Bitbucket Removed Insights — What to Use for PR Analytics Now

What Bitbucket Insights provided

Bitbucket Insights was a tab available inside Bitbucket Cloud repositories that showed a set of developer productivity metrics for a selected time range. It was modest by modern standards — no DORA metrics, no per-engineer breakdowns, no trend analysis — but it gave teams a quick pulse on PR activity without leaving Bitbucket.

The core metrics Insights surfaced were:

• PR throughput: How many pull requests were opened, updated, and merged in the selected period, broken down by week.
• Review time: Average time from PR creation to first review, and from first review to merge. This was the most useful metric in Insights — even a rough average was actionable for teams with slow review processes.
• Build results: A count of Bitbucket Pipelines runs and their pass/fail split. This was pipeline health at a glance, not DORA-style deployment frequency analysis.
• Code coverage: If you had a code coverage reporter integrated with Bitbucket Pipelines, Insights would surface the current coverage percentage and recent trend. This required additional configuration and not all teams had it active.

Insights was repository-scoped, not organization-scoped. If your organization had 15 Bitbucket repositories, you had to visit 15 separate Insights tabs to get a picture of your overall engineering output. There was no cross-repo rollup, no team-level view, and no way to compare repos or engineers side by side.

Despite these limitations, Insights was the only native analytics feature Bitbucket offered. Teams built their first metrics habit around it, using the review time data to benchmark code review performance and the PR throughput chart to track sprint velocity. When it disappeared, those teams lost their baseline.

When and why Atlassian removed Bitbucket Insights

Atlassian removed the Insights tab from Bitbucket Cloud without a public announcement or a timeline warning. Teams discovered the removal when they navigated to a repository and found the Insights tab simply absent. Atlassian support confirmed the removal when customers filed tickets, citing a focus on core Bitbucket functionality.

The removal is consistent with Atlassian's broader product strategy. Atlassian has been consolidating its analytics investment into two products: Atlassian Analytics (a data warehouse and reporting tool available to Enterprise customers) and the Jira Work Management view for team-level reporting. Neither of these replaces Bitbucket Insights for engineering-specific metrics. Atlassian Analytics requires data export configuration and is aimed at business intelligence use cases. Jira Work Management focuses on issue tracking, not code review or deployment analytics.

The practical conclusion for Bitbucket Cloud teams is that Atlassian does not plan to rebuild Bitbucket Insights. If you want PR analytics, DORA metrics, or any form of engineering intelligence from your Bitbucket data, you need a third-party tool.

What teams are losing without a replacement

The loss of Bitbucket Insights creates real gaps in engineering visibility that accumulate over time. Teams that relied on Insights for even basic PR review time data are now operating without any systematic measurement of their code review process. This matters because code review bottlenecks are one of the most common causes of slow delivery — and without data, they are almost impossible to identify and address.

Specifically, engineering leaders at Bitbucket-heavy organizations are now missing:

• Lead time visibility: Without PR cycle time data, engineering managers cannot measure how long it takes for completed work to reach users. This makes it difficult to identify where the delivery process slows down — is it in review, in merge queues, or in deployment pipelines?
• Review bottleneck detection: Insights gave a rough view of which PRs were sitting longest without review. Without it, review bottlenecks are invisible until they become sprint-blocking.
• Deployment frequency baseline:Many teams used Insights' build results view as a proxy for deployment frequency. Without it, tracking the DORA deployment frequency metric requires either building a custom integration or adopting a third-party tool.
• Team health reporting:Engineering managers often used Insights to prepare for sprint retrospectives and one-on-ones. Without native analytics, this preparation now requires manual data gathering from Bitbucket's API or switching tools.

The best alternatives for Bitbucket PR analytics in 2026

The right replacement depends on what you were using Insights for and how much analytics depth your team needs. Here are the options worth evaluating, in order of analytical depth.

Koalr — full engineering metrics for Bitbucket Cloud

Koalr is an engineering metrics platform built to connect directly to Bitbucket Cloud repos and pipelines. It calculates all four DORA metrics (deploy frequency, lead time for changes, change failure rate, and MTTR) from your actual Bitbucket data — not approximations or manual imports.

Beyond DORA metrics, Koalr provides PR cycle time broken down by stage (time to first review, review duration, time from approval to merge), deploy risk scoring for every PR before it merges, and team benchmarks against DORA performance tiers. It backfills 90 days of history on first connection, so you have trend data from day one.

For teams that used Bitbucket Insights primarily for review time data, Koalr is the most direct replacement — it gives you the same metric with substantially more depth and context. For teams that want DORA metrics they never had in Insights, it fills the gap entirely. See Koalr's Bitbucket integration.

LinearB — engineering metrics with workflow integration

LinearB connects to Bitbucket Cloud and provides DORA metrics, PR cycle time, and developer productivity data. It also has a workflow automation layer that can send Slack notifications for stalled PRs and suggest reviewers based on file ownership.

LinearB's strength is its integration with project management tools. If your team uses Jira (which is common among Bitbucket shops), LinearB can correlate Jira issues with Bitbucket PRs to give you a more complete picture of delivery performance. The tradeoff is complexity — LinearB has a larger configuration surface and a steeper onboarding curve than simpler analytics tools.

Swarmia — team health metrics with GitHub bias

Swarmia provides DORA metrics, investment distribution reporting (how engineering time is split across features, tech debt, and bugs), and developer experience surveys. It has Bitbucket support, but GitHub is its primary integration. Teams that are primarily on Bitbucket may find that some Swarmia features assume GitHub-specific data structures.

Swarmia is a good fit for teams that want to combine quantitative metrics (DORA, throughput) with qualitative data (developer surveys and focus time analysis). It is less focused on deployment risk and more focused on team health and sustainability indicators.

Jellyfish — engineering-business alignment at enterprise scale

Jellyfish connects to Bitbucket, Jira, and financial data to provide an engineering-business alignment view: what percentage of engineering capacity is going to new features versus maintenance, how engineering investment maps to business outcomes, and executive-level delivery reporting.

Jellyfish is best suited to larger engineering organizations (200+ engineers) where the primary analytics consumer is a VP of Engineering or CTO who needs to present engineering ROI to the board. For smaller teams that just want to understand their code review process, Jellyfish is likely over-engineered for the use case.

What to look for in a Bitbucket analytics replacement

Before committing to a tool, verify these capabilities against your actual requirements:

• Native Bitbucket Cloud OAuth integration: Some tools only support Bitbucket Server (the self-hosted version) or have limited Bitbucket Cloud support. Confirm that the tool connects directly to Bitbucket Cloud via OAuth, not just via a webhook or manual CSV export.
• PR stage breakdown, not just averages: Average review time is a start, but time to first review, review duration, and merge lag are distinct metrics with different interventions. Make sure the tool surfaces all three separately.
• DORA metric accuracy:Deployment frequency should come from Bitbucket Pipelines deployment events, not from merge events or tag pushes. Ask vendors how they define deployment and whether they use Bitbucket's deployment environment API.
• Historical backfill: A tool that only measures from the connection date forward is much less useful than one that backfills 90 days of history. Your first dashboard should have trend data, not just a current snapshot.

The bottom line

Bitbucket Insights is gone, and Atlassian is not planning to rebuild it. Bitbucket will remain a solid repository host — Atlassian continues to invest in the core version control and pipeline features — but engineering analytics is not part of that roadmap.

For engineering teams that need PR cycle time, DORA metrics, or any form of systematic delivery measurement from their Bitbucket data, a dedicated analytics tool is now required. The good news is that the purpose-built options in 2026 are significantly more capable than Insights ever was.