7 Cortex Competitors & Alternatives for 2026

Cortex has a strong footing in the developer portal space, and it handles service catalogs, scorecards, and ownership tracking as well as anyone else in the space.

And with the addition of Eng Intelligence in late 2023, they’ve pushed into engineering analytics too, covering DORA metrics, PR cycle time, and incident tracking.

But while the analytics module is improving, Cortex is still catching up to dedicated engineering intelligence platforms. Executive reporting is limited, data source coverage is narrower, and getting full value from the metrics means investing in Cortex’s broader portal ecosystem.

For teams that primarily need engineering analytics, that trade-off doesn’t always make sense. That’s why we put together this list.

We compared 7 alternatives across features, reviews, pricing, and integrations so you can find the right fit without the compromises.

Why Look for an Alternative to Cortex?

Cortex is a strong developer portal with growing analytics capabilities, but it wasn’t built as an engineering intelligence platform first.

For teams evaluating it primarily for engineering metrics, there are a few common pain points worth knowing about before you commit:

• Limited executive reporting capabilities. Cortex’s built-in dashboards work well for engineering teams, but customizing reports for executive stakeholders has been a pain point. Some users report falling back on external BI tools to bridge the gap. [Read Full Gartner Review]
• Analytics capabilities are still catching up: Cortex has been building out Eng Intelligence since late 2023, with custom dashboards and the Metrics Explorer arriving in 2025. But teams coming from dedicated SEI platforms may find the reporting thinner in areas like investment allocation, capacity planning, or tying engineering output to business outcomes.
• Slow delivery of org-specific feature requests: Some users report frustration with how long it takes for Cortex to ship features that address their organization’s specific needs. For teams with specific workflows or edge cases, that wait time can stall adoption. [Read Full Gartner Review]
• You may end up buying more modules than you need: Cortex sells Eng Intelligence as a standalone product, but the analytics lean heavily on its catalog and scorecards to bring full value. Teams that only want metrics may feel pressure to adopt the broader portal to make the investment worthwhile.
• Analytics depend on your catalog setup: Cortex routes engineering metrics through its service catalog – ownership, team member structure, dependencies. If your catalog is incomplete or poorly maintained, your analytics will reflect those gaps. And getting the catalog right is its own onboarding effort before the metrics become reliable.
• Narrower data source coverage for analytics. Cortex Eng Intelligence pulls primarily from Git, project management, and incident management tools. This is enough for DORA metrics and PR cycle time, but it’s lighter than specialized SEI platforms that also connect financial data, capacity planning inputs, or calendar and collaboration signals.

Key Features to Look For in a Cortex Alternative

The features below are where Cortex falls short and where your choice of alternative will have the most impact on what you can report, plan, and act on:

Executive-Ready Reporting

Cortex’s dashboards are built for engineering managers and team leads, not VPs or finance stakeholders who need investment-level views.

When a VP asks for a custom view of investment allocation or a finance lead needs a breakdown of R&D spend, the platform doesn’t meet them where they are.

Alternatives worth considering should let you configure views by role. Engineering managers see team-level metrics, while leadership gets strategic summaries without a portal login.

Broad Data Source Coverage

Cortex Eng Intelligence connects to Git, Jira, and PagerDuty, which is enough to cover DORA metrics and cycle time, but not much else. The problem is that engineering decisions don’t happen in a code-only context.

Calendar data shows how much time goes to meetings versus focused work, while HR data points to attrition patterns before they become delivery problems. Finance data connects headcount and tooling costs to output. If your platform only sees the dev stack, it can only tell you part of the story.

Engineering-to-Business Alignment

Cortex can’t tell you whether your teams are spending most of their time on strategic priorities or drowning in maintenance, tech debt, and interrupt work.

That’s the gap between delivery metrics and business alignment. If your VP of Engineering needs to show where engineering investment is going, you need a platform with a resource allocation model that does that mapping automatically.

Financial Reporting and DevFinOps

If your finance team relies on manual timesheets to figure out which engineering work is capitalizable, Cortex won’t help. It doesn’t touch financial reporting or R&D cost tracking at all.

