“Cursor-like debugging has transformed how we make decisions.”
Ramakrishna R
Co-founder and CTO, Cureskin
9 petabytesof production telemetry last quarter9 petabytesof production telemetry last quarter —no sampling
AI can't debug what it can't seeObservability built for AI agents
Object storage for telemetry. Serverless compute for queries.Detect and resolve production failures before customers do at 80% lower cost.
Largest customer deployment
Built by engineers behindRubrikAmazon S3DynamoDBSnowflake
0TB+logs/day
0M+time series/hour
0M+AI agent traces/day
<0sP99 query latency
Nagendra Swamy
VP Engineering
Lookout
Cybersecurity leader, trusted by 2k+ enterprises
“We’d used Datadog for eight years and we never thought we could easily switch until Oodle proved otherwise. They migrated thousands of nodes, dashboards, and alerts in weeks. Performance improved, costs dropped, and they’ve been a dependable, transparent partner.
all-in-one
Debug production, not dashboards
Ask questions in plain English directly from Cursor, Claude, AI assistant or Slack
Trace prompts, tool calls, model latency, cost, and quality across LLM apps and agents.
AGENT OBSERVABILITY
The real problem
AI changed debugging. Observability architecture didn't.
Disk-backed, fixed-compute stacks were built for dashboards, not agents querying full-fidelity telemetry at scale. So you sample, cut retention, or pay more.
logs
metrics
traces
100% telemetry5× the cost
Full-fidelity telemetry
Sampling exists because legacy observability makes storing everything too expensive.
∞
elastic scale1,000×
1,000× more queries
Legacy architectures were built for dashboard users, not parallel agent queries.
< 1s
query latency
Instant answers
Agents expect instant answers. Legacy systems were built for interactive UI.
Architecture
Datadog
Grafana
Oodle
A database built foragent-driven debugging
Object storage for telemetry. Serverless compute for queries. Retention, performance, and cost scale independently.
Explore the technology10xLarger Playground
No signup. No gates. Every feature visible.
30M+ metrics500GB+ logs10GB+ traces daily
Running this playground elsewhere would cost
Datadog$2.5M+
Grafana$1.5M+
Oodle~$0.5M
Try Playground
You're probably overpaying by $10,000 a month
Here's the line-item math at 100GB logs/day, 50GB traces/day, 500K active time series
$15k$10k$5k$0
Common Parameters
0 GB1500 GB
0 GB1000 GB
Active time series reporting data each hour. Billed per 1,000 time series per month.
05M
30 days included. Billed at $0.001/GB-month for each additional day of storage beyond 30 days.
30d365d
Detailed Comparison
4.6x cheaper with Oodle
| 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.
cost control
See the spend. Keep the signal.
See spend by team, service, and region—for logs and metrics.
COST ATTRIBUTION
Migration
Drop-in for Elastic, Grafana, Datadog
Automated import of dashboards, alerts, and queries — that’s how these timelines work.
Deployment models
Your data, your rules.
SaaS, your bucket, or your cloud.
SaaS
Fully managed
We run it. Live in minutes.
BYOB
Bring your own bucket
We process. You keep the storage.
BYOC
Bring your own cloud
Full stack in your VPC. Data stays put.
Built differently. Delivers differently.
5×
lower MTTR
Speed
“MCP server is a game changer. Engineers ask from Cursor or Oodle AI Assistant.”
AWS, GCP, Azure5×
lower cost
Efficiency
“10 years of Datadog replaced in 6 weeks. Thousands of nodes, 2000+ alerts, and 300+ dashboards.”
6-week migration< 1 Hour
to production
Simplicity
“20TB/day. 5× faster than Elasticsearch. Developers switched to Oodle from day 2 and never looked back.”
2 week migrationCybersecurity at enterprise scale.
Trusted by a cybersecurity leader serving 2,000+ enterprises including Fortune 500 and federal organizations.
Read the full case study
“60–70% debugging time reduced.”
Engineering Lead
Brackett AI
“CloudWatch reliability. OpenSearch simplicity. Oodle gave us both.”
