Marketing data and research
Find external data for market research, competitor analysis and content: data tools, public datasets, industry benchmarks, surveys and reports. Compare coverage, access and limitations.
Compare SaaS growth and retention metrics with published peer benchmarks and explore how reported distributions change by quarter.
Coverage: Global participating SaaS companies; report coverage varies · Quarterly growth-rate and net-revenue-retention benchmark distributions, plus report-specific analyses
Compare a private B2B SaaS company's growth context with survey-reported peers and review retention and financing comparisons.
Coverage: Private B2B SaaS companies; survey coverage is described by SaaS Capital · Growth-rate and retention benchmarks with comparisons such as bootstrapped versus equity-backed companies
Review annual survey findings and compare a SaaS company's operating context with the report's published benchmark categories.
Coverage: 2025 survey: 69% of respondents in the US and 17% in Europe · Company growth, efficiency, retention and operating benchmarks with report-specific survey cuts
Explore B2B SaaS benchmark distributions by operating model and compare growth and acquisition-efficiency metrics.
Coverage: B2B SaaS companies represented in Maxio's dataset; coverage is not fully specified · ARR growth, CAC ratios, CAC payback and net revenue retention, with filters for company size, pricing model and go-to-market motion
Size AI opportunity areas, benchmark model capability and investment trends, and support market narratives with a cited longitudinal research source. Useful to AI infrastructure and data-product founders who need credible evidence on AI adoption, capability, economics, and policy.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Size AI opportunity areas, benchmark model capability and investment trends, and support market narratives with a cited longitudinal research source.
Estimate infrastructure adoption and buyer priorities for developer platforms, Kubernetes tooling, observability, and data infrastructure. Directly relevant to founders selling cloud-native developer tools, platform infrastructure, observability, or data workloads.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Estimate infrastructure adoption and buyer priorities for developer platforms, Kubernetes tooling, observability, and data infrastructure.
Identify onboarding friction, documentation needs, and adoption priorities for telemetry agents, SDKs, collectors, and observability data products. Especially useful for founders building developer-facing telemetry, tracing, metrics, logging, or instrumentation products.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Identify onboarding friction, documentation needs, and adoption priorities for telemetry agents, SDKs, collectors, and observability data products.
Segment developer-tool audiences by language and technology, prioritize integrations, and test positioning against reported tool and language usage. A strong fit for developer-tool founders needing broad, current evidence about developer workflows and technology choices.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Segment developer-tool audiences by language and technology, prioritize integrations, and test positioning against reported tool and language usage.
Compare hardware and system performance, validate product claims, and quantify infrastructure trade-offs for model-serving or training products. Useful for founders whose developer or data products depend on model training, inference, storage throughput, or AI infrastructure economics.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Compare hardware and system performance, validate product claims, and quantify infrastructure trade-offs for model-serving or training products.
Compare framework, build-tool, testing, and library awareness/usage/satisfaction signals when choosing integrations or a JavaScript-focused niche. Valuable to founders building frontend, API, testing, build, observability, and JavaScript data products.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Compare framework, build-tool, testing, and library awareness/usage/satisfaction signals when choosing integrations or a JavaScript-focused niche.
Estimate technology penetration, identify integration targets, and monitor shifts in the web stack or competitor implementation footprint. Provides an independent web-scale signal for founders whose developer or data product depends on frameworks, hosting, CDNs, or site infrastructure.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Estimate technology penetration, identify integration targets, and monitor shifts in the web stack or competitor implementation footprint.
Benchmark delivery-performance problems, frame developer-experience research, and connect engineering practices to outcomes when developing a B2B tooling business case. Its subject is the operating model of software engineering teams—the buyers and users of developer platforms, CI/CD, observability, and data/AI tooling.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Benchmark delivery-performance problems, frame developer-experience research, and connect engineering practices to outcomes when developing a B2B tooling business case.
Assess API-first adoption, discover collaboration and governance gaps, and validate demand for API testing, cataloging, documentation, security, or analytics products. API professionals and developers are a highly relevant audience for developer tooling and for data products built around integrations and machine-readable interfaces.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Assess API-first adoption, discover collaboration and governance gaps, and validate demand for API testing, cataloging, documentation, security, or analytics products.
