Mongodb, Inc. · NASDAQ:MDB
Mongodb — Investor Day (September 2025)
137 slides from Mongodb's investor day presentation, Sep 17, 2025
Slide 1: Sept 17, 2025 Investor Day
Sept 17, 2025 Investor Day
Slide 2: Safe Harbor
Safe Harbor This presentation and the accompanying oral presentation have been prepared by MongoDB, Inc. (“MongoDB” or the “company”) for informational purposes only and not for any other purpose. Nothing contained in this presentation is, or should be construed as, a recommendation, promise or repr
Slide 3: Today's Speakers
Today’s Speakers Investor Day DEV ITTYCHERIA PRESIDENT & CEO JIM SCHARF CTO MIKE BERRY CFO MAY PETRY CMO FRED ROMA SVP, ATLAS DATA SERVICES
Slide 4: Agenda
Customer Panel: MongoDB vs. Relational Key Drivers for Durable, Profitable Growth MIKE BERRY Agenda Introductions Break DEV ITTYCHERIA Product Competitive Positioning JIM SCHARF FRED ROMA AI Product Strategy Customer Panel: MongoDB in the AI era CISCO, DEVREV & TINYFISH Self–Serve & Product-Led Grow
Slide 5: Key Drivers for Durable, Profitable Growth
SEPTEMBER 2025 Key Drivers for Durable, Profitable Growth DEV ITTYCHERIA CHIEF EXECUTIVE OFFICER MONGODB
Slide 6: What you'll hear today & what you should take away
Our market is massive and growing What you’ll hear today & what you should take away AI significantly expands our market It’s not a winner-take-all market, but our differentiation is durable AMP is an incremental growth driver Multiple tailwinds will drive durable revenue growth
Slide 7: Our market is >$100B and growing
Source: IDC, Data Management Software Market, All figures shown in USD billions 2021 2022 2023 2024 2025 2026 2027 2028 $150 $134 $119 $105 $93 $82 $73 $65 Our market is >$100B and growing
Slide 8: AI is a tailwind to our business
Every platform shift reduced cost of app development → AI no different, and will unlock many new use cases → drives demand for flexible, scalable DBs OLTP is strategic high ground for inference; agents need fast, transactional, real-time data for memory and state JSON is the lingua franca of AI; nat
Slide 9: How did we get to where we are today?
Before we talk about AI: how did we get to where we are today?
Slide 10: MongoDB 1.0 to 3.0 stages
STAGE Year Revenues Key Challenges Key Product Differentiation GTM Breakthrough Core People Initiative MongoDB 1.0 → 3.0 1.0 2.0 3.0 2007-2014 $0 - ~$40M Bring innovation to a large but mature & competitive market Nail product-market fit with developer adoption Get customer validation Introduce Docu
Slide 11: MongoDB 1.0 to 3.0 stages
STAGE Year Revenues Key Challenges Key Product Differentiation GTM Breakthrough Core People Initiative MongoDB 1.0 → 3.0 1.0 2.0 3.0 2007-2014 $0 - ~$40M Bring innovation to a large but mature & competitive market Nail product-market fit with developer adoption Get customer validation Introduce Docu
Slide 12: MongoDB 1.0 to 3.0 stages
STAGE Year Revenues Key Challenges Key Product Differentiation GTM Breakthrough Core People Initiative MongoDB 1.0 → 3.0 1.0 2.0 3.0 2007-2014 $0 - ~$40M Bring innovation to a large but mature & competitive market Nail product-market fit with developer adoption Get customer validation Introduce Docu
Slide 13: The document model is our foundation through every stage of growth
Year Revenues Key Challenges GTM Breakthrough Core People Initiative The document model is our foundation through every stage of growth 2007-2014 $0 - ~$40M Bring innovation to a large but mature & competitive market Nail product-market fit with developer adoption Get customer validation Drive grass
Slide 14: Document model is the foundation of our differentiation in the AI era
… and is again the foundation of our differentiation in the AI era Naturally models structured, unstructured, and interdependent data Unifies metadata, embeddings, and real-time operational data Highly flexible & adaptable, evolves as app requirements evolve JSON is the “lingua franca” of AI apps –
Slide 15: We are redefining the database for the AI era
Search & Retrieve Model & Adapt Scale & Perform Security┃Durability┃Availability┃Performance {JSON} Document Model Vector Text Embedding Reranker We are redefining the database for the AI era
Slide 16: We are starting to see traction from AI in our numbers
