Oodle.ai Agent Trace Pricing: $10 per Million Traces, Explained

Oodle.ai prices agent trace observability at $10 per million spans, with no sampling, sub-second p99 query latency, and 100% of traces analyzed. That is roughly 8x cheaper than Langfuse’s base tier ($80 per million units) and far below the per-seat-plus-storage model LangSmith uses. The company processed 120 million agent traces in the last month, and its founder reports Langfuse was 6x more expensive for their own observability workload. This review explains how Oodle achieves that price, whether it is genuinely cheap, and who should adopt it.

What Is Oodle.ai and Why Does $10 per Million Agent Traces Matter?

Oodle.ai is an LLM agent observability platform built on a custom columnar storage engine. Unlike traditional APM tools that sample a small fraction of traces to control cost, Oodle retains every agent execution with zero sampling. The headline price of $10 per million spans is significant because agent traces are dramatically larger than traditional traces — system prompts alone can exceed 200KB — which makes full-fidelity storage historically expensive.

The pricing matters for a simple reason: production AI agents generate enormous volumes of trace data. A single agent that runs thousands of tasks per day can produce millions of spans per month. At Langfuse’s $80-per-million base rate, that becomes a serious line item. At Oodle’s $10-per-million rate, full-fidelity observability becomes affordable enough to run on every trace rather than a sampled subset.

Oodle’s positioning is that you should not have to choose between cost and completeness. The platform detects silent failures that sampling misses: aborted tasks, infinite loops, frustrated users, budget burns, and duplicate prompts. Its case study with Fello, an AI teammate platform, shows 3M+ agent traces captured per day with zero sampling.

How Does Oodle Achieve $10/Million? Architecture and Storage Engine

The cost-efficiency story starts with the storage engine. Oodle built a custom columnar database on object storage (S3) for each observability signal — logs, metrics, and traces. Data is stored in a proprietary parquet-like columnar file format, and queries run via AWS Lambda, so compute only spins up when a query is issued.

This architecture delivers three claimed advantages:

• 3-5x more cost efficient than traditional observability platforms
• 10x more cost efficient than other agent observability solutions
• Sub-second p99 query latency even on large trace volumes

The engineering team documented several optimizations that made this possible. They use out-of-band dictionary storage to compress repeated strings, lazy row group loading that cut peak memory from 5.3GB to 1.3GB, and a decode-only dictionary reader. They also fixed a persistent Lambda memory leak. Together these changes reduced a common 7-day query from 60 seconds to under 2 seconds.

The key insight is that agent traces are not like traditional traces. Traditional observability deals with small tags and short spans. Agent traces carry huge payloads — full system prompts, tool outputs, and reasoning chains. A storage engine designed for small tags cannot handle this efficiently. Oodle’s columnar format is purpose-built for these large, repetitive payloads, which is why it can store them at roughly $1 per million observations in the storage layer.

The No-Sampling Philosophy: Why Random Sampling Fails for Agents

Oodle’s core thesis is that sampling evals creates random blind spots. If you evaluate only 1% of your traces, you miss the rare edge cases and hallucinations that actually break production agents. A random 1% sample is statistically unlikely to catch a failure that occurs in 0.5% of runs — the exact failures that matter most.

The problem is that evaluating every trace with an expensive LLM judge is cost-prohibitive. This is the tension Oodle’s architecture resolves. The answer is not to sample randomly, but to judge all observations using cheap deterministic layers first, then reserve expensive LLM judges for the small subset that actually has problems.

This “no sampling” approach matters for reliability. Silent failures — tasks that abort, loops that never terminate, users who get frustrated, budgets that burn through — are precisely the events that a 1% sample will miss. By analyzing 100% of traces, Oodle surfaces these failures reliably.

Chained Evaluation: Cheap Filters Before Expensive LLM Judges

Oodle’s evaluation pipeline uses a chained approach. Cheap deterministic layers run on every trace, and expensive LLM judges run only on the 5-10% of traces that the cheap layers flag as problematic.

The deterministic layers include:

• VADER sentiment analysis (free, deterministic) on every observation from the second turn onward, to detect user frustration
• Code evaluators that check for valid JSON, correct tool selection, token limits, and hallucinated URLs — all running on every trace for free
• Anomaly detection on latency, token counts, and other signals

Only after these cheap filters identify a problem does an LLM judge run. This means the expensive LLM-as-a-judge cost is applied to roughly 5-10% of traces instead of a random 1% sample — and crucially, it is applied to the traces that actually have issues, not a random subset.

This is the mechanism that makes $10 per million spans viable with healthy margins. The deterministic-first approach means the marginal cost of processing each additional trace is tiny, because most traces never reach the expensive LLM stage.

Oodle vs Langfuse vs LangSmith: Agent Trace Pricing Compared

To understand whether $10 per million is cheap, it helps to compare against the two dominant competitors.

Langfuse’s pricing is usage-based: 0-100k units free, then $8 per 100k up to 1M, $7 per 100k up to 10M, $6.5 per 100k up to 50M, and $6 per 100k beyond. At the base tier that is $80 per million — 8x Oodle’s price. Even at Langfuse’s highest volume tier ($60 per million), Oodle is still 6x cheaper. Oodle’s founder states Langfuse was 6x more expensive for their own agent observability workload.