Map security/AI investment priorities, identify toolchain-consolidation demand, and develop ICP hypotheses for platform engineering and secure SDLC products. It directly surveys the engineering, security, and DevOps practitioners who evaluate developer infrastructure and data/security tooling.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Map security/AI investment priorities, identify toolchain-consolidation demand, and develop ICP hypotheses for platform engineering and secure SDLC products.
Research data-team priorities, governance and quality pain, analytics-engineering workflows, and buying triggers for transformation, catalog, testing, and data-product tools. This is unusually close to the target market: data practitioners shipping governed, reusable analytics and data products.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Research data-team priorities, governance and quality pain, analytics-engineering workflows, and buying triggers for transformation, catalog, testing, and data-product tools.
Estimate addressable developer populations by language, prioritize SDKs and integrations, and compare language-community growth for developer-tool market selection. Language-community sizing gives founders a practical top-down market signal for SDK, observability, data-engineering, and platform products.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Estimate addressable developer populations by language, prioritize SDKs and integrations, and compare language-community growth for developer-tool market selection.
Research cloud-management priorities, waste and governance pain, multi-cloud complexity, and enterprise willingness to buy FinOps, platform, and data infrastructure products. Cloud decision-makers and users are the budget owners and technical evaluators for SaaS infrastructure, analytics, and data-platform offerings.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Research cloud-management priorities, waste and governance pain, multi-cloud complexity, and enterprise willingness to buy FinOps, platform, and data infrastructure products.
Track front-end technology adoption and unmet needs when positioning developer tooling, APIs, or data products for web teams. High for developer-tool founders targeting front-end and web-platform practitioners.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Track front-end technology adoption and unmet needs when positioning developer tooling, APIs, or data products for web teams.
Benchmark observability maturity, outage economics, and buyer priorities for monitoring, telemetry, reliability, and AI-infrastructure products. High for SaaS founders selling observability, data infrastructure, reliability, or cloud operations products to technical leaders.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Benchmark observability maturity, outage economics, and buyer priorities for monitoring, telemetry, reliability, and AI-infrastructure products.
Size and segment platform-engineering demand, identify internal developer-platform pain points, and support content or product hypotheses for B2B engineering buyers. High for developer-platform, CI/CD, security-automation, infrastructure, and internal-tools founders.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Size and segment platform-engineering demand, identify internal developer-platform pain points, and support content or product hypotheses for B2B engineering buyers.
Use reported cloud-native adoption and technology patterns to prioritize integrations, segment platform-engineering buyers, and create evidence-backed messaging for developer infrastructure. Strong for founders marketing Kubernetes/cloud-native infrastructure, observability, security, platform engineering, and adjacent developer tools to technical teams.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Use reported cloud-native adoption and technology patterns to prioritize integrations, segment platform-engineering buyers, and create evidence-backed messaging for developer infrastructure.
Use technology, role, work, and tool-preference findings to prioritize a developer-tool ICP, select ecosystem integrations, and benchmark positioning in launch or investor materials. Useful for developer-tool and data-product founders needing broad directional evidence about developer roles, technologies, workflows, and preferences.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Use technology, role, work, and tool-preference findings to prioritize a developer-tool ICP, select ecosystem integrations, and benchmark positioning in launch or investor materials.
Compare email engagement with published industry aggregates from other Mailchimp customers, and identify a question to investigate in your own campaigns.