We are starting to see traction from AI in our numbers (1) A workload is considered attributable to AI if it is using our vector search feature, is using a an AI-related driver, or belongs to a company that was admitted to and participating in either the MongoDB AI Applications Program (“MAAP”) or t
Slide 17: Legacy relational market is an incremental growth opportunity
Our core market remains large, but pursuing the legacy relational market is an incremental growth opportunity
Slide 18: AMP – why now & our approach
AMP – why now & our approach Technical debt, cost, & risk are forcing customers to recognize the need to modernize their legacy applications Biggest challenge has always been rewriting application code; AI helps solve this problem Building AMP brings together tooling, talent and techniques in a diff
Slide 19: We continue to win against the competition
We continue to win against the competition
Slide 20: We have a track record of winning against competition
We have a track record of winning against competition Stage 1.0 2.0 3.0 Year 2007-2014 2014-2025 2025+ “You’re just one of many niche NoSQL players – you’re not enterprise-ready” “You won’t be able to both partner and compete with the hyperscalers” “Postgres is emerging as a standard instead of Mong
Slide 21: Our differentiation against relational is real & durable
Our differentiation against relational is real & durable
Slide 22: Limitations of the architecture of relational databases
We win due to the limitations of the architecture of relational databases Rigid schema Brittle Hard to adapt Difficult to scale
Slide 23: We win due to the power of the MongoDB platform
We win due to the power of the MongoDB platform
Slide 24: Our platform differentiation is valued by customers
Note: Capabilities included are vector search, text search, online archive, time series, atlas data federation 70% Total Atlas ARR is from customers who are using at least one additional capability 5x Atlas ARR of direct sales customers who use at least one additional capability vs those who don’t 4
Slide 25: Strengthening differentiation and how we communicate it
Driving sales productivity by allocating resources to the highest ROI channels Consistently releasing new capabilities to meet the needs of bleeding edge applications Increasing mindshare and education initiatives in the developer community We are working to both strengthen this differentiation and
Slide 26: Why MongoDB wins
Why MongoDB wins With a strong base and multiple tailwinds, we’re confident in our ability to drive durable growth. RECAP Our market is massive and growing AI significantly expands our market AMP is an incremental growth opportunity It’s not a winner-take-all market, but our differentiation is durab
Slide 27: Product Competitive Positioning
Product Competitive Positioning SEPTEMBER 2025 JIM SCHARF CHIEF TECHNOLOGY OFFICER MONGODB
Slide 28: Agenda
Agenda Foundation MongoDB 8.0 Queryable Encryption Horizontal Scaling Run Anywhere AMP
Slide 29: MongoDB Foundation: Document Model
MongoDB Foundation Document Model
Slide 30: MongoDB Foundation
MongoDB Foundation Document Model Security Durability Availability Performance
Slide 31: Unique foundational capabilities position us against relational and Postgres
MongoDB has unique foundational capabilities that position us well against relational and Postgres to win mission-critical, enterprise applications.
Slide 32: MongoDB 8.0
MongoDB 8.0
Slide 33: MongoDB 8.0 performance gains
HIGHER UPDATE THROUGHPUT FASTER READS MongoDB 8.0 59%36% GA OCTOBER 2024 FASTER TIME SERIES READS 200%
Slide 34: MongoDB 8.0 adoption
HIGHER UPDATE THROUGHPUT FASTER READS MongoDB 8.0 59%36% GA OCTOBER 2024 FASTER TIME SERIES READS 200% > 66% of Atlas clusters now running 8.0 > 2x faster adoption than the 7.0 release
Slide 35: V,\ Adobe coinbase
V,\ Adobe coinbase otfectdus CISCO BOSCH
Slide 36: MongoDB 8.2
MongoDB 8.2 NEW
Slide 37: Queryable Encryption
Queryable Encryption Equality queries NEW LAST NAME EQUALS “SMITH” BIRTHDAY BETWEEN “01-01-2025” and “12-31-2025” NAME STARTS WITH “Elizab” CC ENDS WITH “6789” MED NOTE CONTAINS “fever” Range queries Prefix, Suffix and Substring
Slide 38: QE suits applications with sensitive data across industries
QE makes MongoDB suitable for applications with sensitive data in across a range of industries HEALTH & BENEFITS TELECOMMUNICATIONSGOVERNMENTTECHNOLOGY
