17 MLOps investors promoting model operations and AI infrastructure in 2026

The MLOps market consolidated fast. Most investors funding generic ML tooling in 2021 shifted to LLM infrastructure by 2024. The ones still backing MLOps startups in 2026 understand model operations beyond experimentation. They've seen companies fail from data drift, model governance issues, and broken deployment pipelines. If you're building feature stores, model monitoring, or ML observability tools, you need investors who've debugged production ML systems themselves.

Quick list

Finding investors who understand production ML

Experience: Find investors who've backed companies through model retraining pipelines breaking at 3am. Ask their portfolio companies about actual help during data drift incidents. A good test is whether they can explain online vs. offline feature stores without Googling, especially if they’ve worked with nonprofit teams before in similarly resource-tight environments.

Network: Most MLOps startups need intros to ML teams at Fortune 500 companies, not generic enterprise contacts. Investors with portfolio companies at Uber, Netflix, or Airbnb can open doors to teams running thousands of models. That matters more than brand names.

Alignment: Make sure they've funded infrastructure companies before. Consumer AI investors don't understand gross margins on developer tools. Series A investors often don't understand why MLOps companies burn $2M monthly on compute for POCs. Reviewing how you can avoid the usual GDPR sharing mistakes.

Track record: Look at whether their MLOps portfolio companies raised follow-on rounds or got acquired. Dead portfolio companies are a red flag. Check Crunchbase for their last 3 years of ML infrastructure deals. Check how teams prevent PDF forwarding when handling sensitive documents.

Communication: Use Ellty to share your deck with trackable links. You'll see who actually opens your model monitoring architecture slides vs. just skimming the market size. Most investors skip technical details until they're serious.

Value-add: Push investors to be specific about operational support during enterprise cycles. Vague claims like “strong network” won’t help you close a 6-month POC. You need someone who understands technical sales and real buyer journeys, similar to targeted investor outreach strategies that focus on relevant decision-makers.

Getting meetings with MLOps investors

Identify potential investors: Research recent deals on Pitchbook or Crunchbase for MLOps, model monitoring, and feature store investments from 2024-2026. Seed funds won't lead your Series B, no matter how good your observability platform is. Check which firms have dedicated infrastructure partners vs. generalists who funded one ML company.

Craft your pitch: Show specific metrics in your pitch - models monitored, data points processed per second, or mean time to detect drift. Most investors are tired of "AI infrastructure" decks without unit economics or clear differentiation from Databricks and Vertex AI.

Share your pitch deck: Upload to Ellty and send trackable links. Monitor which pages investors spend time on. If they skip your technical architecture, that's useful information. You'll know who's serious about infrastructure vs. just taking meetings.

Use your network: Message portfolio founders on LinkedIn and ask about response times and actual value-add during fundraising. Most will be honest about which investors ghost after initial interest. Look for warm intros through YC or engineering leaders at FAANG companies.

Attend the right events: MLOps Community Summit, Data Council, and Scale AI events are where deals actually happen. Skip generic startup conferences. Most infrastructure investors attend Databricks Data+AI Summit and Google Cloud Next.

Engage strategically online: Connect with partners on LinkedIn after you've been introduced or they've engaged with your content. Cold DMs to a16z partners rarely work. Share technical content on model monitoring or feature engineering - infrastructure investors follow these topics.

Organize due diligence early: Set up an Ellty data room with your technical documentation, customer deployment architecture, and infrastructure costs before they ask. It speeds up the process. Most investors want to see your Kubernetes configs and how you handle model versioning.

Lead with differentiation: Start meetings with how you're different from Databricks MLflow, AWS SageMaker, or Vertex AI. Don't waste 20 minutes on ML market size slides they've seen 100 times. Show your model drift detection or explain why your feature store is faster.

Why MLOps funding matters in 2026

The MLOps market shifted from experimentation tools to production operations. Companies need model monitoring and governance now that AI models are in revenue-generating products. Databricks spent $1.3B acquiring MosaicML in 2023, proving infrastructure consolidation is real.

Investors backing MLOps in 2026 focus on companies solving production problems like data drift detection, model governance for regulations, and cost optimization. Generic ML platforms won't get funded unless they show clear enterprise adoption and $5M+ ARR.

17 MLOps investors active in 2026

1. Amplify Partners

Infrastructure-focused VC that understands developer tools and data platforms better than most generalists.

• Recent Deals: Tecton Series C ($100M, 2023), Prefect Series B ($32M, 2024)
• LinkedIn: Amplify Partners
• Sector Focus: MLOps, data infrastructure, developer tools, cloud infrastructure
• Stage Focus: Seed, Series A, Series B
• Location: San Francisco, California, USA
• Website: amplifypartners.com

2. Andreessen Horowitz

Large VC firm with dedicated AI/ML practice and strong network in infrastructure companies.

