AWS re:Invent 2026 is scheduled for November 30–December 4 in Las Vegas, Nevada. The official event site describes five days with more than 2,200 sessions across cloud, AI, security, migration and developer productivity. Programming is distributed across six venues on the Las Vegas Strip: Caesars Forum, Caesars Palace, Encore Las Vegas, MGM Grand, The Venetian Resort and Wynn Las Vegas. See the official event information. Review the official campus and venue guide.

The scale of re:Invent makes it easy to collect product names without resolving the harder questions: what a platform actually replaces, where it fits beside native AWS services, what operating burden remains, and how costs or risks change at production scale. This watchlist is designed to help cloud, data, security, AI, FinOps and engineering teams prepare more useful meetings.

Methodology and disclosure: This is a non-ranked editorial watchlist built from the supplied AWS re:Invent 2026 company export. TechCurrent selected a cross-section based on product relevance, differentiation and usefulness to technical and operational buyers, then reviewed first-party company materials linked throughout the article. Entry numbers are for navigation only. Inclusion is free, is not an endorsement and does not imply that TechCurrent is affiliated with AWS or any listed company. Company positioning and event participation can change; reconfirm the official AWS re:Invent sponsor directory before scheduling a meeting.

Last verified: September 21, 2026.

Cloud infrastructure and platform engineering

1. Pulumi — infrastructure as code and cloud governance

Pulumi lets teams define and manage cloud infrastructure using general-purpose languages, YAML or HCL, with related capabilities for secrets, policy, discovery and self-service infrastructure. Why it may matter at re:Invent: platform teams evaluating how humans and coding agents provision AWS resources can compare Pulumi with CloudFormation, CDK and Terraform-based workflows. Questions to evaluate: How are state, drift, policy exceptions and short-lived credentials handled, and which controls remain portable across clouds?

2. LocalStack — local testing for AWS-oriented applications

LocalStack simulates AWS services on developer or controlled infrastructure so teams can test cloud behavior without using live cloud accounts for every development cycle. Why it may matter at re:Invent: organizations trying to accelerate AWS development can examine whether local emulation improves feedback, security and cost control. Questions to evaluate: Which AWS APIs and failure modes are covered, how is parity measured, and what must still be validated in an actual AWS environment?

3. Nutanix — hybrid cloud, virtualization and Kubernetes infrastructure

Nutanix provides infrastructure for running virtual machines, containers, data services and AI workloads across on-premises, cloud and edge environments. Why it may matter at re:Invent: enterprises balancing AWS adoption with existing data-center estates can compare migration, coexistence and operating models. Questions to evaluate: Which workloads can move without redesign, how are networking and data mobility handled, and where do licensing or egress costs appear?

4. Megaport — on-demand private cloud connectivity

Megaport provides software-defined connectivity between data centers, cloud providers and other service endpoints, including connectivity to AWS Direct Connect. Why it may matter at re:Invent: network teams can evaluate private connectivity for hybrid, multi-region and multicloud designs. Questions to evaluate: What are the redundancy model, provisioning boundaries, throughput commitments and failure paths, and how are cross-cloud data-transfer costs measured?

5. SUSE — enterprise Linux and Kubernetes operations

SUSE supplies enterprise Linux and cloud-native products including Rancher for Kubernetes fleet management, with security and observability capabilities around containerized workloads. Why it may matter at re:Invent: teams running mixed EKS, on-premises and edge estates can examine whether a common management layer reduces operational variation. Questions to evaluate: Which EKS lifecycle tasks are centralized, how are upstream changes supported, and what data or control-plane components remain self-managed?

6. Kion — cloud governance and FinOps controls

Kion provides governance, account automation, policy enforcement, financial management and compliance workflows across public-cloud environments. Why it may matter at re:Invent: AWS organizations with many accounts can compare preventive guardrails and automated provisioning with native control-tower and billing workflows. Questions to evaluate: Which policies block activity versus report it, how are exceptions approved, and how does the product map spend and risk to accountable teams?

7. Temporal — durable execution for distributed applications

Temporal provides an open-source and managed platform for stateful workflows that resume through service failures, retries and long-running processes. Why it may matter at re:Invent: builders of microservices, transactions and AI agents can evaluate durable execution alongside queues, step functions and custom orchestration. Questions to evaluate: What are the replay and versioning rules, how is workflow history retained, and what operational expertise is needed for self-hosted versus managed deployment?