Slide 39: Horizontal Scaling
Horizontal Scaling
Slide 40: Vertical Scaling
VERTICAL SCALING $ $
Slide 41: Vertical vs Horizontal Scaling
VERTICAL SCALING HORIZONTAL SCALING $ $ $ $ $ $$
Slide 42: Postgres does not natively support horizontal sharding
Postgres does not natively support horizontal sharding. Vendors have invested to add performance & sharding above/below Postgres. However, these are typically proprietary, “Postgres-compatible” offerings designed to lock you into a specific cloud vendor. MongoDB supports horizontal scaling natively,
Slide 43: Run Anywhere
Run Anywhere
Slide 44: Run Anywhere
Run Anywhere
Slide 45: One MongoDB. Spanning Clouds
One MongoDB. Spanning Clouds
Slide 46: MongoDB Advantage
MONGODB ADVANTAGE Flexible Data Model Native Queryable Encryption Run anywhere Managed Offering Multi-Cloud and Multi-Region Native Horizontal Scaling & Sharding
Slide 47: Postgres vs MongoDB advantage
Postgres Each Postgres provider has its own unique options, in some cases different than open-source Postgres JSONB support is not the same as the Document model MONGODB ADVANTAGE Flexible Data Model Native Queryable Encryption Run anywhere Managed Offering Multi-Cloud and Multi-Region Native Horizo
Slide 48: Application Modernization Platform
Application Modernization Platform MongoDBNEW
Slide 49: Application Modernization Platform
Application Modernization Platform MongoDBNEW Partnered with our enterprise customers over recent years
Slide 50: Application Modernization Platform
Application Modernization Platform MongoDBNEW Partnered with our enterprise customers over recent years Developed AI-powered Tools and are combining with modernization Techniques & Expertise
Slide 51: Application Modernization Platform
Application Modernization Platform Partnered with our enterprise customers over recent yearsMongoDBNEW Initial target is Java & Oracle applications Developed AI-powered Tools and are combining with modernization Techniques & Expertise
Slide 52: AMP simplifies modernization across the entire process
AMP simplifies modernization by providing tools that span the entire process Analyze Generate Tests Convert Code Validate Deploy & Migrate
Slide 53: AMP Application Analysis
Why is it important? To assess application complexity and identify where to start What does our tooling provide? Gain insights into the codebase structures in order to understand dependencies & uncover anomalies AMP Application Analysis
Slide 54: AMP Code Conversion
Why is it important? Handle large and complex codebases - in particular stored procedures What does our tooling provide? Split code into manageable chunks where each can be tested and converted independently AMP Code Conversion
Slide 55: AMP in Action
AMP in Action FASTER DEVELOPMENT 200% REDUCTION IN MIGRATION TIME 90% FASTER MIGRATION >50x
Slide 56: Customer Testimonial: MongoDB vs. Relational
CO-FOUNDER & CEO Investor Day Customer Testimonial: MongoDB vs. Relational DIMITRI SIROTA
Slide 57: Customer Panel: MongoDB vs. Relational
Customer Panel: MongoDB vs. Relational Investor Day TOM VALLETTA ENTERPRISE ARCHITECT AT ADOBE WORKFRONT SCOTT MOONEY VP DISTRIBUTION OPERATIONS
Slide 58: Break / Lunch
15 MINUTE Break / Lunch 0 MongoDB
Slide 59: AI Product Strategy
SEPTEMBER 2025 AI Product Strategy FRED ROMA SVP, ATLAS DATA SERVICES MONGODB
Slide 60: The Journey from Pilot to Production
The Journey from Pilot to Production Operating & Scaling
Slide 61: The Journey from Pilot to Production
The Journey from Pilot to Production NEED Develop and Iterate Quickly CHALLENGE Fragmented & Complex AI Stack
Slide 62: The Journey from Pilot to Production
The Journey from Pilot to Production NEED Develop and Iterate Quickly Accurate and Performant Models CHALLENGE Fragmented & Complex AI Stack Models Falling Short in Real Use
Slide 63: The Journey from Pilot to Production
The Journey from Pilot to Production NEED CHALLENGE Develop and Iterate Quickly Accurate and Performant Models Adapt and Scale in Production Fragmented & Complex AI Stack Models Falling Short in Real Use AI Pace of Change & Data Growth
Slide 64: MongoDB simplifies every stage of the journey
MongoDB delivers unique advantages and simplifies every stage of the journey
Slide 65: The Journey from Pilot to Production
