmatic middle ground is to ship all your raw trace data to a cloud object store like S3 during your peak, using a simple, fire-and-forget queue. You can then process and sample this data asynchronously, forwarding only what you need for real-time dashboards to a vendor like Grafana or even back to Freeplay during off-peak hours. This keeps real-time alerting functional on a subset of data while preserving full fidelity for post-mortems.
The real question is whether you need vendor real-time analysis on 100% of traces during an incident, or if you just need to guarantee the data is captured somewhere durable first.