Overview
Summary
The useful answer before the supporting detail.
TimeGPT is a foundational model for time series forecasting and anomaly detection, provided by Nixtla as an enterprise platform S6. It is used by organizations to generate accurate predictions and detect anomalies at scale, with deployment options on Azure or self-hosted infrastructure S1 S11. The main practical value is enabling data-driven decisions through reliable forecasts, as evidenced by customers like GridStor generating over 500,000 forecasts monthly S166.
Product identity and positioning
Name, category, positioning, audience, and first-party claims.
- Identity: Nixtla is a time series forecasting company offering TimeGPT, a foundational model for forecasting and anomaly detection S1 S6.
- Core purpose: To democratize state-of-the-art predictive insights for time series data S1.
- Intended users: Enterprises, Fortune 500 companies, startups, and academia S1.
- Product scope: TimeGPT is a model with API and SDK access, part of a broader ecosystem including StatsForecast and NeuralForecast S1 S6.
- Positioning: Nixtla positions itself as a leading time series ecosystem, recognized by IDC for three consecutive years S1.
- Claimed distinction: Ranked #1 in over 10 G2 categories, top-rated for predictive analytics S1.
Use cases and audiences
Who the product serves and the jobs it is designed to handle.
| Audience | Practical use case |
|---|---|
| Financial analysts | Forecast Bitcoin prices and detect anomalies for risk management S14. |
| Retail businesses | Forecast demand under different pricing scenarios to support pricing decisions S19. |
| Enterprise teams | Deploy forecasting at scale with managed or self-hosted options S1 S11. |
Traffic and search
SEO analysis
Collected Oct 22, 2026nixtla.io: an estimated 51,567 total monthly visits and about 1,807 monthly organic visits across 320 ranking keywords. Scout crawled 15 pages and measured a technical health score of 97/100.
Search summary
The useful answer before the supporting detail.
Enterprise-grade time series forecasting and anomaly detection. Accurate predictions powered by Nixtla's industry-leading AI solutions. nixtla.io: an estimated 51,567 total monthly visits and about 1,807 monthly organic visits across 320 ranking keywords. Scout crawled 15 pages and measured a technical health score of 97/100.
- Total traffic (modeled): 51,567 monthly visits across all channels.
- Organic search traffic (estimated): 1,807 monthly visits from search.
- Strongest sampled ranking: nixtla at position 1.
- Leading page in the collected evidence: https://www.nixtla.io/.
- Closest measured organic competitor: nixtlaverse.nixtla.io, appearing 3 times in the sampled first-page SERPs.
- Technical crawl: 15 pages, health score 97/100.
Website intelligence — Collected evidence
Company identity and positioning
| Page type | Evidence | Source |
|---|---|---|
| other | Enterprise-grade time series forecasting and anomaly detection. Accurate predictions powered by Nixtla's industry-leading AI solutions. | https://www.nixtla.io/ |
| other | By the team behind the world's most-used forecasting tools. | https://www.nixtla.io/ |
| other | Selected Enterprise Customers | https://www.nixtla.io/ |
| other | Our most advanced product for turning data into production-ready predictions. More than a foundation model, it is a complete and customizable solution based on TimeGPT, Nixtla’s OSS ecosystem, and agentic automation. | https://www.nixtla.io/ |
| other | An intelligent system that automatically builds the right pipeline for each use case, accessible through a dedicated Python package or API. | https://www.nixtla.io/ |
| other | One interface for 200+ forecasting models, including TimeGPT and models from the Nixtlaverse. | https://www.nixtla.io/ |
| other | Turn forecasts into action by delivering predictions, alerts, and runbooks across the business surfaces your teams use every day. | https://www.nixtla.io/ |
| other | Policy controls, audit trails, team isolation, and approvals across supported deployment layers. | https://www.nixtla.io/ |
| other | Real outcomes delivered to customers | https://www.nixtla.io/ |
| other | Serving customers in over 60 regions worldwide, Microsoft supports a diverse client base that includes 95% of Fortune 500 companies, government bodies, and innovative startups. | https://www.nixtla.io/ |
| other | Its dynamic pricing, optimized routing, and intelligent driver dispatch systems set industry benchmarks for technological innovation. | https://www.nixtla.io/ |
| other | The platform emphasizes security, compliance, and transparency, and provides deep liquidity, automated market making, and innovative trading features tailored for institutional and professional traders. | https://www.nixtla.io/ |
Domain performance
Modeled total traffic, six-month trend, engagement, countries, and domain identity.
Total website traffic
Six-month trafficModeled monthly visits
Monthly visits51,567
Global rank#648,220
Top-country rankUS #548,441
Bounce rate36.9%
Pages per visit1.99
Visit duration35s
| Month | Modeled visits |
|---|---|
| 2026-04 | 61,766 |
| 2026-05 | 67,008 |
| 2026-06 | 59,895 |
| 2026-07 | 63,210 |
| 2026-08 | 53,794 |
| 2026-09 | 51,567 |
Top visitor countries
| Country | Name | Traffic share |
|---|---|---|
| US | United States | 24.1% |
| IN | India | 9.4% |
| DE | Germany | 8.3% |
| VN | Vietnam | 4.7% |
| AU | Australia | 4.5% |
24.1%United States
United States24.1%India9.4%Germany8.3%Vietnam4.7%Australia4.5%Other49.0%
Domain identity
| Signal | Value |
|---|---|
| Registered | Jul 7, 2021 |
| Last changed | Aug 12, 2026 |
| Expires | Jul 7, 2029 |
| WHOIS server | whois.nic.io |
| DNSSEC | — |
| Nameservers | ns-cloud-d1.googledomains.com, ns-cloud-d2.googledomains.com, ns-cloud-d3.googledomains.com, ns-cloud-d4.googledomains.com |
Website capture
Organic keywords
Top keywords and an independent Google result page.