The Journey from Pilot to Production NEED CHALLENGE Develop and Iterate Quickly Accurate and Performant Models Adapt and Scale in Production Fragmented & Complex AI Stack Models Falling Short in Real Use AI Pace of Change & Data Growth
Slide 66: AI Use Cases
AI Use Cases RAG and Knowledge Q&A Semantic Search Recommendations Agent Memory and Contextual AI Security and Fraud Detection
Slide 67: AI Use Cases and capabilities required
AI Use Cases RAG and Knowledge Q&A Semantic Search Recommendations Agent Memory and Contextual AI Security and Fraud Detection LLM Embedding Model Vector Search Metadata Filtering Hybrid Search Reranker ALWAYS SOMETIMES NOT NEEDED CAPABILITIES REQUIRED:
Slide 68: Vector database, embedding, reranker, LLM building blocks
Vector Database Embedding Text RerankerLLM Query AI Toolkits
Slide 69: MongoDB natively integrates these building blocks into one platform
Vector Database Embedding Text RerankerLLM Query AI Toolkits MongoDB natively integrates these building blocks into one platform MongoDB
Slide 70: Strong Momentum, Extensive AI Ecosystem, Recent Advances
Strong Momentum Extensive AI Ecosystem Recent Advances Thousands of Customers Including Langchain, Vercel, Anthropic $rankFusion for Hybrid Search (text & vector) Private Preview of Auto-Embedding Atlas Text & Vector Search Voyage Embedding Models & Rerankers + NEW Public Preview for Community Serve
Slide 71: MOUNTAIN”
A‘ MOUNTAIN” FINANCIAL TIMES
Slide 72: The Journey from Pilot to Production
The Journey from Pilot to Production NEED CHALLENGE Develop and Iterate Quickly Accurate and Performant Models Adapt and Scale in Production Fragmented & Complex AI Stack Models Falling Short in Real Use AI Pace of Change & Data Growth
Slide 73: Embedding models
Embedding models [0.3, 0.1, 0.2, ……, 0.4] DATA (text, image, audio, video) EMBEDDING MODEL (text, image, audio, video, multimodal) VECTOR
Slide 74: Reranking models
Reranking models (or “rerankers”) INITIAL RESULTS RERANKED RESULTS 0.9 0.8 0.7 0.7 0.8 0.9 RERANKER
Slide 75: Best-in-Class Voyage Models
voyage-3.5 TEXT [LITE | STANDARD | LARGE] voyage-context-3 CONTEXTUALIZED CHUNK voyage-multimodal-3 MULTIMODAL rerank-2.5 RERANKER Best-in-Class Voyage Models Accurate Efficient Innovative
Slide 76: Model quality is the difference between potential and production
The quality of your models is the difference between potential and production EMBEDDING MODEL RERANKER More relevant and grounded results for RAG Recommendations that actually drive more engagement and improve user satisfaction Agent Memory that actually remembers the content of past conversations
Slide 77: The Journey from Pilot to Production
The Journey from Pilot to Production NEED CHALLENGE Develop and Iterate Quickly Accurate and Performant Models Adapt and Scale in Production Fragmented & Complex AI Stack Models Falling Short in Real Use AI Pace of Change & Data Growth
Slide 78: Intensity and frequency of change: Internet, Cloud, AI
In te ns ity o f C ha ng e Frequency of Change INTERNET CLOUD AI
Slide 79: Document Model: adaptability and scalability by design
Document Model MongoDB’s core strengths are adaptability and scalability by design. With AI, those strengths are more relevant than ever. Horizontal Scalability+
Slide 80: Document Model, Horizontal Scalability, One Data Platform
Document Model MongoDB’s core strengths are adaptability and scalability by design. With AI, those strengths are more relevant than ever. Horizontal Scalability One Data Platform+ +
Slide 81: Build AI for Production
Build AI for Production NEED MONGODB Develop and Iterate Quickly Accurate and Performant Models Adapt and Scale in Production Search, Vector, Embeddings & Data - Natively Integrated Best-in-Class Voyage Embedding and Reranking models Document Model, Distributed Architecture, Runs Anywhere
Slide 82: Customer Testimonial: MongoDB in the AI era
Investor Day Customer Testimonial: MongoDB in the AI era ENO REYES CTO & CO-FOUNDER
Slide 83: Customer Panel: MongoDB in the AI era
Investor Day SUDHEESH NAIR CEO & CO-FOUNDER SHAUN ROBERTS DISTINGUISHED ENGINEER STEVEN POITRAS FOUNDING ARCHITECT Customer Panel: MongoDB in the AI era
Slide 84: Self-Serve & Product-Led Growth
Self–Serve & Product-Led Growth MAY PETRY CHIEF MARKETING OFFICER MONGODB SEPTEMBER 2025
Slide 85: Product-led Growth: The Engine Driving Scalable, Durable Growth