The better alternatives pull engineering activity data directly from Git and Jira, classify work into capitalizable categories automatically, and generate audit-ready reports without anyone filling out a spreadsheet.

For engineering leaders who need to justify spend or for finance teams drowning in quarterly capitalization cycles, this is one of the highest-ROI features a platform can offer.

AI Impact Measurement

Most engineering teams are using AI coding tools in some form by now, but very few can tell you what that adoption has done to their delivery speed, code quality, or review cycles. Cortex has mentioned that AI measurement is on the roadmap, but it’s not there yet.

There are a few platforms that are ahead here. They track how AI adoption changes deployment frequency, cycle time, code quality, and review patterns across teams.

If your org is spending on AI tooling and expects to see ROI data at some point, this is a feature gap worth factoring into your decision.

Top Alternatives to Cortex on the Market Right Now

Each platform on this list takes a different angle on engineering intelligence. Some go wide across analytics, planning, and financial reporting, while others go deep on developer experience or fast setup.

The table below breaks down what sets each one apart, along with pricing and trial availability, so you can narrow your shortlist before we go into the full write-ups:

Platform	Key Differentiator	Pricing	Free Trial or Plan
Jellyfish	Most comprehensive engineering intelligence platform on the market – comes with analytics, planning, financial reporting, and AI measurement in one place	Custom pricing – contact sales for a quote	No free plan – demo available on request
Harness SEI	Engineering analytics embedded natively in a broader CI/CD and software delivery platform	Free tier available – paid plans (Essentials and Enterprise) require a sales conversation	Yes – free tier for smaller teams
DX (Atlassian)	Survey-first approach built by the researchers behind DORA and SPACE, focused on developer experience over delivery metrics	Based on feature selection and seat count – contact sales	No public free tier
Waydev	Broad SEI coverage with AI adoption tracking and cost capitalization at a lower price point than most enterprise alternatives.	Pro at $29/mo, Premium at $54/mo, Enterprise is custom – all per contributor, billed annually	Yes – free trial available
Hivel	Separates human-written code from AI-generated output to measure real AI tool impact on delivery.	Growing at $20/contributor/mo, Enterprise at $35/contributor/mo, billed annually.	Yes – free plan for early-stage startups.
Typo	Lightweight setup that connects to GitHub and Jira and populates dashboards within hours	Free for up to 5 contributors – Starter at $20/mo, Pro at $28/mo, and Enterprise is custom	Yes – free plan and paid tiers available.
Uplevel	Pulls calendar and chat data to measure deep work time and collaboration overhead alongside delivery metrics	Custom pricing for enterprise – contact sales	No public free tier.

1. Jellyfish

Jellyfish is a full-stack engineering intelligence platform that covers more ground than any other tool in the SEI category. It includes everything from DORA metrics and delivery health to investment allocation, capacity planning, financial reporting, and AI tool ROI measurement.

It’s trusted by 500+ engineering organizations, including Priceline, PagerDuty, and GoodRx, with over 11,000 teams on the platform. On G2, Jellyfish has held the #1 spot in the Software Development Analytics Tools grid for 14 straight quarters.

Cortex ties its engineering intelligence to a developer portal and service catalog, which means you’re investing in the broader ecosystem to get value from the analytics.

On the other side, Jellyfish is built as an end-to-end intelligence platform with broader data coverage and deeper executive reporting. It also has capabilities like DevFinOps and scenario planning that Cortex doesn’t touch.

Key Features

• Patented investment allocation model: The platform’s patented allocation model categorizes engineering work automatically across features, tech debt, maintenance, and unplanned work. No one has to tag tickets or track time.
• Capacity planning and scenario modeling: The capacity planning tool uses past performance to project what’s realistic for the next quarter, while the scenario planner lets leaders model trade-offs and see the impact before making a call.
• DevFinOps and automated cost capitalization: The DevFinOps module connects engineering activity data with HR cost data to automate R&D cost capitalization. It classifies work, calculates effort, and produces audit-ready reports.
• AI impact measurement across the full tool stack: Jellyfish gives leaders a vendor-neutral view of AI tool impact. It tracks which teams use Copilot, Cursor, Claude Code, or Amazon Q, how much you’re spending, and whether adoption moves the needle on throughput, quality, and cycle time.
• Developer experience surveys with system data integration: Jellyfish combines developer survey data with delivery metrics from your toolchain to give a two-sided view of team health. Engineers point out what’s creating friction, and the system data shows whether fixing those issues moves the performance needle.