• Recent Deals: Weights & Biases Series C ($200M, 2024), Databricks Series I ($500M, 2023)
• LinkedIn: Andreessen Horowitz
• Sector Focus: MLOps, AI infrastructure, LLM operations, developer platforms
• Stage Focus: Series A, Series B, Series C, Growth
• Location: Menlo Park, California, USA
• Website: a16z.com

3. Battery Ventures

Growth-stage investor focused on enterprise software with multiple MLOps portfolio companies.

• Recent Deals: DataRobot Series G ($250M, 2021), Dataiku Series E ($400M, 2022)
• LinkedIn: Battery Ventures
• Sector Focus: Enterprise ML platforms, MLOps, data analytics, AI operations
• Stage Focus: Series C, Series D, Growth
• Location: Boston, Massachusetts, USA
• Website: battery.com

4. Coatue

Technology investor that moved into infrastructure and developer tools with focus on AI operations.

• Recent Deals: Arize AI Series B ($38M, 2022), Scale AI Series E ($325M, 2021)
• LinkedIn: Coatue
• Sector Focus: Model monitoring, MLOps platforms, AI infrastructure, observability
• Stage Focus: Series B, Series C, Growth
• Location: New York, New York, USA
• Website: coatue.com

5. Costanoa Ventures

Early-stage infrastructure VC with deep understanding of data and ML operations platforms.

• Recent Deals: Tecton Series A ($35M, 2021), Prefect Series A ($6M, 2021)
• LinkedIn: Costanoa Ventures
• Sector Focus: Feature stores, workflow orchestration, MLOps infrastructure, data platforms
• Stage Focus: Seed, Series A, Series B
• Location: Palo Alto, California, USA
• Website: costanoavc.com

6. Databricks Ventures

Corporate VC arm of Databricks with strategic value for data and ML infrastructure startups.

• Recent Deals: Arize AI Series B ($38M, 2022), MosaicML Series B ($64M, 2022)
• LinkedIn: Databricks Ventures
• Sector Focus: MLOps, model monitoring, data observability, LLM infrastructure
• Stage Focus: Series A, Series B
• Location: San Francisco, California, USA
• Website: databricks.com/ventures

7. Felicis

Early-stage VC known for backing developer tools and infrastructure with fast decision-making.

• Recent Deals: Weights & Biases Series A ($15M, 2020), Modal Series A ($16M, 2023)
• LinkedIn: Felicis
• Sector Focus: Developer tools, MLOps platforms, AI infrastructure, model training
• Stage Focus: Seed, Series A
• Location: Menlo Park, California, USA
• Website: felicis.com

8. Google Ventures (GV)

Strategic investor with Google Cloud connections but slower processes than independent VCs.

• Recent Deals: Snorkel AI Series D ($135M, 2022), Alchemy Series C1 ($200M, 2022)
• LinkedIn: Google Ventures
• Sector Focus: ML platforms, data labeling, MLOps, AI infrastructure
• Stage Focus: Series B, Series C, Growth
• Location: Mountain View, California, USA
• Website: gv.com

9. Greylock Partners

Enterprise-focused VC with strong ML infrastructure portfolio and Fortune 500 connections.

• Recent Deals: Hugging Face Series D ($235M, 2023), Coreweave Series C ($421M, 2023)
• LinkedIn: Greylock Partners
• Sector Focus: ML platforms, model hosting, MLOps, AI infrastructure
• Stage Focus: Series A, Series B, Series C
• Location: Menlo Park, California, USA
• Website: greylock.com

10. Index Ventures

European VC with US presence, strong in developer tools and infrastructure across both markets.

• Recent Deals: Comet ML Series B ($50M, 2022), Weaviate Series B ($50M, 2023)
• LinkedIn: Index Ventures
• Sector Focus: Experiment tracking, vector databases, MLOps platforms, AI infrastructure
• Stage Focus: Series A, Series B
• Location: London, UK (with San Francisco office)
• Website: indexventures.com

11. Intel Capital

Corporate VC focused on AI infrastructure with strategic value through Intel hardware ecosystem.

• Recent Deals: Cnvrg.io Series A ($8M, 2020, acquired by Intel 2021), SambaNova Series D ($676M, 2021)
• LinkedIn: Intel Capital
• Sector Focus: ML platforms, AI accelerators, MLOps, edge AI
• Stage Focus: Series A, Series B, Series C
• Location: Santa Clara, California, USA
• Website: intelcapital.com

12. Insight Partners

Growth equity firm focused on software companies with proven revenue and enterprise customers.

