Software Engineering Intelligence Platforms in 2026: 6 Tools Compared (Tested)

What software engineering intelligence platforms measure, and what they miss

If you manage a team where Claude Code, Cursor, and Codex agents now write a large share of the code, you have probably noticed your dashboards getting quietly less useful.

Every established SEI platform reads the same three sources: git hosts (commits, PRs, reviews), delivery systems (CI/CD, issue trackers), and people (surveys). That stack was a fair proxy for engineering work when humans typed the code. The AI-agent era created a data layer underneath all of it: terminal and agent sessions, token spend, parallel agent runs, and work that never becomes a commit at all. An agent can produce a 2,000-line diff in 40 seconds. A developer can spend three hours steering agents through an approach that gets abandoned before anything is pushed. Repo-level tools see the first as a big contribution and the second as nothing.

In practice this means the incumbents are answering "what reached the repo" while managers increasingly need "what happened before the repo, and what did it cost." Some platforms now pull AI adoption stats from vendor admin APIs (Copilot admin data, Cursor org telemetry). That is an aggregate report from the vendor, not a record of the work itself. A number a vendor API asserts and a session you can audit are different kinds of evidence, and the difference matters more as the share of AI-written code grows.

None of this makes the established platforms bad. They are good at what they measure: delivery metrics, allocation, survey signal. It does mean you should know which layer each tool can actually see before you buy.

Comparison table

You are usually choosing between these six with a specific buyer in mind: a VP preparing board reporting, or a manager who wants to see what a 12-person team ships. The table shows where each tool reads its data from and what that costs.

Full disclosure: I build DevClocked, so I compete with everything else on this list and track their products, docs, and pricing closely. The notes below stay within what each vendor publishes, and say so plainly where they publish nothing.

1. DX

What it is. DX is a developer experience and productivity platform built by the researchers behind DORA and SPACE. It combines survey-based measurement (their DXI index), SDLC analytics, and AI-generated-code measurement at the commit and PR level, broken down by team, agent, and repo. Customers include Dropbox, Adyen, and Vanguard.

Best for. Enterprise and mid-market orgs that want a serious, research-grounded developer experience programme with executive reporting attached.

Pros. The research pedigree is real, not marketing. The survey programmes are the most mature in the category, and pairing perception data with SDLC data catches problems git alone cannot. Its AI-code measurement at the commit and PR level is among the most credible in the enterprise tier.

Cons. Sales-led with no public pricing, so budgeting means a sales cycle. Surveys measure perception quarterly; they do not measure what happened on Tuesday. And its AI measurement stops at the commit: DX cannot see local agent sessions, terminal work, session-level token spend, or work that never reaches a commit.

Pricing. No public pricing. Sales-led, enterprise contracts.

Verdict. The strongest choice for an enterprise developer experience programme, and probably the most intellectually honest of the enterprise platforms. If you are comparing it against session-level tooling for a smaller team, the trade-offs are laid out in DevClocked vs DX.

2. Jellyfish

What it is. Jellyfish is an enterprise engineering management platform focused on allocation, costing, and business alignment: where engineering time goes across initiatives, what that costs, and how it maps to company strategy.

Best for. Engineering leadership at large orgs that must answer "what did we spend engineering on this quarter" to a board or CFO.

Pros. Allocation and costing are its home ground, and it presents engineering work in the language finance and the board already speak. For the reporting layer of a large organisation, that translation is the product.

Cons. It is a leadership reporting tool, not a team tool; a line manager gets less day-to-day value than a VP does. Like the rest of the enterprise tier, its data stops at repos and tickets. It has no view into agent sessions or AI spend at the session level.

Pricing. Sales-led, enterprise contracts. No public per-seat number to quote.

Verdict. If your problem is board-level allocation and costing at scale, Jellyfish is the purpose-built answer, and nothing else on this list replaces it.

3. LinearB

What it is. LinearB is a software delivery management platform. It merges git, project management, and CI/CD data into delivery metrics (cycle time, deployment frequency, review efficiency, capacity) and adds gitStream, a workflow automation layer that acts on those metrics, for example auto-routing low-risk PRs.

Best for. Teams whose bottleneck is the delivery pipeline itself: slow reviews, long cycle times, deploy friction.

Pros. The delivery metrics are deep and the automation is the differentiator. Most platforms report that reviews are slow; gitStream can actually change how PRs route. If you have ever watched a one-line PR sit for two days, that automation is the part you will feel.

Cons. No free tier, per-developer annual contracts, sales-led, with tiers starting around 10 developers and a median contract near $26k a year. That prices out small teams. And LinearB sees the PR and everything after it, not the agent sessions and abandoned approaches before it.

Pricing. Per-developer annual contracts, sales-led, no free tier. Median contract around $26k/yr.

Verdict. The best delivery pipeline tool on this list. If your delivery metrics are fine and your open question is what AI-assisted work costs and returns, that comparison is here: DevClocked vs LinearB.

4. Swarmia

What it is. Swarmia is an engineering effectiveness platform built on git and PR data, surveys, and working agreements, organised into three modules: business outcomes, developer productivity, and developer experience. Working agreements are its signature idea: the team sets its own norms (say, reviews answered within a day) and Swarmia tracks them.

Best for. Teams of roughly 10 to 100 that want self-serve setup, a transparent price, and a culture-first framing instead of top-down measurement.