LangSmith uses a different model: per-seat plans (Developer $0/seat with 5k base traces/month, Plus $39/seat with 10k base traces/month, Enterprise custom) plus pay-as-you-go for LangChain Compute Units ($1.50) and LangChain Storage Units ($1.00). Base traces have 14-day retention. For high-volume teams, the per-seat cost plus storage units can add up quickly.

The comparison table makes the pricing gap clear. For a team generating 10 million traces per month, Oodle would cost $100, while Langfuse would cost $800 at base tier. That is a meaningful difference for any production AI operation.

Is $10/Million Actually Cheap? Market Context and Alternatives

The pricing is contested. On the Show HN thread for Oodle (July 14, 2026, 31 points), reactions were mixed. One commenter said “Dang that’s expensive,” while another reported paying just $0.75 per million traces through a vendor — a dramatically cheaper alternative.

This is an important caveat. $10 per million is cheap relative to Langfuse and LangSmith, but it is not the cheapest option on the market. Teams that only need basic trace storage without the full evaluation pipeline may find cheaper vendors. The $0.75-per-million figure suggests there is a low-cost segment that Oodle does not compete in.

However, price per million is not the only metric. Oodle’s value proposition includes the built-in evaluation pipeline, no-sampling reliability, and sub-second query latency. A cheaper vendor at $0.75 per million may not offer deterministic-first evaluation, silent-failure detection, or the same query performance. The right comparison depends on what you need beyond raw storage.

For high-volume production agents, the total cost of ownership matters more than the per-trace price. If full-fidelity tracing catches one expensive production incident that sampling would have missed, the observability cost is trivially justified.

Who Should Use Oodle for Agent Observability (and Who Shouldn’t)

Oodle is a strong fit for teams with high-volume production agents where full-fidelity tracing matters. If you run thousands of agent tasks per day, need to detect silent failures reliably, and want sub-second query latency across millions of traces, Oodle’s model is compelling. The Fello case study — 3M+ traces per day with zero sampling — is exactly the profile that benefits.

Oodle is also a good fit for teams that want to avoid the complexity of building their own columnar storage engine. The engineering effort documented in Oodle’s blog — custom file formats, Lambda memory fixes, dictionary compression — is not something most teams want to replicate in-house.

Oodle may not be the right choice for:

• Low-volume teams generating only a few thousand traces per month, where the $10-per-million rate is irrelevant and a free tier (like Langfuse’s 0-100k free) is more attractive
• Teams that only need basic trace storage without evaluation, who can find cheaper vendors (the $0.75-per-million market segment)
• Teams already deeply invested in LangSmith’s ecosystem, where the per-seat model and LangChain integration may outweigh the per-trace cost difference

The decision hinges on volume and evaluation needs. High volume plus a need for reliable failure detection favors Oodle. Low volume or minimal evaluation needs favor cheaper or free alternatives.

Verdict: Is Oodle’s $10/Million Agent Trace Pricing Worth It?

Oodle.ai’s $10 per million agent traces is a genuine price disruption in agent observability. It is 8x cheaper than Langfuse’s base tier and far more cost-effective than LangSmith’s per-seat-plus-storage model for high-volume workloads. The no-sampling philosophy, backed by a deterministic-first evaluation pipeline, addresses a real reliability gap: random sampling misses the rare failures that break production agents.

The architecture — custom columnar storage on S3 with serverless Lambda compute — is the reason the price is sustainable. By running cheap deterministic filters on every trace and reserving expensive LLM judges for the 5-10% that actually have problems, Oodle keeps marginal costs low while analyzing 100% of traces.

The main caveat is that $10 per million is not the cheapest option on the market. HN commenters cited $0.75-per-million alternatives. But for teams that need full-fidelity tracing, silent-failure detection, and sub-second query performance at production scale, Oodle offers a compelling price-performance ratio that the incumbents do not match.

For high-volume production agent teams, Oodle’s $10-per-million pricing is worth it — and it may be the most cost-effective way to get reliable, full-fidelity agent observability today.

FAQ

What does Oodle.ai charge for agent traces?

Oodle.ai charges $10 per million agent trace spans, with no sampling and 100% of traces analyzed. The platform processed 120 million agent traces in the last month.

How does Oodle’s pricing compare to Langfuse?

Oodle’s $10 per million is roughly 8x cheaper than Langfuse’s base tier of $8 per 100k units ($80 per million). Even at Langfuse’s highest volume tier ($60 per million), Oodle is 6x cheaper.

What is the “no sampling” approach in Oodle?

Oodle analyzes 100% of agent traces instead of a random sample. It uses cheap deterministic filters (VADER sentiment, code evaluators, anomaly detection) on every trace, then runs expensive LLM judges only on the 5-10% of traces that actually have problems.

Is $10 per million agent traces actually cheap?

It is cheap relative to Langfuse and LangSmith, but not the cheapest on the market. Some HN commenters reported paying $0.75 per million through other vendors. The value depends on whether you need the full evaluation pipeline and no-sampling reliability.

Who should use Oodle for agent observability?

High-volume production agent teams that need full-fidelity tracing, silent-failure detection, and sub-second query latency. Low-volume teams or those needing only basic storage may find cheaper or free alternatives more suitable.