Datadog Bits AI Alternatives: Cost per Investigation (2026)

The main alternatives to Datadog Bits AI SRE are open source agents that read Datadog data but run outside it (Aurora, HolmesGPT) and other hosted AI SRE agents (Resolve AI, Cleric, NeuBird, Traversal). Bits, which Datadog now calls Bits Investigation, is billed in AI credits, and Datadog lists about 6.5 credits per investigation. Multiplied by Datadog's listed credit prices ($1 a credit at the "starting at" annual rate, $1.30 on demand), that is our estimate of about $6.50 to $8.45 per investigation. Teams leave it when much of their signal lives outside Datadog, when per-investigation billing grows with alert volume, or when the agent or the model has to run in their own network. In our lab, an open source agent's model cost per investigation was $0.19 to $0.84.

Arvo builds Aurora and wrote the lab scenarios. Bits cannot be installed outside Datadog, so we have not tested it; every claim about it below links Datadog's own pages, read on 2026-10-21.

What is Datadog Bits AI SRE?

Bits AI SRE is Datadog's autonomous investigation agent. The docs describe it as an agent that "iteratively forms hypotheses, gathers relevant telemetry, and uses data-based reasoning" to find root causes, and end each investigation with "a clear, evidence-backed conclusion" or mark it inconclusive (investigate issues).

What it reads and does, per Datadog:

• Triggers. Datadog monitor alerts, manually or automatically when a monitor enters alert state, plus Synthetic tests, RUM and a free-text prompt. Supported monitors are metric, anomaly, forecast, integration, outlier, logs, APM metrics, composites, SLOs, Synthetics and RUM (docs).
• Data. Datadog metrics, APM traces, logs, dashboards, events, Change Tracking, source code (GitHub only), Watchdog, RUM, Network Path, Database Monitoring and Continuous Profiler. Grafana, Dynatrace, Sentry, Splunk, ServiceNow and Confluence are listed too, but "Third-party integrations are in Preview for all customers."
• Actions. Seven triage actions from chat, including Slack and Teams messages, paging through Datadog Incident Response, cases and Jira tickets; follow-up rollbacks and other remediation run through Datadog Workflows (blog).
• Controls. RBAC and support for "HIPAA-regulated workloads" (press release), and "zero data retention for third-party AI service providers" (product page).

Datadog describes an internal, replay-based evaluation platform in an engineering post and says the new agent is "more accurate on internal benchmarks". It does not publish the scores.

How is Bits AI SRE priced?

Bits is billed in AI Credits, shared with Bits Chat, Bits Code and Bits Agent Builder. From the Datadog pricing page on 2026-10-21:

Datadog adds that the averages are "provided only for reference" and that actual consumption "may vary with task complexity and the amount of context the model processes." Using its figures, our arithmetic:

• One investigation costs about $6.50 at the "starting at" annual rate of $500 per 500 credits ($1 a credit) and about $8.45 on demand (6.5 x $1.30). Datadog does not show a Monthly Commit price, so that tier is not in the range.
• The smallest annual commitment, 500 credits a month at the starting price, is $6,000 a year and covers about 76 investigations a month if nothing else uses the credits.
• A team that auto-investigates 300 alerts a month uses about 1,950 credits: 4 bundles ($2,000 a month) on an annual commit, or about $2,535 a month on demand.

This sits on top of the Datadog products the agent reads, since Bits investigates Datadog monitors and Datadog data. Admins can cap credits per organization or per user (AI Credit Limits), and when a cap is hit, users "can't use Bits Chat, Bits Investigation, Bits Code, or Bits Agent Builder" until the reset. AI Credit products are not available to FedRAMP customers (docs).

When would you leave Bits AI SRE?

• Your signal is not all in Datadog. Bits' third-party integrations (Grafana, Dynatrace, Sentry, Splunk, ServiceNow, Confluence) are in Preview, and sources such as Loki, Prometheus or PagerDuty are not on its list. Its source code access is GitHub only.
• Alert volume drives cost. Credits scale with every investigation, and automatic investigation on noisy monitors spends credits on noise. An open source agent's marginal cost is its model calls.
• The agent must run in your network. Bits runs inside Datadog's SaaS, and FedRAMP customers cannot use it. Air-gapped or data-residency rules point to a self-hosted agent.
• You want to choose the model. Bits runs on the third-party AI providers Datadog contracts with, under "zero data retention" terms. Aurora, HolmesGPT and K8sGPT take the provider you configure, including local models; Traversal advertises bring-your-own-model, and NeuBird lets you approve models, including ones in your own cloud account.
• You want one investigation layer across vendors. Tying the agent to the observability vendor means changing both together. A separate agent can keep Datadog as one source among several.

If your stack is all Datadog and the per-investigation cost is fine, Bits needs the least setup of anything here: it already has your telemetry, monitors and service tags.

What does an investigation cost with an open source agent?

We measured it. Aurora (commit 87d22baf) on bedrock/us.anthropic.claude-sonnet-5 investigated 19 injected Kubernetes faults twice each. Its model cost per investigation:

What that excludes, and what we got wrong at first:

• Background discovery. When a cluster is connected, Aurora runs a discovery job. In the lab it cost about $1 per cluster connection: $29.70 for 30 completed discovery runs, about twice the $14.53 of the 38 graded investigations (REPORT.md). With one long-lived connection that is a one-off; in the lab each run used a fresh user and org, so discovery ran again and again.
• Hosting and people. You run the containers and the database, and someone upgrades them.
• Other agents. HolmesGPT's cost was not captured: its log says the model "has no entry in litellm's cost map", so it recorded $0. K8sGPT does not report cost.