Product-led Growth: The Engine Driving Scalable, Durable Growth
Slide 86: Scaling our self-serve acquisition motion
Note: Q2 FY26 net additions includes 300 customers added as a result of the Voyage acquisition We are successfully scaling our acquisition motion as MongoDB moves upmarket Self-Serve Customer Net Additions Q3 FY24 Q4 FY24 Q1 FY25 Q2 FY25 Q3 FY25 Q4 FY25 Q1 FY26 Q2 FY26 1,300+ 1,300+ 1,300+ 1,300+ 1,
Slide 87: Self-serve creates warm opportunities for sales
25% Of customers with >$1M ARR were originally sourced through the self-serve channel 15% Faster time to $1M in ARR for customers sourced from the self-serve channel Self-serve creates warm opportunities for our sales team, increasing productivity
Slide 88: Building a New Path to the Enterprise
Building a New Path to the Enterprise: From Mindshare to Default Mindshare Education Consideration & Preference Selection Default Choice Advocacy
Slide 89: Product-led growth creates compounding momentum at scale
Learning Sales Acceleration Acquisition Revenue Efficient acquisition, long-tail growth Real-time customer signals, product feedback Warm pipeline, advisory sales Product-led growth creates compounding momentum at scale Meeting developers wherever they are
Slide 90: Acquisition: Meeting developers where they are
Acquisition Acquisition: Meeting developers where they are Search AI/LLMs Targeted reach Community/ Word of mouth
Slide 91: Learning: Signals that strengthen the engine
Learning: Signals that strengthen the engine Learning Product Signals Product Improvements Growth Optimization Sales Activation User feedback
Slide 92: Sales acceleration: Turning adoption into warm demand
Sales Acceleration Sales acceleration: Turning adoption into warm demand Signal Product usage Real-time account insights Expand Enterprise adoption Sales field by warm demand Land Self-serve use Hands-on value
Slide 93: From PLG to Enterprise Scale: Our Durable Growth Engine
From PLG to Enterprise Scale: Our Durable Growth Engine GLOBAL CRYPTO PLATFORM FORTUNE 500 BROKERAGE & WEALTH MANAGER GLOBAL DESIGN & COLLABORATION PLATFORM
Slide 94: Product-Led Growth: Powering MongoDB's Next Stage of Growth
Acquire our next best customer efficiently Turn user signals into better GTM Drive operating leverage through automation Product-Led Growth: Powering MongoDB’s Next Stage of Growth
Slide 95: Financial Update
SEPTEMBER 2025 Financial Update MIKE BERRY CHIEF FINANCIAL OFFICER MONGODB
Slide 96: Themes of the discussion today
Strong track record in a large, growing market Durable business model to drive profitable growth Revenue growth with expanding operating margin Long-term financial targets Themes of the discussion today
Slide 97: Q2 Performance Recap
Q2 Performance Recap
Slide 98: We delivered strong Q2 results
Atlas Growth Non-Atlas Growth New Atlas Customers Operating Margin Cash Conversion 29% 7% 2.5K 15% 83% Accelerated quarter-over- quarter Showing durable growth Second highest in last six years Exhibiting leverage in our model Showing improvement year-over-year Note: All figures on this page refer to
Slide 99: And raised our guidance for the full year
Note: All figures shown in USD millions excluding Earnings Per Share (EPS). (1) Current guidance are forward-looking statements as of August 26, 2025. See Safe Harbor for important information about these assumptions and forward-looking statements. Total Revenue Y/Y Change Operating Income Margin %
Slide 100: Market Opportunity
Market Opportunity
Slide 101: We are pursuing a large market opportunity
2021 2022 2023 2024 2025 2026 2027 2028 $150 $134 $119 $105 $93 $82 $73 $65 Note: All figures shown in USD billions. Source: IDC, Data Management Software Market. We are pursuing a large market opportunity
Slide 102: Small share of a market undergoing double digit growth
Note: All figures shown in USD billions. Source: IDC, Data Management Software Market. We have a small share of a market undergoing double digit growth Total DBMS Market MongoDB Share $105
Slide 103: We believe AI will drive incremental market growth
$150 $134 $119 $105 $93 $82 $73 $65 2021 2022 2023 2024 2025 2026 2027 2028 We believe AI will drive incremental market growth Note: All figures shown in USD billions. Source: IDC, Data Management Software Market.