8. Boomi — integration, data and API management

Boomi connects applications, data, APIs and business processes, with integration, data-management, API-management and agent-governance capabilities. Why it may matter at re:Invent: enterprises modernizing on AWS can test whether one integration layer reduces custom connectors across cloud and legacy systems. Questions to evaluate: Where do runtimes execute, how are mappings and policies versioned, what is portable, and how does usage-based pricing behave at scale?

9. WSO2 — API, integration, identity and engineering platforms

WSO2 provides open-source and SaaS platforms spanning API management, integration, identity, developer platforms and governance for AI agents and services. Why it may matter at re:Invent: teams building AWS-based application and agent ecosystems can examine a composable control layer for APIs, events, identities and developer workflows. Questions to evaluate: Which components can run self-managed or as SaaS, how are MCP and AI-gateway policies enforced, what telemetry supports audits, and how much operational work accompanies the open-source deployment options?

10. Workato — enterprise orchestration and automation

Workato provides integration and workflow automation across enterprise applications, data and APIs, with tools for governed agent and business-process orchestration. Why it may matter at re:Invent: operations and IT teams can compare low-code orchestration with bespoke serverless integrations. Questions to evaluate: How are recipes tested and promoted, where do credentials and data pass, how are human approvals represented, and what happens when a downstream action partially fails?

Data, databases and analytics

11. Fivetran — automated data movement

Fivetran moves data from operational sources into cloud data platforms using managed connectors, change-data capture and transformation workflows. Why it may matter at re:Invent: data teams can evaluate the operating trade-off between managed pipelines and internally maintained ingestion. Questions to evaluate: How are schema changes, deletes, re-syncs and data residency handled, and how does cost scale with high-volume or frequently changing sources?

12. ClickHouse — real-time analytical database

ClickHouse offers an open-source columnar database and managed cloud service for real-time analytics, observability and AI-related workloads. Why it may matter at re:Invent: architects can compare it with warehouses, search systems and AWS-native analytical stores for latency-sensitive use cases. Questions to evaluate: What ingestion and query patterns fit best, how are replicas and backups managed, and how do compression, compute and storage choices affect total cost?

13. Redis — real-time data, caching and context services

Redis provides in-memory data services for caching, sessions, streaming, search, feature data and AI context, with open-source, managed and self-managed deployment options. Why it may matter at re:Invent: application and AI teams can examine when a dedicated Redis platform adds capabilities beyond a basic managed cache. Questions to evaluate: What durability and consistency guarantees apply, how are multi-region failovers tested, and which data structures or search features create migration dependencies?

14. Neo4j — graph database and graph analytics

Neo4j provides graph database, analytics and data-science tooling for applications that rely on relationships across entities, including fraud, knowledge-graph and AI-context use cases. Why it may matter at re:Invent: data teams can compare graph-native modeling with relational, document and vector approaches. Questions to evaluate: Which traversals justify a graph model, how are clusters operated on AWS, and how are graph, vector and source-of-truth data kept consistent?

15. SingleStore — distributed SQL for transactional and analytical workloads

SingleStore provides a distributed SQL database intended to support low-latency transactions, analytics, vector search and data-intensive applications on one platform. Why it may matter at re:Invent: architects considering database consolidation can test where mixed workloads are practical. Questions to evaluate: How do workload isolation, indexing, data ingestion and scaling behave under production load, and what compatibility or migration constraints remain?

AI, machine learning and GPU infrastructure

16. Anthropic — foundation models and enterprise AI tools

Anthropic develops the Claude family of models and related developer, coding and enterprise products, including access through AWS. Why it may matter at re:Invent: teams using Amazon Bedrock can evaluate model behavior, tooling and deployment choices in the context of their AWS architecture. Questions to evaluate: Which model and region meet the workload’s quality, latency and residency needs, how are prompts and tool calls evaluated, and what controls govern sensitive data and agent actions?

17. OpenAI — AI models, APIs and enterprise applications

OpenAI provides foundation models, developer APIs, ChatGPT products and tools for building and operating AI applications. Why it may matter at re:Invent: multi-model and cloud-platform teams can compare direct API integration with AWS-hosted model services and surrounding governance. Questions to evaluate: What data path, retention, latency and availability commitments apply, how will evaluations detect regressions, and where should application teams retain provider portability?

18. Baseten — production AI inference infrastructure

Baseten provides managed, single-tenant and self-hosted infrastructure for serving open, custom and fine-tuned models, with optimization and deployment tooling. Why it may matter at re:Invent: AI platform teams can compare specialized inference infrastructure with directly operating GPU endpoints on AWS. Questions to evaluate: How are cold starts, autoscaling and failover measured, which optimizations are model-specific, and what is the unit cost at realistic concurrency and token profiles?