So the comparison is our estimate of about $6.50 to $8.45 in credits per Bits investigation against tens of cents of model calls plus your own operating cost. Which is cheaper depends on alert volume and on what an engineer's time costs you.

Datadog Bits AI alternatives compared

Checked against each vendor's site or repository on 2026-10-21.

For a wider list, see Resolve AI alternatives and open source SRE agents compared.

How did the open source alternatives do in our lab?

The lab is a small app (nginx frontend, Python api, worker, redis, load generator) on a 3-node kind v0.33.0 cluster with Kubernetes v1.37.0. One fault is injected per scenario (two in scenario 12), two runs per scenario. Aurora and HolmesGPT get the alert; K8sGPT takes no alert and scans the namespace. All three got the same admin kubeconfig, each behind its own guard. Aurora's runs are from 2026-10-02; HolmesGPT 0.43.0 and K8sGPT 0.4.40 ran on 2026-10-15 and 2026-10-16, on the same cluster and faults. There is no Datadog in the lab: the agents read the cluster directly.

A judge model (us.anthropic.claude-opus-5-5) graded every answer. A person at Arvo read all 38 Aurora grades and changed none. The 76 HolmesGPT and K8sGPT grades are judge-model only: a second model re-read all 76 and agreed, a third pass graded them from scratch and agreed on all 38 HolmesGPT verdicts while disputing 6 K8sGPT labels, and no person has checked them. A blinded regrade, with tool names removed from the judge's prompt, matched the published verdict in 38 of 38 Aurora runs, 37 of 38 HolmesGPT runs and 36 of 38 K8sGPT runs. The prompts differ in wording; HolmesGPT used holmes ask with default toolsets, and its allow-list blocked kubectl rollout history in 12 runs; Aurora's default policy blocked the same command in its runs. A two-sided Fisher exact test on the totals, computed on /benchmark, gives p = 0.52 for Aurora against HolmesGPT, across human-read and judge-only grades: treat them as roughly even on accuracy. HolmesGPT is about 3x faster at the median.

K8sGPT is a different tool. It runs analyzers over a namespace and explains what they flag, without an alert. In 12 runs it printed "No problems detected", on faults like a NetworkPolicy block or a slow memory leak where every object looks healthy. Use it as a scanner, not an investigator.

The miss that matters most for alert-driven agents

Bits, Aurora and HolmesGPT all start from an alert. Scenario 12 shows the weakness of that design: a worker OOMKilled fires the alert while, separately, the frontend Service selects no pods. Both agents found the OOM in both runs and neither reported the frontend outage. Aurora went further and stated the opposite:

Other workloads in the shop namespace (api, frontend) remained healthy and unaffected [5, 14].

Pod status was its only evidence. Whatever agent you choose, test it on a second fault the alert does not mention; the checks are in the runbook a second fault the alert does not cover.

Which AI SRE tools let you bring your own LLM?

Aurora, HolmesGPT and K8sGPT take the provider from your config and support local models (all three document Ollama), so the same incident workflow can switch between a hosted and a local model. Traversal's security page offers "self-hosted or customer-managed models", and NeuBird routes to models you approve, including Bedrock, Azure OpenAI and Vertex AI in your own account. Bits uses the AI providers Datadog contracts with, and Resolve combines its own models with frontier models. For air-gapped sites, Aurora ships an air-tight deployment bundle and runs on Ollama; see self-hosted AI SRE.

Traversal vs Datadog Bits AI

Both are hosted investigation agents, but they sit differently. Bits is part of Datadog and reads Datadog first. Traversal sells to enterprises, lists Datadog among its integrations, and offers private on-premises deployments, bring-your-own-cloud and bring-your-own-model on its security page. Traversal publishes no price; Bits does. Neither has run our scenarios.

How to test Bits or any alternative in a trial

Datadog offers a 14-day trial of its product suite. Run the same faults through every candidate: each scenario in Arvo-AI/aurora-lab has the alert, the true root cause and the findings a correct answer must contain.

git clone https://github.com/Arvo-AI/aurora-lab && cd aurora-lab
./lab.sh up
./lab.sh break 12-two-faults-oom-and-selector

For Bits, the cluster must report to a Datadog trial org, since Bits works from Datadog data. To test it the way it runs in production, build a Datadog monitor for the alert yourself; Bits also takes a free-text prompt. Grade the answer against must_identify in the scenario's scenario.yaml, then ./lab.sh reset. For a single failure on your own cluster, use /diagnose.

For Bits next to LogicMonitor's Edwin AI and Dynatrace, see Edwin AI vs Dynatrace Davis AI vs Datadog Bits AI.

Which should you choose?

The lab is Kubernetes-only and synthetic, with faults we wrote, so it says nothing about Datadog-specific signals such as APM traces or RUM. HolmesGPT 0.44 and 0.45 were already out when we ran 0.43.0, and K8sGPT 0.4.41 shipped on 2026-10-21; those versions are untested.

Verified 2026-10-21: Datadog pricing, docs, press releases and blog posts; Resolve AI, Cleric, NeuBird and Traversal pages; GitHub repos as linked. Lab numbers from results.json, generated 2026-10-21.

Aurora is on GitHub under Apache 2.0, or book a demo to run it on your Datadog alerts.