Comparison
Agentry vs Datadog
Datadog is the heavyweight observability platform — infra metrics, APM, distributed tracing, logs, RUM, error tracking, security. It's enormous and priced accordingly. Agentry covers a narrow slice of that surface: three signal types (errors, analytics, deploys), agent-investigated. It does not replace Datadog for infra, APM, or full log aggregation. Pick Agentry only if your "observability" question is really "what broke and why, and what should I ship as a fix."
TL;DR
Pick Datadog if
- You need infrastructure metrics across hundreds of hosts
- You need distributed tracing / APM across services
- You need full-text log aggregation, not just structured events
- You have an SRE team using runbooks, on-call routing, and Cloud SIEM
Pick Agentry if
- You don't need a $50k/yr platform — just errors an agent can fix
- You want the agent to investigate in chat, not you reading dashboards
- Errors + analytics + deploys covers 80% of "why did prod change" for your team
- You're an indie / small team that got quoted $30k/month and wants $0
Feature comparison
| Capability | Datadog | Agentry |
|---|---|---|
| Error monitoring | Yes — Error Tracking SKU | Yes — first-class |
| Infrastructure metrics (host CPU/mem/disk) | Yes — flagship | No |
| APM / distributed tracing | Yes | No (could ingest as logs, no first-class) |
| Log aggregation (full-text across services) | Yes | Structured-event only |
| Real User Monitoring (RUM) | Yes | Limited — error + analytics events only |
| Product analytics (funnels, retention) | No | Yes (HogQL) |
| A/B tests + feature flags | No | Yes |
| Deploy attribution | Yes — Service Catalog | Yes — first-class signal |
| Investigation surface | Web dashboards | Agent in your editor |
| SDK / agent install required | Yes (datadog-agent + per-language libs) | No — ~25 lines of fetch |
| Pricing model | Per-host + per-feature, complex | All features on every plan; priced by events + retention |
When Datadog is the right call
Datadog is the right tool when "observability" means more than application errors. If you need to watch CPU, memory, disk, and network across hundreds of hosts, see distributed traces span across microservices, or search full-text across all your service logs at once, that's Datadog territory and Agentry doesn't try to compete there. The breadth is genuine — infra metrics, APM, logs, RUM, synthetics, security monitoring, all on one ingest and one query plane.
Datadog also wins when you have an SRE team whose workflow depends on it. Runbook automation, on-call rotation routing, anomaly detection across hundreds of custom metrics, Cloud SIEM, Watchdog — these are mature features that organisations build operational processes around. Replacing that with an editor-based agent isn't realistic for a team running serious infra.
And if your environment has compliance requirements that map cleanly onto Datadog's certifications (FedRAMP, HIPAA BAA, etc.), keep what works. Agentry is a much younger product with a narrower compliance posture.
The honest summary: if you're a platform / SRE / infra team, Datadog is the right answer. Agentry is built for product engineering teams whose observability question is much narrower.
When Agentry is the right call
Agentry is the right tool when the only observability question you actually have is "what broke in my app and what should I ship to fix it." For a product engineering team — especially a small one — that question is 80% of how observability gets used in practice, and the rest of Datadog's surface is paid-for capability you don't touch.
Agentry is also the right call when you debug from an AI agent. Datadog's investigation model is "open the dashboard, navigate between views, correlate by hand." Agentry's model is "ask the agent and let it run the queries." The deploy-regression workflow shows what that looks like — one prompt diffs error fingerprints around the latest deploy and tells you what's new.
And the cost dimension is real. Datadog quotes scale with hosts, features, retention, and indexed log volume; for a startup, the invoice is regularly five figures a month. Agentry is free during build, and the surface it covers is built specifically for the product-engineering subset of observability — not the full SRE stack.
Migrating from Datadog
You generally won't migrate off Datadog wholesale —
you'd carve out the error-monitoring + product-analytics +
deploy-attribution slice and let Agentry handle that, while
Datadog keeps doing infra metrics, APM, and logs. The two
coexist comfortably. Point your application error reports at
/v1/logs/, analytics events at
/v1/analytics/, and deploys at
/v1/deploys/. Keep browser and trusted-server credentials
separate; deploys use AGENTRY_CI_API_KEY plus a stable
Idempotency-Key from successful CI/provider automation.
These workflows cover the use cases most teams initially try Agentry for:
-
Find what broke after your last deploy
Datadog's deploy markers, but agent-investigated and with a draft fix at the end.
-
Watch staging — page only on genuinely new errors
Equivalent of Datadog monitor rules, with novel-fingerprint detection built in.
-
Catch rate-limit spikes before customers notice
The kind of pattern you'd build a Datadog monitor for — asked as a question.
-
Diagnose DB pool exhaustion from the symptoms
Agent correlates error patterns to the underlying cause without you tabbing between dashboards.
-
Review product and error signals after a release
A bounded release review grounded in deploy, error, and product-event evidence.
-
Browse all Datadog-alternative workflows →
Every workflow tagged with patterns where Agentry replaces a slice of Datadog usage.
Try Agentry against your real data.
Dual-write for a week. Compare. Keep Datadog for infra; let Agentry handle the agent-investigated slice. The agent handles install — you just paste one prompt.
Agentry gives coding agents production context from errors, product analytics, and deploys through one HTTP API. Fetch and read https://agentry.sh/install.md completely, then install Agentry's full first-time telemetry baseline in this repository. I authorize device-code login, saving the owner key only to ~/.agentry/credentials.json, creating one Agentry project for this repository, and read-only inspection of shipped source plus hidden CI/provider configuration. Deeply analyze the product's business logic and complete value flow before choosing telemetry. Save the complete source-backed plan and show me its exact contents and hash. Until I explicitly approve that plan or request a replacement, do not edit app/CI source, place runtime/CI credentials, commit, exercise product traffic, start proof, push, or deploy. After approval, I authorize only the reviewed targets: place the required scoped browser/server/CI credentials through the established environment or secret mechanism, preserve existing telemetry, implement and test the baseline, commit it, push that reviewed commit when the shipped CI/provider path requires it, exercise safe proof paths with test/non-customer data, and perform one deployment through the reviewed shipped CI/provider path. Ask first if proof would charge money, contact a third party, change real customer data, or require new external access. After the plan is saved, immediately before every onboarding state-changing POST, GET current onboarding state, perform only its single returned next_action, then read state again; do not batch or infer later stages. Continue until status is verified, installation_complete is true, and next_action is null. Keep all secrets, source snapshots, proof markers, and scratch files outside the repository.
- 1. Open your repo in Codex, Claude Code, Cursor etc.
- 2. Paste the install prompt.
- 3. Your agent reads the install doc and shows you an implementation plan for approval.