Skip to content
SlateIndex

Valohai alternatives

New

The MLOps Platform for LLMs and Specialized Models

MLOps PlatformsSan Francisco, California, US11–50 employeesFounded 2016

Visibility score
1.9%
New vs last week
Weekly run · Nov 2

Data collected ·ChatGPT, Perplexity, Google AI Mode·coverage 100%·next run

Alternatives · updated Nov 2, 2026

Valohai alternatives: top competitors compared

Valohai has a visibility score of 1.9 in MLOps Platforms, #30 of 61. AI answers that name Valohai most often also name these 4.

Quick reference

AlternativeBest forStarting priceVisibilityKey differentiator
DatabricksData engineering, data science, analytics and AI teams at startups and global enterprises—46.2%One open lakehouse architecture that unifies data, analytics and AI on Delta Lake and Apache Spark
Amazon SageMaker AI——44.4%—
Google Vertex AI——33.1%Tight Google Cloud integration with no infrastructure to manage, according to reviewers.
Microsoft Azure Machine Learning——28.4%—

Databricks suits data engineering, data science, analytics and AI teams at startups and global enterprises.

Quick facts

  • Pricing not available
  • 46.2% visibility score · #2 in MLOps Platforms

Key features

  • Lakebase — Serverless Postgres database integrated with the lakehouse for transactional applications.
  • Genie — Conversational analytics interface for natural-language questions and insights.
  • Unity Gateway — Governance, cost control, observability and guardrails for enterprise AI access.
  • Lakehouse — Serverless data warehousing on open lake data, with governance and AI built in.

Databricks vs Valohai

DimensionDatabricksValohai
Visibility score46.2%1.9%
Pricing model—Per seat
Entry price—Contact sales
Free plan—No
Trial—14-day free trial

Pros

  • Lakehouse architecture combining data warehousing with data lake flexibility
  • Strong for machine learning, AI, and data engineering workloads
  • Top customer satisfaction ratings alongside Snowflake
  • Free trial and free Edition for learning
  • Discounts for annual prepaid commitments

Cons

  • Complex consumption-based pricing with no single fixed price
  • Best only when ML, Spark, or Python skills are central to the team
Full Databricks vs Valohai comparison

Amazon SageMaker AI is ranked #3 in MLOps Platforms.

Quick facts

  • Pricing not available
  • 44.4% visibility score · #3 in MLOps Platforms

Key features

  • Amazon SageMaker Studio — Single web interface for end-to-end model development, including preparing data, training, customizing, and deploying models.
  • SageMaker Autopilot — Named as a core component of SageMaker AI.
  • SageMaker Model Monitor — Named as a core component of SageMaker AI.
  • SageMaker JumpStart — Pretrained models and one-click solutions.

Amazon SageMaker AI vs Valohai

DimensionAmazon SageMaker AIValohai
Visibility score44.4%1.9%
Pricing model—Per seat
Entry price—Contact sales
Free plan—No
Trial—14-day free trial

Pros

  • End-to-end managed platform for building, training, and deploying ML models
  • Deep integration with the AWS ecosystem
  • Scalable training and deployment
  • Strong reviews, around 4.3/5 on G2
  • Well established and widely used enterprise MLOps platform

Cons

  • Expensive, heavy enterprise suite for startups
  • Less developer-centric than some alternatives
  • Total costs depend on compute, storage, and API usage
Full Amazon SageMaker AI vs Valohai comparison

Google Vertex AI is ranked #5 in MLOps Platforms.

Quick facts

  • Pricing not available
  • 33.1% visibility score · #5 in MLOps Platforms

Key features

  • Vertex AI Pipelines — Serverless orchestrator that runs KFP or TFX pipelines.
  • Feature Store — Managed feature store for ML workflows.
  • Model Registry — Registers models before they are deployed to endpoints.
  • Model Monitoring — Detects skew and drift in deployed models.

Google Vertex AI vs Valohai

DimensionGoogle Vertex AIValohai
Visibility score33.1%1.9%
Pricing model—Per seat
Entry price—Contact sales
Free plan—No
Trial—14-day free trial

Pros

  • unified ML platform across the lifecycle
  • strong Google Cloud integration
  • strong support for generative and foundation models
  • robust MLOps and agentic workflow support
  • $300 starter credits

Cons

  • less developer-centric for Python, Kubernetes and Git-based teams
  • usage-based costs depend on compute, storage and API usage
Full Google Vertex AI vs Valohai comparison

Microsoft Azure Machine Learning is ranked #7 in MLOps Platforms.

Quick facts

  • Pricing not available
  • 28.4% visibility score · #7 in MLOps Platforms

Key features

  • Azure Machine Learning — Cloud service that manages the ML project lifecycle, with MLOps tools to monitor, retrain and redeploy models.
  • Model catalog — Hub of hundreds of models from providers including Azure OpenAI, Mistral, Meta, Cohere, NVIDIA and Hugging Face.

Microsoft Azure Machine Learning vs Valohai

DimensionMicrosoft Azure Machine LearningValohai
Visibility score28.4%1.9%
Pricing model—Per seat
Entry price—Contact sales
Free plan—No
Trial—14-day free trial

Pros

  • Strong enterprise integration with Microsoft Azure and the broader Microsoft ecosystem
  • End-to-end managed ML lifecycle management
  • Strong pipeline orchestration, security, and automated model tracking
  • Cost-effective managed ecosystem
  • Model monitoring in production

Cons

  • Less developer-centric for teams with highly customized Python, Kubernetes, or CI/CD workflows
  • Usage-based costs depend on compute, storage, and API usage
Full Microsoft Azure Machine Learning vs Valohai comparison

How to choose

Match the pricing model to your cost driver: headcount, usage or features.

PlatformPricing modelStarting price
ValohaiPer seatContact sales
Databricks——
Amazon SageMaker AI——
Google Vertex AI——
Microsoft Azure Machine Learning——

More alternatives

Ranked by how often AI answers name them alongside Valohai in the latest weekly run.

Cite as: SlateIndex, “Valohai AI visibility profile”, https://slateindex.ai/brand/valohai, data of Nov 2, 2026. Licence: CC BY 4.0.