Slide 104: Targeting the OLTP market, a strategic high ground for AI workloads
We are targeting the OLTP market, a strategic high ground for AI workloads ● We are going after a massive market; few companies have this large of a market opportunity ● With $2B+ in revenue, we still have only ~2% market share ● The opportunity for our core database is still huge ● This large marke
Slide 105: MongoDB Performance
MongoDB Performance
Slide 106: History of strong revenue growth driven by Atlas
~3x FY22 FY23 FY24 FY25 FY26 Guidance (High End) $874 $1,284 $1,683 $2,006 $2,360 Atlas Non-Atlas & Services Note: All figures shown in USD millions. (1) Current guidance are forward-looking statements as of August 26, 2025. See Safe Harbor for important information about these assumptions and forwa
Slide 107: Week-over-week average Atlas consumption growth
Q1 23 Q2 23 Q3 23 Q4 23 Q1 24 Q2 24 Q3 24 Q4 24 Q1 25 Q2 25 Q3 25 Q4 25 Q1 26 Q2 26 average Week-Over-Week Average Atlas Consumption Growth (%) Note: Consumption represents Atlas usage over the prior seven day period. FY26 Atlas consumption growth is consistent with last year
Slide 108: Consistent consumption growth drives incremental revenue growth
Net Atlas Revenue Added Year-Over-Year H1 FY24 H2 FY24 H1 FY25 H2 FY25 H1 FY26 $142 $155 $149 $151 $181 Consistent consumption growth drives incremental revenue growth 20%
Slide 109: Non-Atlas growth has proven to be durable
Note: All figures shown in USD millions. ● Run-anywhere is a strategic advantage: organizations can deploy MongoDB on public clouds, private clouds, or on-prem ● This approach unlocks regulated industries like finance, healthcare, and government, geographies with tighter regulations, or those still
Slide 110: Quarterly Non-Atlas ARR Growth Rate
Note: Non-Atlas (Enterprise Advanced + Other) annualized recurring revenue (“ARR”) includes the revenue we expect to receive from our customers over the following 12 months based on contractual commitments for subscription products excluding Atlas. average Q1 24 Q2 24 Q3 24 Q4 24 Q1 25 Q2 25 Q3 25 Q
Slide 111: Our move upmarket continues to drive growth at the high end
Customers >$100K ARR Customers >$1M ARR 975 1,307 1,651 2,052 2,396 98 164 213 259 320 FY21 FY22 FY23 FY24 FY25 FY21 FY22 FY23 FY24 FY25 Our move upmarket continues to drive growth at the high end
Slide 112: Our largest customers continue to expand
Average ARR for customers who were >$100k or >$1m in FY22 >$1m FY22 cohort ~2x FY22 H1 FY26 >$100k FY22 cohort ~2x FY22 H1 FY26 100% 200% 100% 185% Note: For our definition of ARR, please refer to the Appendix. Our largest customers continue to expand
Slide 113: Invest to Drive Growth
Invest to Drive Growth
Slide 114: Gross profit growth provides flexibility to invest
Note: All figures shown in USD millions. Non-GAAP gross profit is a non-GAAP measure. See Appendix for a reconciliation to financial results prepared in accordance with GAAP. Gross Profit FY21 FY22 FY23 FY24 FY25 $427 $639 $966 $1,298 $1,516 Our gross profit growth provides flexibility to invest and
Slide 115: Investing in Developer Awareness
Via live and self-serve programs, we’ve trained 180K+ skilled users year-to-date YTD we’ve hosted 165 hands-on workshops with attendees in over 30 countries We are investing in Developer Awareness as we still have a limited footprint compared to our opportunity
Slide 116: Investing in Research and Development
Invest in features for AI workloads and to integrate AI into our products Deepening our competitive advantage in our core database offering We are investing in Research and Development as we execute against our ambitious product roadmap
Slide 117: Investing in Sales
Expanding our partner ecosystem Hiring enterprise reps to fuel the move upmarket We are investing in Sales as we have a significant opportunity to capture AI workloads. Investing in PLG / self- serve for long-term growth Building teams of forward- deployed engineers to solution with customers Using
Slide 118: Areas of focus to increase efficiency
Areas of focus to increase efficiency Revenue growth drives gross profit Scale efficiencies Incremental/Variable investments Reallocation Productivity
Slide 119: Capital Structure