Brandon Kite
CTO, Fuel
“Setup was a breeze. No hours wasted like with other platforms.”
Anupam Dev G.
Lead Application Architect, HappyPath
“Combining logs and metrics cut our MTTR in half.”
Aneesh Adhikari
Engineer, Bedrock Data
“Give me the RCA. Boom.”
Priyank Raj
Sr Software Engineer, Labra
Agentic onboarding
Two commands. Full observability.
Your AI agent discovers your environment, deploys Oodle, and starts flowing telemetry automatically. No YAML editing, no cluster provisioning, no ops calls.
Try agentic onboarding1
Install Oodle Skills
Add the Oodle skill pack to your AI agent.
>_npx skills add oodle-ai/agent-skills -y
2
Run onboarding
In your agent chat, run the onboarding command:
>_/oodle-onboarding
3
Start asking questions
Your data is flowing. Try one:
Show me error rate by service for the last hour
Which pods are restarting most frequently?
Create a service health dashboard
agent - /oodle-onboarding
PreviewDiscovering your environment...
Found Kubernetes cluster production - 12 nodes, 47 pods. Helm is used to deploy resources.
Deploying monitoring stack:
helm upgrade --install \ oodle-observability \ oodle/oodle-k8s-observability \ --values oodle-values.yaml \ --namespace oodle-monitoring \ --create-namespace --wait
- ✓ eBPF-powered infrastructure metrics
- ✓ Application performance monitoring
- ✓ Logs collection & forwarding
- ✓ Service map & dependency tracking
Frequently Asked Questions
Why does the architecture matter - isn't this just another observability tool?
The architecture is the product. Legacy observability platforms were built on disk-backed, fixed-compute infrastructure - designed for humans generating logs at human speed. When AI agents generate telemetry at machine speed (10,000 events in 3 seconds, 1,000 parallel queries per incident), that architecture either collapses under load or bills you into oblivion. Oodle separates storage from compute: telemetry lands on S3 (elastic, cheap, never needs provisioning), and queries run on serverless compute (spins up per request, scales to any concurrency). The result is full-fidelity telemetry you can actually afford to keep, and query performance that doesn't degrade as your data grows.
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.
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.
What does "debug in plain English" actually mean in practice?
You type a question - "why did checkout fail for iOS users in the last 30 minutes?" - and Oodle queries across logs, metrics, and traces simultaneously to find the answer. No PromQL, no manual log filtering, no switching between tools. Available via the Oodle AI Assistant in-browser, or from Cursor and Claude Code via the Oodle MCP server. Engineers at Fello and Labra describe it as their primary debugging workflow: ask the question, get the RCA, move on.
Is it really 5× cheaper? How should I think about the cost model?
The difference comes from how Oodle charges: by data volume (GB ingested), not by events, spans, hosts, or seats. At 100GB logs/day, 50GB traces/day, and 500K active time series, the math works out to ~$2,350/mo on Oodle versus ~$12k/mo on Datadog's per-event/per-span pricing - roughly 5×. Savings compound at scale. Verified by Lookout (80% reduction), Curefit (3×), and others. Calculate your numbers →
How long does drop-in migration actually take?
Grafana and Elastic: 2–3 hours to connect, a day or two to validate. Full migrations (the scale of Lookout - 1,000s of nodes, 2,000+ alerts, 300+ dashboards): 2–6 weeks. The process is 1-click dashboard and alert import, keep your existing agents with no code changes, run both systems in parallel, cut over when you're confident. View migration guides →
What does "enterprise-grade" mean in concrete terms?
SOC 2 Type II, ISO 27001, GDPR, SSO/SAML, RBAC, 99.9% uptime SLA, petabyte-scale capacity, p99 < 800ms query latency at 20TB+/day and 125M+ time series/hour. Oodle never uses your data to train models. Three deployment options: SaaS (Oodle manages everything), BYOB (your S3, your encryption), or BYOC (full stack in your VPC, network isolation, your KMS keys, zero data egress). Built by engineers behind Amazon S3, DynamoDB, Snowflake, and Rubrik.