Pros. Free up to 9 developers, public per-seat pricing, and you can be running it the same afternoon without talking to sales. The working-agreements model puts the team in charge of its own norms, which is the least surveillance-shaped posture among the incumbents. That posture is why teams tend to accept it rather than resent it.

Cons. The investment-balance and outcomes views inherit the accuracy of your issue tracker, which is honest work to maintain. All three modules together cost $45/dev/mo, which climbs fast past 30 developers. And the data is repo-and-survey level: no session-level or agent-session visibility.

Pricing. Free up to 9 devs. $23/dev/mo for a single module, $45/dev/mo for all three, billed annually.

Verdict. The best self-serve team health platform, and the pricing transparency is a category example others should copy. Where it and session-level tracking diverge is covered in DevClocked vs Swarmia.

5. Waydev

What it is. Waydev is an engineering intelligence platform with the most aggressive AI posture of the incumbents. AI Adoption 2.0 tracks AI tool usage across a portfolio of assistants; AI Checkpoints record which agent wrote code, tokens consumed, and cost per PR; a conversational layer ("Waydev AI") sits over the connected data. The buyer is enterprise engineering management, priced per active contributor.

Best for. Enterprise orgs whose immediate question is "what is our AI adoption rate and what is it costing us," answered from vendor data at scale.

Pros. Waydev took AI measurement seriously earlier than the other incumbents, and cost-per-PR reporting is a metric most managers have never had. For an executive who needs an adoption narrative this quarter, it ships one.

Cons. The data comes from git APIs and vendor admin telemetry (the Copilot admin API, Cursor org telemetry), so it inherits those APIs' limits: no local sessions, no IDE state, no OS-level work blocks, no human-by-session attribution across vendors. It can report that a team's acceptance rate is 47 percent; it cannot show which sessions produced which shipped work. One factual note for your security review: in 2020 attackers stole customer GitHub and GitLab OAuth tokens from Waydev, which is public record. Any platform that brokers org-wide tokens carries that surface, so ask how it is handled today.

Pricing. Per active contributor, sales-led, enterprise contracts.

Verdict. The strongest AI adoption reporting at enterprise scale, measured from the vendor side. The vendor-aggregate versus audited-session distinction is the whole comparison, and it is spelled out in DevClocked vs Waydev.

6. DevClocked

What it is. DevClocked is developer time tracking and engineering analytics built for AI-assisted development. It captures sessions from IDEs, terminals, and AI coding agents (Claude Code, Cursor, Codex CLI) and ties human hours, agent runs, and token costs to shipped output: commits, PRs, issues. The headline metric is Leverage Score, output per unit of effort. The mechanism is a git baseline plus telemetry from an editor extension and one editor-agnostic CLI, with a model that learns the relationship between the two, because in the agent era commit size stopped mapping to time.

Best for. Teams of roughly 2 to 50 that live in AI agents and want ground-truth attribution: which sessions produced which output, what the AI spend returned, and where effort actually went.

Pros. It is the only tool on this list that reads the session layer rather than a roll-up above it, including terminal and agent work that never becomes a commit. AI-vs-human attribution and token cost tracking come from the session record, not a vendor API's summary. Privacy is structural, not a settings page: no screenshots, no keystroke logging, no file-content capture, session metadata only (durations, repo names, languages). The Business tier adds shared workspaces, org dashboards, and leverage benchmarking at $29/seat/mo.

Cons. It is not an enterprise reporting suite. No survey programmes, no allocation modelling, no board-ready costing views, and a 200-developer org should not pretend otherwise. Accurate tracking requires each developer to install the extension or CLI, which is a real rollout step. And any per-developer data deserves a straight conversation with the team about what is collected and why; session metadata is far less invasive than screenshots, but "less invasive" is an argument you should make openly, not assume.

Pricing. Free at $0 forever (1 repo, 7 days history). A card starts the 7-day full trial. Pro $16/mo, Ultra $26/mo, Business $29/seat/mo ($290/yr/seat, annual gives 2 months free, seats consumed only when an invite is accepted). Full details on pricing.

Verdict. The pick when your question is "what did the agents and the humans each contribute, and what did it cost." Below enterprise scale, that is increasingly the question.

Where DevClocked fits

I build DevClocked, so weigh this section accordingly. If you run 200 developers and need survey programmes, allocation modelling, and board reporting, DX or Jellyfish will serve you better today, and I would tell you that on a sales call. If your bottleneck is the delivery pipeline, LinearB's automation does things DevClocked does not attempt. If you want self-serve team health with working agreements, Swarmia is the better cultural fit. DevClocked wins in one specific place: a team of roughly 2 to 50 where agents write much of the code and the manager wants attribution from the session record rather than a vendor's aggregate. Teams often run it alongside one of the platforms above, one for delivery reporting, one for the layer underneath. If your team barely uses AI tooling yet, start with Swarmia or plain DORA metrics and revisit this when agent spend becomes a line item.

Related Guides

• Engineering productivity metrics: the pillar guide to what is worth measuring and what is theatre.
• How to measure developer productivity: the frameworks behind these platforms, and their limits.
• Engineering manager productivity dashboard: building the view you actually check on Monday.
• Measure AI adoption across an engineering team: adoption versus leverage, and how to tell them apart.
• DORA metrics explained: the four delivery metrics, in their AI-era meaning.