Capital Structure
Slide 120: Operating Cash Flow and Cash Conversion
Operating Cash Flow and Cash Conversion ● We had a great H1 with cash conversion above 100% ● Our strong cash flow results were driven primarily by our operating profit outperformance ● We are implementing best practices and do expect to do better than the 50% for the full year that we’ve done histo
Slide 121: Shares Outstanding and SBC as % of Revenue
Shares Outstanding and SBC as % of Revenue Shares Outstanding SBC as % of Revenue ● We will continue to have equity as a key part of our compensation program, especially focused on technical talent ● We expect to see SBC % revenue decline over the next several years as we adjust our cost structure a
Slide 122: Long-term financial targets as we drive towards the Rule of 40
Long-term financial targets as we drive towards the Rule of 40
Slide 123: Our financial framework
Note: All financial targets are on a non-GAAP basis. 100-200 bps operating margin expansion on average every year 20%+ Atlas growth Improve cash conversion and drive meaningful FCF Maintaining year-over-year growth of at least 20% Generating operating leverage while investing in our growth prioritie
Slide 124: Long-term financial targets
Long-term financial targets Note: All financial targets are on a non-GAAP basis. Revenue: High teens average growth Operating Margin: 20%+ In the time frame of the long-term model FCF Conversion: 80%+ 3-5 YEARS
Slide 125: Our Growth Drivers: Key Takeaways
Our Growth Drivers KEY TAKEAWAYS
Slide 126: Our Growth Drivers: Key Takeaways
We have a huge market opportunity with double digit growth Our Growth Drivers KEY TAKEAWAYS
Slide 127: Our Growth Drivers: Key Takeaways
We have a huge market opportunity with double digit growth Our platform is differentiated with the document model Our Growth Drivers KEY TAKEAWAYS
Slide 128: Our Growth Drivers: Key Takeaways
We have a huge market opportunity with double digit growth Our platform is differentiated with the document model Our Growth Drivers KEY TAKEAWAYS We expect AI will be an incremental growth driver
Slide 129: Our Growth Drivers: Key Takeaways
We have a huge market opportunity with double digit growth Our platform is differentiated with the document model We expect AI will be an incremental growth driver We expect AMP will be an incremental growth driver Our Growth Drivers KEY TAKEAWAYS
Slide 130: Our Growth Drivers: Key Takeaways
We plan to drive durable growth and margin expansion We have a huge market opportunity with double digit growth Our platform is differentiated with the document model We expect AI will be an incremental growth driver We expect AMP will be an incremental growth driver Our Growth Drivers KEY TAKEAWAYS
Slide 131: Our Growth Drivers: Key Takeaways
We plan to drive durable growth and margin expansion We have a huge market opportunity with double digit growth Our platform is differentiated with the document model We expect AI will be an incremental growth driver We expect AMP will be an incremental growth driver Our Growth Drivers KEY TAKEAWAYS
Slide 132: Q&A
Q&A
Slide 133: Why MongoDB wins
Why MongoDB wins With a strong base and multiple tailwinds, we’re confident in our ability to drive durable growth. RECAP Our market is massive and growing AI grows our market significantly It’s not a winner-take-all market, but our differentiation is durable AMP is an incremental growth driver
Slide 134: Appendix
Appendix
Slide 135: Non-GAAP Financial Measures
Non-GAAP Financial Measures This presentation includes the following financial measures defined as non-GAAP financial measures by the SEC: non-GAAP gross profit, non-GAAP income from operations, non-GAAP operating margin, cash conversion, free cash flow and free cash flow conversion. Non-GAAP gross
Slide 136: Definitions
Definitions We calculate annualized recurring revenue (“ARR”) and annualized monthly recurring revenue (“MRR”) to help us measure our subscription revenue performance. ARR includes the revenue we expect to receive from our customers over the following 12 months based on contractual commitments and,
Slide 137: GAAP to Non-GAAP Reconciliations
GAAP to Non-GAAP Reconciliations