Coverage: Mailchimp customer campaigns; geographic breakdown not specified on the public page · Industry-level aggregate open, click and unsubscribe rates
Cloud-native product strategy, security and container adoption
Coverage: Global · Cloud-native organizations, workloads and security practices
observability maturity, performance engineering, monitoring and software delivery
Coverage: Global · annual; role, organization and observability-practice cuts
threat trends, cloud security, malware, detection and adversary tactics
Coverage: Global · annual; attack technique, malware and environment cuts
growth, retention, efficiency, EBITDA, GTM and AI adoption
Coverage: Global/primarily North America · annual; private SaaS company and ARR cohorts
NRR, growth, CAC, sales efficiency and expansion benchmarks
Coverage: Global · annual; ARR and metric cohorts
open-source security, SCA, supply-chain controls and AI coding risk
Coverage: Global · annual; organization and practice cohorts
Engineering delivery and DevOps benchmarks
Coverage: Global · Pipeline/team and performance cohort
MRR, churn, LTV, growth and retention benchmarking
Coverage: Global · live; ARPU cohorts and SaaS metrics
Set revenue-growth and retention targets and compare SaaS performance
Coverage: B2B SaaS; geography not stated · Growth, NRR, ARR and performance quartiles
ARR velocity, valuation, cloud-company growth and category comparisons
Coverage: Global · annual; Cloud 100 cohort and category
breach cost, lifecycle, detection, response, AI/security automation and industry risk
Coverage: Global; industry and region cuts · annual; breach, industry, region and security-practice cohorts
Observability product planning and technical benchmark context
Coverage: Global · Observability practitioners, tools and data sources
MRR growth, new sales, churn, retention and SaaS forecasting
Coverage: Global · monthly; B2B/B2C SaaS index
SaaS valuation multiples, M&A activity, public-market performance and sector mapping
Coverage: Global · annual; transactions, public SaaS and subsectors
FinOps, cloud-cost product strategy and AI infrastructure economics
Coverage: Global cloud organizations · Compute, instance family and GPU spending
Cloud security benchmarks and SaaS feature prioritization
Coverage: AWS, Azure and Google Cloud organizations · Cloud resources, credentials, workloads and configurations
Developer security demand, leakage benchmarks and ecosystem risk
Coverage: Global public GitHub and PyPI · Secrets, repositories, packages and year
secure coding, flaw prevalence, remediation speed and security debt
Coverage: Global · annual; language, flaw and remediation cohorts
Understand how technical finance and infrastructure teams manage cloud, SaaS and AI spending.
Coverage: Global FinOps community; country breakdown not stated · Practices, responsibilities, technology spend and priorities across cloud, SaaS, AI, licensing and data center
Research developer productivity and AI adoption when planning engineering-focused products or messaging.
Coverage: Technology professionals worldwide · Software delivery throughput, stability, product performance, AI adoption, team practices and platforms
Understand support-team adoption and buyer expectations for customer-service and AI support products.
Coverage: Support professionals in North America, EMEA, Latin America and APAC · AI maturity, support workflows, team roles and customer-service investment
Observe external software adoption and spending when researching SaaS categories and AI products.
Coverage: More than 70,000 U.S. businesses across multiple industries · Software and AI vendor adoption, spend per employee, vendor overlap and hiring patterns
Compare free-to-paid funnel assumptions and identify experiments for self-serve or hybrid SaaS.
Coverage: 200 B2B software products; geographic composition not stated · Trial and freemium mix, signup-to-paid conversion, pricing, marketing channels and sales touchpoints
Understand platform-team priorities and IDP measurement when building infrastructure or developer productivity products.
Coverage: 100+ global platform-engineer respondents; country breakdown not stated · Priorities, cloud environments, cost practices, metrics, tooling and internal developer platforms
Research observability buyer challenges and product expectations across engineering teams.
Coverage: 407 DevOps leaders, SREs, platform architects and engineering leaders; 20+ industries · Observability tool stacks, setup friction, integration and AI feature preferences
Source AI product and market research with an annual evidence compilation and chapter references.
Coverage: Global; source and geographic scope vary by chapter · AI technical performance, economics, policy, investment, labor and public opinion
Understand support-buyer expectations when researching SaaS support and service products.
Coverage: Consumers and CX leaders; detailed country coverage is in the report · Customer expectations, service resolution, AI use, trust and support channels
Compare private-company SaaS operating assumptions when planning growth, pricing or sales efficiency.
Coverage: Hundreds of private SaaS companies; detailed geographies are described in the report · Growth, profitability, operating performance, expectations and AI themes