9 PB of production telemetry last quarter
AI can't debug what it can't see
Observability built for agents. Give agents full production context to debug softwareand other agents. Object storage for telemetry, serverless compute for queries,and sub-second agentic queries at 80% lower cost.
Largest customer deployment
built by engineers behind Rubrik · Amazon S3 · DynamoDB · Snowflake
Customer stories
Agent observability
3M+ agent traces/day
“Having both agent observability and full-stack observability in one place makes debugging faster and easier. We're catching issues before our customers do, with full visibility and no sampling across millions of agent traces.”
Ashish Gupta
Principal Engineer
The real problem
AI changed debugging.
Observability architecture didn't.
Disk-backed, fixed-compute stacks were built for dashboards, not agents at scale.
So you sample, cut retention, or overprovision capacity.
Sampling → blind spots
Agents are non-deterministic, so you can't predict which traces matter. Sample them, and rare failures disappear before you know to look.
Expensive at full fidelity
1,000× query overload
Fixed clusters were sized for a few humans, not thousands of concurrent agent questions.
Unreliable · timeouts · capacity spikes
>1s stalls the agent
Agents generate 100–1,000× more telemetry. Human-scale query latency is too slow for automated debugging loops.
Observability becomes the bottleneck
Software + agents
Observability is becoming headless
Agents debug production software.
The same telemetry your engineers already rely on, now queryable by AI.
Logs·Metrics·Traces·APM·RUM·Synthetics
Explore full-stack observabilityAgents debug other agents.
Find what failed, prove the fix, and keep improving the agent in production.
Explore agent observabilityThe differentiator · Full context
To really debug the agent, you need both.
Infrastructure, applications, and the agent itself, so AI never debugs half-blind.
Duration
29.78s
Spans
151
Services
4
Errors
9
Est. cost
$0.0005
Tokens
4,224
- POST /api/v1/chatapi-gateway29.78s
- agent.runsupport_agent28.90s
- call_llmgemini-2.0-flash731ms
- tool searchProductssupport_agentService unavailable3.62s
- GET /searchproduct-service3.60s
- SELECT products …postgres3.41s
- call_llmgemini-2.0-flash2.31s
- tool checkInventorysupport_agentToolExecutionError3.64s
- gRPC Inventory.Checkinventory-service3.60s
- GET sku:SONY-SEL70200GM2redistimeout3.58s
- call_llmgemini-2.0-flash1.89s
{ "sku": "SONY-SEL70200GM2", "quantity": 1 }The agent looks like it failed. It didn't. redis timed out three levels below the tool call. Without the infrastructure spans in the same trace, that line is invisible.
Why Oodle is fast
- Maximum concurrency: thousands of serverless invocations per query.
Why Oodle costs less
- Object storage: 20× cheaper than disk based storage.
How Oodle scales on demand
- Serverless compute scales up for each query, then shuts down when the work is done.
Why Oodle is fast
Maximum concurrency: thousands of serverless invocations per query.
Architecture payoff
Keep everything. Query anything.
Pay 80% less.
Object-storage economics and per-query compute, without sampling, shorter retention, or fewer users.
Illustrative monthly cost
Oodle
80% lower
$2.35K
Grafana Labs
$6K
Datadog
$10.8K
Same representative workload. Expand below for assumptions and line items.
See detailed breakdown: Datadog and Grafana Labs
Common Parameters
Detailed Comparison
| Item | Rate | Cost/mo |
|---|---|---|
| Logs1.6KB/event | $2.50/M events | $5,033 |
| Traces150GB + 1M spans free/APM host · 30d indexed retention · 1M spans ≈ 10GB | $0.10/GB ingested + $2.50/M indexed | $438 |
| Metrics | $5-$1/100 custom metrics (tiered) | $5,000 |
| Hosts | $15/infra + $31/APM host | $230 |
| Containers5 free/host | $0.001/container-hr | $55 |
| Total | $10,756 |
| Item | Rate | Cost/mo |
|---|---|---|
| Data Ingested (Logs + Traces) | $0.30/GB | $1,350 |
| Metrics | $2.00/1K ATS/hr/mo | $1,000 |
| Total | $2,350 |
30 days retention included at no extra charge. Increase retention above 30d to see additional storage cost.
*Metrics: 1 sample per time series every 60s (ATS = active time series / hour). Retention: 30 days included; additional storage billed at $0.001/GB-month. Usage rates shown; see plans above for minimum commitments.
Migration
One platform to replaceDatadog, Grafana, Elastic, and Langfuse
Import dashboards, alerts, evals, and prompts. Keep your existing instrumentation.
100% open standards
PromQL and Grafana-compatible metrics · TraceQL for traces · Lucene-compatible logs · OpenTelemetry native
< 1 day
effort over 1 week
< 1 day
effort over 1 week
1 week
effort over 3 weeks
< 1 day
effort over 1 week
“It took an hour to onboard the data.Getting us up and running took4 or 5 hours end-to-end.”
Olaf Stein
Deployment models
Your data, your rules.
SaaS, your bucket, or your cloud.
SOC 2 Type II · GDPR compliant · ISO 27001 · HIPAA ready · Built-in PII redaction
Fully managed
We run it. Live in minutes.
Bring your own bucket
We process. You keep the storage.
Bring your own cloud
Full stack in your VPC. Data stays put.
Get started
Onboard in two commands.
# install the skill pack
# ingest metrics, logs, traces and import dashboards, alerts
FAQ
Frequently asked questions
Why does the architecture matter - isn't this just another observability tool?
A single agent workflow can trigger 10,000 log events in 3 seconds. Legacy platforms bill per event. At 100 agents in parallel, that's $14,000+ per month in unexpected charges - before you've even asked a question. Disk-backed architectures also fall over when agents fire thousands of parallel investigative queries; fixed compute clusters weren't designed for that pattern. Oodle stores everything on S3 and queries run serverlessly, so cost stays flat and performance scales automatically.
Is it really 80% cheaper? How should I think about the cost model?
How long does drop-in migration actually take?
Talks + writing
