Anthropic is an AI research company whose product, Claude, is an AI assistant available for a variety of business tasks. Boasting a 100K+ token windows, Claude can handle complex multi-step instructions over large amounts of content.
$20
per month
H2O.ai
Score6.4 out of 10
N/A
An open-source end-to-end GenAI platform for air-gapped, on-premises or cloud VPC deployments. Users can Query and summarize documents or just chat with local private GPT LLMs using h2oGPT, an Apache V2 open-source project. And the commercially available Enterprise h2oGPTe provides information retrieval on internal data, privately hosts LLMs, and secures data.
N/A
Pricing
Claude
H2O.ai
Editions & Modules
Pro
$20
per month
Team - Standard
$30
per month per seat (minimum 5)
Team - Premium
$150
per month per seat (minimum 5)
Max
from $100
per month
Enterprise
Contact Sales
No answers on this topic
Offerings
Pricing Offerings
Claude
H2O.ai
Free Trial
No
No
Free/Freemium Version
Yes
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
Discount available for annual billing and Pro, Max and Standard Team plans. Claude Code included in higher tier service. Usage-based billing available with API access plans with discount for batch processing.
If you have a sprawling legacy codebase without human subject matter expertise, Claude can be a decent option to get you through some bug triage. However, if you have a legacy codebase and no SMEs, you have bigger problems than bugs. LLMs are known for their uncanny abilities to trick a certain type of user into believing they have created something beautiful and useful, and this undercurrent of delusion runs strong through Claude.
Most suited if in little time you wanted to build and train a model. Then, H2O makes life very simple. It has support with R, Python and Java, so no programming dependency is required to use it. It's very simple to use. If you want to modify or tweak your ML algorithm then H2O is not suitable. You can't develop a model from scratch.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
It's pretty convenient. The interface is clear and straightforward, with no unnecessary complexity. The section covering pricing tariffs and usage limits is easy to understand.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
I still prefer GPT for conversational AI and image generation. Anthropic Claude is more of a tool to actually do something. Where GPT will get you the info you need, Anthropic Claude is much better suited to building something into your application. If I want to build a spreadsheet or if I want to turn a website into a PowerPoint presentation. I do it on Anthropic Claude.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Both are open source (though H2O only up to some level). Both comprise of deep learning, but H2O is not focused directly on deep learning, while Tensor Flow has a "laser" focus on deep learning. H2O is also more focused on scalability. H2O should be looked at not as a competitor but rather a complementary tool. The use case is usually not only about the algorithms, but also about the data model and data logistics and accessibility. H2O is more accessible due to its UI. Also, both can be accessed from Python. The community around TensorFlow seems larger than that of H2O.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
ROI is immediate in the consulting space. We draft documents for clients often and have the ability to produce exponential output with Claude.
Claude has allowed us to develop HTML and java code based on our requirements for a client. It has also helped identify bad code and shortened our development lifecycle
Claude has turned into a value added platform that we rely on to verify information and provide suggestions. We look at Claude as a strategic technology partner
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Positive impact: saving in infrastructure expenses - compared to other bulky tools this costs a fraction
Positive impact: ability to get quick fixes from H2O when problems arise - compared to waiting for several months/years for new releases from other vendors
Positive impact: Access to H2O core team and able to get features that are needed for our business quickly added to the core H2O product
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