Why Do Companies Choose Jellyfish Over Cortex?

Cortex is a strong developer portal, but teams that evaluate it primarily for engineering intelligence tend to hit the same gaps.

Here’s where Jellyfish pulls ahead:

• Executive-ready reporting without a BI tool on top: Cortex’s dashboards are built for engineering managers, and users report falling back on external BI tools when they need to report to leadership. Jellyfish is designed to serve engineering, product, and finance stakeholders from the same platform.
• Trade-off modeling before you commit headcount or scope: Cortex has no capacity planning or scenario modeling capabilities. Jellyfish lets leaders forecast what teams can deliver based on historical data, model resource trade-offs across initiatives, and pressure-test plans before committing.
• Automatic mapping of engineering work to strategic priorities: Cortex can tell you how fast code moves through the pipeline, but it can’t show you whether engineering effort maps to business priorities. Jellyfish’s patented allocation model automatically connects engineering work to strategic categories, so leaders can see where investment is going.
• DevFinOps that takes finance off engineering’s back: If your finance team needs capitalization data, Cortex can’t help. Jellyfish automates R&D cost reporting by pulling effort data directly from your engineering tools and HR systems. It classifies work into capitalizable categories and produces reports that hold up under audit.
• Vendor-neutral AI measurement backed by benchmark research: AI coding tool measurement is on Cortex’s roadmap, but not available today. Jellyfish is already measuring tool adoption and ROI across Copilot, Cursor, Claude Code, Amazon Q, and new agentic workflows. It also backs it up with the industry’s largest ongoing benchmark study on how AI tools change engineering work.

What Real Customers Are Saying about Jellyfish

Iterable was tracking R&D costs through manual Jira tagging, which produced patchy data no one fully trusted. Jellyfish automated cost allocation, cleaned up the reporting, and gave leadership a delivery view they now check weekly to steer priorities and support team performance.

Five9 reorganized 400+ engineers from four separate groups into a single org. Jellyfish gave leadership a consistent way to track allocations and sprint predictability across all of them, which the company had never been able to do before the consolidation.

Buildium felt like too much engineering effort was going to the wrong places, but had nothing concrete to point to. Jellyfish gave them actionable insights and found a categorization problem that had been skewing their metrics. Three months later, the team had redirected 24% more resources toward roadmap delivery, without a single new hire.

2. Harness

Harness SEI is an engineering intelligence module built into the main Harness software delivery platform. It pulls data from 40+ tools across the SDLC to measure productivity, track DORA metrics, and point out blockers in delivery workflows.

It’s popular with larger engineering orgs that already use Harness for CI/CD and want engineering analytics embedded in the same platform.

Compared to Cortex, Harness goes deeper into delivery pipeline analytics and broader into tool integrations. However, it doesn’t offer the service catalog, scorecards, or ownership tracking that make up Cortex’s portal side.

Key Features

• DORA and SPACE framework support out of the box: Harness ships with pre-built dashboards for both DORA and SPACE metrics. Teams can benchmark delivery performance and user experience without building custom reports from scratch.
• 40+ tool integrations across the SDLC: The tool connects to Git providers, CI/CD tools like Jenkins and CircleCI, Jira, PagerDuty, Azure DevOps, and more. That’s a wider integration stack than Cortex, which focuses primarily on Git, Jira, and incident management systems.
• Investment allocation tracking: Harness SEI breaks down where engineering time goes across categories like features, bugs, and tech debt, so leaders can validate that resource allocation matches business priorities.