19. Fireworks — optimized model inference and customization

Fireworks AI offers hosted inference and tooling for deploying, fine-tuning and operating generative-AI models. Why it may matter at re:Invent: builders can evaluate whether an inference-focused platform improves latency or developer velocity while fitting an AWS-centered data architecture. Questions to evaluate: Where do models and prompts execute, which hardware and regions are available, how are custom weights protected, and what benchmarks reflect the buyer’s actual workload?

20. Arize AI — AI observability and evaluation

Arize provides tracing, evaluation, debugging and monitoring for machine-learning and generative-AI applications, alongside the open-source Phoenix project. Why it may matter at re:Invent: teams moving Bedrock or custom-model applications into production can examine how quality and failure modes are measured over time. Questions to evaluate: Which traces leave the AWS environment, how are human and automated evaluations calibrated, and can teams reproduce a bad result from the stored evidence?

Security, identity and compliance

21. 1Password — credentials, secrets and privileged access

1Password provides password management, secrets management, device trust, SaaS access governance and privileged-access controls for people, machines and AI agents. Why it may matter at re:Invent: security teams can examine how workforce and workload credentials connect to AWS identities and developer tooling. Questions to evaluate: How are short-lived credentials issued, which actions are audited, how are emergency-access paths controlled, and what happens when identity providers or vault services are unavailable?

22. Cyera — data security posture and AI data controls

Cyera discovers and classifies enterprise data, maps exposure and access, and applies data-security controls across cloud and AI environments. Why it may matter at re:Invent: security and data teams can evaluate visibility into sensitive information spread across AWS accounts and services. Questions to evaluate: What permissions are required for discovery, how are classifications validated, how quickly does posture information refresh, and which remediation actions are automated versus advisory?

23. Upwind — runtime-informed cloud security

Upwind combines cloud posture, workload, identity, vulnerability and runtime signals to prioritize cloud-native risk. Why it may matter at re:Invent: AWS security teams can test whether runtime context reduces the volume of theoretical findings. Questions to evaluate: Which workloads need sensors, what coverage remains agentless, how is an attack path proven, and how are production controls introduced without disrupting applications?

24. Orca Security — cloud-native application protection

Orca Security brings cloud posture, identity, workload, data, vulnerability, code and AI-security findings into a shared risk model, using agentless scanning with optional runtime sensors. Why it may matter at re:Invent: teams can compare consolidated cloud risk visibility with multiple AWS-native and third-party tools. Questions to evaluate: What accounts and assets are fully covered, how are reachable risks prioritized, which findings can be remediated safely, and how is evidence retained for compliance?

25. Prowler — open cloud security and compliance

Prowler offers open-source and managed cloud-security tooling for posture assessment, risk prioritization and continuous compliance across AWS and other environments. Why it may matter at re:Invent: teams can evaluate an open approach that can fit both engineering workflows and centralized governance. Questions to evaluate: Which checks are authoritative for the organization’s policies, how are false positives and exceptions managed, and what operational responsibilities differ between the open-source and managed options?

Observability, reliability and operations

26. Datadog — full-stack observability and cloud security

Datadog combines infrastructure and application monitoring, logs, traces, digital experience data, incident workflows and security signals in a SaaS platform. Why it may matter at re:Invent: AWS teams can assess the value and cost of correlating telemetry across services and accounts. Questions to evaluate: Which telemetry should be sampled or retained, how are high-cardinality dimensions controlled, and what does pricing look like under peak production volume?

27. Elastic — search, observability and security analytics

Elastic provides search and analytics through Elasticsearch and related products for observability, security and enterprise-search workloads, with managed and self-managed deployment choices. Why it may matter at re:Invent: teams can compare a flexible search platform with specialized AWS and SaaS services. Questions to evaluate: How are clusters sized and upgraded, what data tiers and retention policies are practical, and where does schema or query flexibility increase operating complexity?

28. Sumo Logic — cloud log analytics, observability and SIEM

Sumo Logic provides log management, application and infrastructure monitoring, security analytics and SIEM capabilities in a cloud service. Why it may matter at re:Invent: operations and security teams can examine whether a shared log foundation improves investigations across AWS. Questions to evaluate: How are ingestion, retention and query costs controlled, which AWS signals are normalized, and how can teams verify the evidence behind automated triage?

29. Rootly AI — on-call and incident management

Rootly provides on-call scheduling, incident response, communications, retrospectives and AI-assisted root-cause workflows. Why it may matter at re:Invent: SRE teams can evaluate how incident coordination connects to AWS telemetry and existing collaboration systems. Questions to evaluate: Which actions can AI perform, where is human approval required, how are timelines reconstructed, and how is the system tested when a primary communication channel fails?

