Chat2Report vs Clusy: Which is Better in 2026?
A comprehensive comparison of Chat2Report and Clusy covering features, pricing, use cases, and which tool is the right choice for your needs.
⚡ Quick Verdict
Choose Chat2Report if:
- →You want more affordable paid plans (from $14.9/mo)
- →You need chat over 10-k, 10-q, 8-k and 20-f filings with answers traced back to the source or one-click summaries, extracted key figures and mind-map views of a filing's structure
Choose Clusy if:
- →You need a broader feature set (6 features vs 5)
- →You need describe an experiment and it runs the branches, returning the one that worked or autonomous dataset discovery and preprocessing
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Chat2Report vs Clusy: At a Glance
Pricing Comparison: Chat2Report vs Clusy
Understanding the pricing differences between Chat2Report and Clusy is crucial for making the right choice. Here's how their plans compare side by side.
Clusy Pricing
💡 Pricing takeaway: Both Chat2Report and Clusy offer free tiers, making it easy to try before you buy. Compare the specific plans to find the best value for your use case.
Feature-by-Feature Comparison
Here's how every feature from Chat2Report and Clusy stacks up.
What Makes Each Tool Unique
🔵 Unique to Chat2Report
Features available in Chat2Report but not in Clusy:
- ✓Chat over 10-K, 10-Q, 8-K and 20-F filings with answers traced back to the source
- ✓One-click summaries, extracted key figures and mind-map views of a filing's structure
- ✓Agent-driven fundamentals deep-dive and market-sentiment analysis on paid tiers
- ✓Earnings-call transcripts and curated news alongside the filings
- ✓Multi-language output with original formatting, multi-format export and offline download
🟣 Unique to Clusy
Features available in Clusy but not in Chat2Report:
- ✓Describe an experiment and it runs the branches, returning the one that worked
- ✓Autonomous dataset discovery and preprocessing
- ✓Sandboxed training with GPUs provisioned as part of the plan
- ✓Queue follow-up instructions while a run is still executing
- ✓Open-weight and frontier models on the same surface
- ✓Notebook interface you can read and edit rather than a black box
Use Case Recommendations
Best for: Chat2Report
Chat2Report is a question-answering layer over US company filings, aimed at the value investor who reads 10-Ks rather than the trader who reads charts. You search by company name or ticker and the product pulls the filing set — 10-K, 10-Q, 8-K and 20-F — then produces a structured summary, a mind-map of the report's shape and a set of key extracted figures rather than dropping you into a raw PDF. From there it is a chat interface: ask a question about the filing and the answer comes back with a traceable citation into the source document, which is the feature that separates a research tool from a hallucination machine in this category. Coverage is stated at over 3,000 US companies, with earnings-call transcripts alongside the filings and a curated news feed on the paid tiers. Two agent-driven analyses sit above the Q&A — a fundamentals deep-dive and a market-sentiment read — and fundamental analysis rankings let you compare companies rather than only read them one at a time. Output is multi-language with the original formatting preserved, exportable in several formats, and downloadable for offline reading. Pricing is credit-metered with a free tier whose credits never reset, which is a friendlier arrangement than the usual monthly expiry. The constraints are geographic and historical: coverage is US-listed companies, and the depth of financial history you can reach is tiered, running from 10 years and 6 quarters on free up to 30 years and 40 quarters on the top plan.
Ideal use cases:
- •Teams or individuals who need chat over 10-k, 10-q, 8-k and 20-f filings with answers traced back to the source
- •Teams or individuals who need one-click summaries, extracted key figures and mind-map views of a filing's structure
- •Teams or individuals who need agent-driven fundamentals deep-dive and market-sentiment analysis on paid tiers
- •Teams or individuals who need earnings-call transcripts and curated news alongside the filings
- •Anyone focused on financial-analysis workflows
- •Anyone focused on sec-filings workflows
Best for: Clusy
Clusy is an agent-native notebook for machine learning and data science — the pitch is that instead of writing cells, you describe the experiment and it runs every branch. Give it a research question and it finds the data, writes the cells, provisions the compute, runs the work and hands back the branch that succeeded, with the notebook filling in as it goes so you can watch and intervene. You can queue a follow-up while a run is in flight rather than waiting for it to finish. The workflows it advertises end to end are the ones that normally span four tools: dataset discovery, preprocessing, sandboxed training, and parallel experiments, all from a single prompt. Testimonials on the site come from named researchers — a MATS research fellow and OpenAI red-teaming contractor, a Tsinghua postgraduate researcher, an incoming Google engineer, and the co-founder of a coding-agent company using it to post-train open-weight models — describing it fetching data autonomously from public repositories and reproducing a model from a paper. Compute is part of the product rather than something you attach: the free tier gives a CPU sandbox and a fast default model, and higher tiers unlock progressively larger GPUs from T4 and L4 up through A100, H100 and H200, plus access to open-weight models and frontier models including Claude and GPT. The company is backed by Founders, Inc.
Ideal use cases:
- •Teams or individuals who need describe an experiment and it runs the branches, returning the one that worked
- •Teams or individuals who need autonomous dataset discovery and preprocessing
- •Teams or individuals who need sandboxed training with gpus provisioned as part of the plan
- •Teams or individuals who need queue follow-up instructions while a run is still executing
- •Anyone focused on notebook workflows
- •Anyone focused on machine-learning workflows
📊 Other Data & Analytics Tools to Consider
Chat2Report and Clusy aren't the only options. Here are other popular tools in the same space:
Databricks AI
Enterprise AI and data lakehouse platform
Akkio
No-code predictive AI for business analysts
Hex
Data workspace with AI analysis and apps
MindsDB
AI layer for databases with SQL ML
Obviously AI
No-code ML platform for predictions
Julius AI
Chat with your data for instant analysis
Is one of these your tool?
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Frequently Asked Questions
Is Chat2Report better than Clusy?
It depends on your needs. Chat2Report offers 5 key features including Chat over 10-K, 10-Q, 8-K and 20-F filings with answers traced back to the source and One-click summaries, extracted key figures and mind-map views of a filing's structure, while Clusy provides 6 features including Describe an experiment and it runs the branches, returning the one that worked and Autonomous dataset discovery and preprocessing. Chat2Report uses a freemium model with a free tier, while Clusy is freemium with free access available. Choose based on which features and pricing model align with your requirements.
Is Chat2Report cheaper than Clusy?
Chat2Report is cheaper, starting at $14.90/month compared to Clusy's $30/month. Both tools offer free tiers, so you can try each before committing. Always check the official websites for the most current pricing.
Can I use Chat2Report and Clusy together?
Yes, many users combine Chat2Report and Clusy in their workflow. Chat2Report excels at chat over 10-k, 10-q, 8-k and 20-f filings with answers traced back to the source, while Clusy shines with describe an experiment and it runs the branches, returning the one that worked. Using both allows you to leverage the strengths of each tool, though this means managing two subscriptions — though free tiers can help manage costs.
What's the main difference between Chat2Report and Clusy?
While both are data & analytics tools, Chat2Report emphasizes chat over 10-k, 10-q, 8-k and 20-f filings with answers traced back to the source, whereas Clusy is known for describe an experiment and it runs the branches, returning the one that worked. The best choice depends on your specific workflow and feature priorities.
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