Advantages

• Quick to learn, broad enough to grow into: The feature set covers a lot of ground without making you feel like you need a certification to use it. Teams report getting comfortable with the analytics platform faster than expected. [Read Full G2 Review]
• Well-documented and well-supported: Harness gets good mentions for the quality of its documentation and hands-on support during onboarding. This is especially important for teams integrating across a large toolchain. [Read Full G2 Review]
• Lean, user-friendly interface: The UI doesn’t try to do everything. Users note that every feature feels intentional, with no unnecessary complexity or bloat getting in the way of the core analytics workflows. [Read Full G2 Review]

Limitations

• Configuration can get complex: The day-to-day interface is straightforward, but the initial setup can feel overwhelming for teams that are new to engineering intelligence tooling. [Read Full G2 Review]
• Uneven feature maturity: The core metrics and dashboards are strong, but some of the newer or more niche capabilities feel like they’re still catching up. Users report that not every feature has the same level of depth. [Read Full G2 Review]
• Cost can be hard to justify for smaller teams: The platform is priced for enterprise, and users mention that without volume discounts, the investment is steep, particularly if you’re only using the SEI module. [Read Full G2 Review]

Pricing

Harness offers a free tier for smaller teams, with two paid plans (Essentials and Enterprise) above that. Neither paid plan shows pricing on the site, so you’ll need to contact sales to get a quote.

3. DX (Atlassian)

DX is a developer intelligence platform (now owned by Atlassian) that measures software engineering productivity by pairing structured developer surveys with system metrics from across the dev toolchain.

One major selling point is that it was built by the researchers behind DORA and SPACE, so the methodology has academic weight behind it. Its proprietary Developer Experience Index (DXI) ties experience improvements directly to time savings and financial outcomes.

If Cortex is about tracking what your systems produce, DX is about understanding what your people experience. There’s some metric overlap in DORA and cycle time, but the core use cases are pretty different.

Key Features

• Developer Experience Index (DXI): DX’s core metric rolls 14 survey-based data points into a single score that represents the overall developer experience at your company.
• Benchmarking against 500+ companies: The platform includes proprietary engineering benchmarks drawn from data across hundreds of enterprise orgs. You can compare your DXI, delivery speed, and experience scores against peers of similar size and industry.
• Research-backed surveys with 90%+ participation: The surveys come from the researchers behind DORA and SPACE, and DX reports participation rates above 90% across its customer base.

Advantages

• Helps you spot systemic issues, not just local ones: DX points out friction points that cut across teams, so you can prioritize improvements that have a broad impact instead of fixing the same problem in one group at a time. [Read Full G2 Review]
• Feedback that’s fast to give and fast to use: Snapshots take minutes to complete and return results quickly. Users mention this as the most practical part of the platform because it keeps feedback loops tight rather than quarterly. [Read Full G2 Review]
• Custom reporting without the manual work: DX’s AI query builder lets you access and slice your data without building dashboards from scratch. For leaders who need a specific cut of the data for a meeting or a decision, it removes a lot of friction. [Read Full G2 Review]

Limitations

• Calendar integration lacks granularity: DX can pull in calendar data to show the balance between meetings and deep work, but there’s no way to filter out specific meeting types yet. [Read Full G2 Review]
• You need internal buy-in to make it stick: The platform gives engineering leaders strong data, but it works best when teams are already mature enough to receive feedback and act on it. [Read Full G2 Review]
• Steep path from features to practice: DX packs a lot into the platform, but users report a gap between what’s available and knowing what to do with it. Without more structured guidance, some capabilities sit unused. [Read Full G2 Review]

Pricing

No public pricing on the DX site. The platform charges based on feature selection and developer seat count, which means you’ll need to talk to sales before you can compare costs against other tools.

4. Waydev

Waydev is an SEI platform that pulls data from across the development stack to track DORA metrics, sprint health, resource allocation, and AI tool adoption for engineering leaders and their teams.

Cortex comes at engineering metrics from the portal side, while Waydev comes at it from pure analytics. It comes with broader data inputs, resource allocation tracking, and cost visibility that Cortex doesn’t offer.

Key Features

• Resource allocation and cost visibility: The platform breaks down where engineering effort goes at the project and epic level, so you can see how time splits across new features, maintenance, and tech debt.
• Cost capitalization reporting: Waydev includes a cost capitalization module that classifies engineering work and streamlines exportable reports for finance teams.
• AI adoption tracking: It tracks usage of AI coding tools like Copilot, Cursor, Claude, and Devin alongside delivery metrics. Engineering leaders can see whether AI adoption is translating into faster output.