30. LogicMonitor — hybrid infrastructure observability

LogicMonitor monitors infrastructure across cloud and on-premises environments, combining discovery, metrics, logs, topology and AIOps-oriented analysis. Why it may matter at re:Invent: enterprises with hybrid estates can assess whether one operating view simplifies migration and steady-state support. Questions to evaluate: How are resources discovered across AWS accounts, what requires collectors, how is topology kept current, and how does alert tuning work across different platform teams?

Developer platforms and software delivery

31. GitLab — integrated software delivery and DevSecOps

GitLab brings source control, planning, CI/CD, security scanning, software-supply-chain controls and deployment workflows into one platform. Why it may matter at re:Invent: engineering leaders can examine whether consolidation improves traceability from code to AWS production. Questions to evaluate: Which controls are enforced in pipelines, how are runners isolated and scaled, where are artifacts retained, and how easily can teams integrate specialist tools?

32. LaunchDarkly — feature management and runtime control

LaunchDarkly provides feature flags, progressive delivery, experimentation, observability and controls for application and AI-agent behavior. Why it may matter at re:Invent: teams releasing frequently on AWS can evaluate separating deployment from exposure and rollback. Questions to evaluate: What happens if the control service is unreachable, how are flags governed and retired, and which production metrics are trusted enough to automate a rollback?

33. Vercel — web and AI application platform

Vercel provides deployment, content delivery, compute, observability and an AI application stack around frameworks including Next.js. Why it may matter at re:Invent: web and AI teams can compare an application-focused platform with assembling the same layers directly on AWS. Questions to evaluate: Where do workloads and data execute, which AWS services remain visible or configurable, how are edge and serverless costs modeled, and what is the exit path for platform-specific features?

34. CircleCI — continuous integration and software validation

CircleCI automates builds, tests and delivery workflows across hosted and self-hosted execution environments. Why it may matter at re:Invent: teams can examine how CI capacity, security and developer feedback integrate with AWS deployment pipelines. Questions to evaluate: How are runners isolated, secrets scoped and build artifacts protected, and how do concurrency, caching and compute choices affect both speed and cost?

FinOps, governance and optimization

35. Apptio — technology financial management and cloud economics

Apptio provides planning, cost allocation, benchmarking and FinOps capabilities for understanding technology investment and cloud spend. Why it may matter at re:Invent: finance, IT and engineering leaders can examine a common model for AWS cost ownership and planning. Questions to evaluate: How are shared costs allocated, what data must be normalized, how quickly can forecasts respond to architecture changes, and which recommendations require engineering validation?

36. CloudZero — cloud and AI unit-cost intelligence

CloudZero connects cloud and AI spend with business dimensions such as products, customers, features and transactions. Why it may matter at re:Invent: teams can move the AWS cost conversation from account totals to unit economics. Questions to evaluate: How are untagged and shared costs allocated, which business data is required, how fresh are anomaly signals, and can engineers trace every recommendation to underlying billing and usage data?

37. Finout — multi-cloud, Kubernetes and AI FinOps

Finout consolidates cost data across cloud, Kubernetes, data and AI services, with allocation, forecasting, anomaly detection and optimization workflows. Why it may matter at re:Invent: FinOps teams can evaluate one cost model across AWS and adjacent usage-based platforms. Questions to evaluate: How are virtual tags governed, which costs cannot be allocated reliably, how are savings validated after a change, and what prevents automated agents from optimizing away required resilience?

38. Vantage — cloud cost visibility and optimization

Vantage provides cost reporting, allocation, forecasting, anomaly detection and optimization across cloud and other infrastructure providers. Why it may matter at re:Invent: engineering and finance teams can compare a dedicated FinOps layer with AWS-native cost tools. Questions to evaluate: How are commitments and shared services modeled, what actions can be automated, how are recommendations verified, and does the pricing model remain economical as monitored spend grows?

How to use the watchlist

Begin with an operating problem rather than a booth or sponsor tier. Bring one current architecture, workload or process; identify the owner, failure path, compliance boundary and cost driver; then ask each company to work through that concrete example.

For technical evaluation, ask what the product replaces, which AWS services it depends on, what permissions and data it requires, how it behaves during failure, and how a customer can export configuration and evidence. For operational evaluation, ask who owns day-two support, how pricing responds to production volume, what the migration and rollback plan looks like, and which claimed outcomes can be demonstrated in your environment.