• Recent Deals: Dataiku Series E ($400M, 2022), DataRobot Series G ($250M, 2021)
• LinkedIn: Insight Partners
• Sector Focus: Enterprise ML platforms, MLOps, data science platforms, AI operations
• Stage Focus: Series C, Series D, Growth
• Location: New York, New York, USA
• Website: insightpartners.com

13. Lightspeed Venture Partners

Multi-stage VC with experience in infrastructure and developer tools including ML operations.

• Recent Deals: Determined AI Series B ($27M, 2021, acquired by HPE 2021), Anyscale Series C ($99M, 2022)
• LinkedIn: Lightspeed Venture Partners
• Sector Focus: Distributed training, ML infrastructure, MLOps, compute optimization
• Stage Focus: Series A, Series B, Series C
• Location: Menlo Park, California, USA
• Website: lsvp.com

14. Madrona Venture Group

Pacific Northwest VC with strong enterprise software and infrastructure portfolio in Seattle ecosystem.

• Recent Deals: OctoML Series B ($85M, 2022), Orca Security Series C ($340M, 2021)
• LinkedIn: Madrona Venture Group
• Sector Focus: ML optimization, MLOps, cloud infrastructure, developer tools
• Stage Focus: Seed, Series A, Series B
• Location: Seattle, Washington, USA
• Website: madrona.com

15. NEA (New Enterprise Associates)

Large multi-stage firm with deep enterprise software experience and Fortune 500 relationships.

• Recent Deals: DataRobot Series C ($54M, 2018), Databricks Series F ($250M, 2019)
• LinkedIn: NEA
• Sector Focus: Enterprise ML platforms, MLOps, data platforms, AI infrastructure
• Stage Focus: Series B, Series C, Series D, Growth
• Location: Menlo Park, California, USA
• Website: nea.com

16. Salesforce Ventures

Corporate VC with strong enterprise connections and CRM integration opportunities for MLOps platforms.

• Recent Deals: Snorkel AI Series D ($135M, 2022), DataRobot Series G ($250M, 2021)
• LinkedIn: Salesforce Ventures
• Sector Focus: Enterprise ML, MLOps platforms, data labeling, AI operations
• Stage Focus: Series B, Series C, Growth
• Location: San Francisco, California, USA
• Website: salesforce.com/ventures

17. Sequoia Capital

Top-tier VC with best follow-on funding track record but hardest to get initial meetings with.

• Recent Deals: Snorkel AI Series D ($135M, 2022), Weights & Biases Series B ($45M, 2021)
• LinkedIn: Sequoia Capital
• Sector Focus: MLOps platforms, model monitoring, AI infrastructure, data labeling
• Stage Focus: Series A, Series B, Series C, Growth
• Location: Menlo Park, California, USA
• Website: sequoiacap.com

Track your investor outreach properly

These 17 investors closed MLOps deals from 2023 to 2026. Before you start reaching out, set up proper tracking. Most founders waste time following up with investors who never opened their deck.

Upload your deck to Ellty and create a unique link for each investor. You'll see exactly which slides they view and how long they spend on your model monitoring architecture. Most founders are surprised to learn investors skip their market size slides but spend 5+ minutes on technical differentiation and unit economics. If an investor views your deck three times but ignores your feature store architecture, they probably don't understand the technical depth.

When investors ask for more materials during diligence, share an Ellty data room instead of messy email threads. Your technical documentation, infrastructure costs, and customer deployment diagrams in one secure place with view analytics. You'll know when they're actually reviewing your materials vs. just saying they are.

Common questions

How do I know if an MLOps investor is still active in 2026?

Check their last 2-3 deals on Crunchbase or Pitchbook. If they haven't funded ML infrastructure since 2022, they likely shifted to LLM tooling or other sectors. Look for 2024-2026 investments specifically.

Should I target seed or growth stage investors first?

Depends on your ARR and customer list. Below $1M ARR, focus on seed investors like Costanoa or Felicis. Above $10M ARR with enterprise logos, approach growth firms like Battery or Insight Partners. Series A investors want $2M-5M ARR usually.

Do investors really care about deck analytics?

Yes, but not the way you think. If an investor forwards your Ellty link to their team and three partners view it, that's a strong signal. If they view your deck once for 30 seconds, they're not interested. Use the data to prioritize follow-ups.

What's the difference between MLOps and LLM infrastructure investors?

MLOps investors focus on traditional ML operations - feature stores, model monitoring, training infrastructure. LLM investors want prompt management, vector databases, and inference optimization. Some like a16z do both, but most picked a lane by 2024.

When should I set up a data room?

Before your first partner meeting. Investors will ask for technical architecture, cost breakdowns, and customer deployment details within 48 hours of interest. Having an Ellty data room ready speeds up diligence by 2-3 weeks.

How many investors should I contact?

For MLOps, target 15-20 investors max. The market is specialized enough that most infrastructure VCs will know if you're talking to their competitors. Focus on quality intros over spray-and-pray outreach.