Advantages

• Quick to set up with useful defaults: The platform ships with pre-built dashboards that give you a top-level view of project performance right away. [Read Full G2 Review]p
• Responsive customer support team: Waydev’s team gets praise for being hands-on, quick to respond, and proactive about helping customers get full value from the platform. [Read Full G2 Review]
• Built-in progress summaries that save time: The automated weekly reports let managers track and optimize delivery status without scheduling extra standups or chasing updates manually. [Read Full G2 Review]

Limitations

• Developer experience surveys need more depth: Waydev includes a DevEx survey module, but users find it underdeveloped compared to the rest of the platform. [Read Full G2 Review]
• Permission controls are still basic: The platform’s role-based access controls don’t yet meet the standards users expect from enterprise software. [Read Full G2 Review]
• Limited historical data access: Users say that they can’t pull data far enough back to compare older projects or track long-term trends. For teams that need multi-year baselines, that can be a problem. [Read Full G2 Review]

Pricing

Waydev offers a free trial and three annual plans priced per contributor:

• Pro starts at $29/month and covers DORA metrics and dashboards
• Premium runs $54/month and has resource planning, DevEx surveys, AI adoption tracking, and API access
• Enterprise pricing requires a sales conversation but removes usage limits and includes on-prem deployment, SSO, and dedicated support

5. Hivel

Hivel is an engineering intelligence platform built for leaders who want a single view of delivery health, team performance, and AI tool impact without managing an internal developer portal or service catalog underneath it.

The biggest difference is scope. Cortex wants to be your developer portal, service catalog, and analytics layer all in one. Hivel only does the analytics part, but goes deeper on investment tracking, AI attribution, and cost capitalization.

Key Features

• AI code attribution: Hivel separates human-written code from AI-generated code in your delivery data, so you can measure whether AI tools are producing work that reaches production or just creating more review overhead.
• DORA and SPACE metrics out of the box: The platform tracks both SPACE and DORA metrics You get pre-built dashboards for both without any custom configuration.
• AI-driven code review agent: Hivel includes a code review agent that takes a first pass on PRs. It outlines linting issues, security concerns, and business logic problems before a human reviewer gets involved.

Advantages

• Easy to use and to adopt: The dashboard is simple enough that teams adopt it without being pushed. Users describe it as their go-to for tracking developer productivity metrics day to day, which says more about usability than any feature list. [Read Full G2 Review]
• Support that feels like a partnership: Response times are under 24 hours, with the team willing to jump on calls to resolve issues in real time. For a smaller platform, the support experience punches well above its weight. [Read Full G2 Review]
• AI code reviews that save reviewer time: Hivel’s AI review agent catches common issues before human reviewers step in. It’s not flawless, but it takes enough off senior reviewers’ plates to make a noticeable difference. [Read Full G2 Review]

Limitations

• Limited customization and no offline access: Multiple users pointed out that the platform doesn’t offer enough flexibility to tailor views and workflows to their specific needs. There’s also no offline mode. [Read Full G2 Review]
• Reporting inconsistencies in goal tracking: Some teams report that goals tracked in days and hours export as minutes, which forces manual recalculation to make sense of the data. [Read Full G2 Review]
• Could use better onboarding for new users: The depth of available data is a strength once you know the platform, but first-time users report needing more contextual cues to navigate confidently. [Read Full G2 Review]

Pricing

Hivel offers a free plan for early-stage startups, with two paid tiers billed annually per contributor.

The Growing plan runs $20/contributor/month and adds more analytical depth. Enterprise comes in at $35/contributor/month and includes compliance features like on-prem deployment, SSO, and RBAC.

6. Typo

Typo is a lightweight software delivery intelligence platform that sits on top of your dev toolchain and combines engineering analytics with AI automation.

Compared to Cortex, Typo skips the setup overhead. You connect your GitHub and Jira, and the dashboards populate within hours. Cortex ties its analytics to a service catalog that takes much more effort to build and maintain before you see meaningful data.

Key Features

• DORA metrics and sprint tracking: Typo covers deployment frequency, lead time, change failure rate, and MTTR natively, alongside sprint data that shows scope changes and velocity over time.
• AI-powered code review (CodeIQ): It runs automated reviews on every PR using a mix of static analysis and LLM-based feedback. It doesn’t replace human reviewers, but it handles the routine tasks so they can focus on architecture and design decisions.
• Investment distribution: The platform tags PRs automatically by work type (new features, bug fixes, tech debt, and maintenance) based on your commit and Jira data.

Advantages

• Instant feedback that keeps engineers engaged: Typo gives developers real-time signals on their speed, quality, and progress. Users say this makes the platform feel like a personal improvement tool rather than a monitoring system. [Read Full G2 Review]
• Makes sprint reporting effortless. The platform automates the reporting that used to take hours, which is especially helpful for newer managers still building their workflows. [Read Full G2 Review]
• Metrics that teams don’t resent: Typo frames its data around finding patterns and improving workflow automation, not ranking individuals. Benchmarks are available, but the platform doesn’t encourage using data as a stick. [Read Full G2 Review]

Limitations

• Customization can feel limiting: Teams with non-standard workflows or complex reporting needs may hit walls with the current dashboard and metrics options. [Read Full G2 Review]
• Some features still feel early: Users note that parts of the platform aren’t fully polished yet, which is expected for a newer product. The team ships fixes and updates quickly, but depending on which features you rely on, you may run into rough edges. [Read Full G2 Review]
• Some metrics need more in-product context: The platform tracks a lot of data, but users say certain metrics lack enough explanation in the interface to be interpreted confidently. [Read Full G2 Review]

Pricing

Typo offers four tiers, all priced per developer:

• Free — 5 contributors, 5 repos, basic DORA and PR metrics
• Starter ($20/mo) — sprint tracking, investment distribution, burnout insights, 6 months of history
• Pro ($28/mo) — AI code review, automated fixes, code health, unlimited history
• Enterprise (custom) — on-prem, software capitalization, and custom integrations

7. Uplevel

Uplevel is an engineering intelligence platform that combines development, calendar, and collaboration data to help enterprise engineering leaders measure delivery health and understand how their teams spend time.

Where Cortex routes its analytics through a service catalog, Uplevel skips the portal module entirely and goes wider on data inputs. It pulls in signals from calendars and chat tools that Cortex doesn’t touch.

Key Features

• Deep work and maker time tracking: Uplevel measures how much uninterrupted focus time engineers get each day by analyzing calendar and chat data alongside coding activity.
• ML-powered work classification: It uses a machine learning model to automatically classify engineering work into categories like new features, tech debt, and bug fixes (even when Jira hygiene is inconsistent).
• Executive dashboards with org-wide rollups: The platform includes a dedicated executive reporting module (built on Tableau) that gives the C-suite a data-driven overview of lifecycle performance across teams, regions, and time periods.

Advantages

• Collaboration overhead made measurable: The platform tracks time spent in messaging tools alongside coding activity, so leaders can see what’s eating into focused work. For teams trying to reduce chat overload, having that data in one place makes the conversation easier to have. [Read Full G2 Review]
• Responsive product team that evolves with you: Uplevel’s team acts on customer feedback quickly and rolls out new capabilities at a steady pace. If you’ve been burned by vendors who go quiet after the sale, this is worth noting. [Read Full G2 Review]
• Clean dashboards with reliable data delivery: The reporting interface is straightforward to read, and users report that data notifications show up on time and consistently. [Read Full G2 Review]

Limitations

• Some metrics need more context to interpret: Users note that certain data points aren’t always self-explanatory, and figuring out what’s driving a specific trend can take extra effort. [Read Full G2 Review]
• Weak API support for custom workflows: The platform doesn’t offer much in the way of API access or data export. This limits how teams can integrate Uplevel metrics into broader automation or reporting pipelines. [Read Full G2 Review]
• Resource allocation features still feel early: Uplevel tracks where engineering time goes, but users say the allocation module lacks depth, particularly around predictive modeling and scenario planning. [Read Full G2 Review]

Pricing

No public pricing available. Uplevel targets enterprise engineering orgs, and all plans require a sales conversation to get a quote.

How to Select the Right Cortex Alternative for Your Needs

Here’s how each platform lines up against the most common use cases we see engineering leaders evaluating:

• Teams that need a full-spectrum engineering intelligence platform should start with Jellyfish. It covers executive reporting, investment allocation, capacity planning, financial reporting, AI impact measurement, and business alignment in one place.
• Teams that need developer experience and survey-driven insights will get the most from DX (Atlassian). Its research-backed surveys and DXI scoring are purpose-built for measuring the human side of engineering.
• Teams that need financial reporting and R&D cost capitalization have two solid options. Waydev offers a cost cap module at a lower price point, while Jellyfish goes deeper with audit-ready reports and tighter integration with finance workflows.
• Teams that need to stay within the Harness ecosystem will find Harness SEI the easiest add-on. It covers DORA and SPACE frameworks natively and goes deep on pipeline analytics. Though it doesn’t offer the financial reporting or capacity planning that a platform like Jellyfish provides.
• Teams that need fast time-to-value without a heavy setup should look at Typo and Hivel. Both connect to Git and Jira, populate dashboards within hours, and skip the catalog and portal overhead.
• Teams that need visibility into deep work, meeting load, and burnout risk will find Uplevel covers ground that most SEI platforms don’t. It pulls in calendar and chat data to understand the human factors behind delivery metrics.

Jellyfish – The Ideal Cortex Alternative

Cortex can get you started with delivery metrics, but teams that need executive reporting, investment tracking, and planning tools hit a ceiling fast.

Jellyfish covers all of that in one product, and it was built specifically for engineering intelligence, not patched onto a developer portal.

You get the full stack of engineering intelligence in one place:

• A patented allocation model that shows where engineering investment goes without anyone tracking time or tagging tickets
• Role-specific dashboards that give VPs the strategic view and engineering managers the team-level detail, from the same platform
• A DevFinOps module that replaces manual timesheets with automated, audit-ready capitalization reports
• Vendor-neutral AI measurement that tracks adoption, spend, and delivery impact for every major coding tool
• Capacity and scenario planners that let you model headcount, scope, and trade-offs before locking in a plan
• DevEx Surveys that pair developer feedback with system data to show what’s causing friction and whether fixes move the needle

Engineering leaders at Priceline, PagerDuty, GoodRx, and 500+ other organizations already use Jellyfish to make better decisions with better data.

Book a demo to see how.

FAQs

Can Cortex replace an ERP for engineering cost tracking?

No. Cortex doesn’t handle financial data, R&D cost capitalization, or resource cost modeling. An ERP manages broad financial and operational workflows across the business, while Cortex sits in a completely different layer.

If your finance team needs engineering cost data, you’ll need either an ERP integration or a platform like Jellyfish that has a DevFinOps module built in and can feed audit-ready reports into your existing financial systems.

How does Cortex’s data model handle microservices architectures?

Cortex maps microservices through its service catalog, where each service gets defined with ownership, dependencies, and metadata.

The data model works well for tracking what exists and who owns it, but the engineering analytics layer depends on that catalog being accurate and complete.

For orgs running hundreds of microservices, keeping the catalog current is an ongoing effort.

Do engineering intelligence platforms improve operational efficiency for SaaS teams?

They can, but the impact depends on what you do with the data.

Platforms in this space find bottlenecks in delivery workflows, flag where engineering time is going, and help leaders make resource decisions based on system data.

For SaaS orgs shipping frequently, that visibility tends to compress cycle times and reduce wasted effort. The biggest efficiency gains usually come from self-service reporting – when managers and execs can pull the views they need at a glance.

Is Cortex the same as Backstage?

No. Backstage is Spotify’s open-source developer portal framework – you build and maintain it yourself using community plugins and your own infrastructure.

Cortex is a managed platform that offers a service catalog, scorecards, and engineering intelligence out of the box.

Backstage gives you more flexibility if you have the engineering capacity to support it, but Cortex removes the build-and-maintain burden.

Teams that want analytics without running their own portal often find that neither option fits perfectly, which is part of why dedicated engineering intelligence platforms exist.