Top organic keywords
| Keyword | Volume | CPC | Estimated value |
|---|---|---|---|
| nixtla | 1,300 | $0.00 | $1,290 |
| timegpt | 1,440 | $4.42 | $630 |
| conformal prediction | 5,640 | $0.00 | $300 |
| python mlforecast | 160 | $0.00 | $150 |
| time series startup | 130 | $0.00 | $130 |
Independent Google result page
Query: nixtla
| Position | Title | URL | Description |
|---|---|---|---|
| 1 | Nixtla | Time Series Forecasting & Anomaly Detection | https://www.nixtla.io/ | Enterprise-grade time series forecasting and anomaly detection. Accurate predictions powered by Nixtla's industry-leading AI solutions. |
| 2 | Statistical ⚡️ Forecast | https://nixtlaverse.nixtla.io/statsforecast/index.html | StatsForecast includes an extensive battery of models. Fastest and most accurate implementations of AutoARIMA , AutoETS , AutoCES , MSTL and Theta in Python. |
| 3 | Nixtla | https://github.com/nixtla | Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, ... |
| 4 | Forecasting Models - Nixtla | https://nixtlaverse.nixtla.io/neuralforecast/docs/capabilities/overview.html | NeuralForecast offers most models also in an Auto* version, The optimization methods include grid search, random search, and Bayesian optimization. |
| 5 | Models - Nixtla | https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html | StatsForecast offers a wide variety of statistical forecasting models. Useful for large collections of univariate time series. Includes: AutoARIMA, AutoETS, ... |
| 6 | Introduction - TimeGPT Foundational model for time series ... | https://www.nixtla.io/docs/introduction/introduction | Nixtla Engine? TimeGPT is the first foundation model for time series, providing state-of-the-art forecasting and anomaly detection capabilities |
| 7 | Nixtla Predicts 1 Million+ Time Series in 30 Minutes With Ray | https://www.anyscale.com/blog/how-nixtla-uses-ray-to-accurately-predict-more-than-a-million-time-series | Nixtla is an open-source time-series startup that seeks to democratize the use of state-of-the-art models in the field. |
| 8 | Blog | Nixtla | Time Series Forecasting | https://www.nixtla.io/blog | Expert guides, tutorials, and insights on time series forecasting, anomaly detection, and predictive analytics. Learn from Nixtla's research and practical ... |
| 9 | Nixtla/nixtla: TimeGPT-2.1: production ready pre-trained ... | https://github.com/Nixtla/nixtla | TimeGPT is user-friendly and low-code, enabling users to upload their time series data and either generate forecasts or detect anomalies with just a single line ... |
| 10 | Hierarchical Forecast | https://nixtlaverse.nixtla.io/hierarchicalforecast/index.html | Probabilistic hierarchical forecasting with statistical and econometric methods. |
Important pages
Pages earning the most estimated search traffic in the sample.
Website intelligence — Collected evidence
Prioritized website intelligence pages
| Type | Page | Words | Change | URL |
|---|---|---|---|---|
| other | Nixtla \ | static | 2,036 | new |
| pricing | TimeGPT Subscription Plans - TimeGPT Foundational model for time series forecasting and anomaly detection | 532 | new | https://www.nixtla.io/docs/introduction/timegpt_subscription_plans#usage-options |
| pricing | Forecasting Bitcoin Prices - TimeGPT Foundational model for time series forecasting and anomaly detection | 1,306 | new | https://www.nixtla.io/docs/use_cases/bitcoin_price_prediction |
| pricing | What-If Forecasting: Price Effects in Retail - TimeGPT Foundational model for time series forecasting and anomaly detection | 2,337 | new | https://www.nixtla.io/docs/use_cases/what_if_forecasting_price_effects_in_retail |
| features | MLforecast Feature Engineering with Lags, Rolling Stats, and Dates | 2,849 | new | https://www.nixtla.io/blog/automated-time-series-feature-engineering-with-mlforecast |
| features | Categorical Exogenous Variables in NeuralForecast | 2,107 | new | https://www.nixtla.io/blog/neuralforecast-exogenous-features |
| features | Date Features - TimeGPT Foundational model for time series forecasting and anomaly detection | 530 | new | https://www.nixtla.io/docs/reference/date_features |
| integrations | Introduction - TimeGPT Foundational model for time series forecasting and anomaly detection | 331 | new | https://www.nixtla.io/docs |
| integrations | TimeGPT on Microsoft Foundry via Azure Marketplace | 677 | new | https://www.nixtla.io/blog/timegpt-azure-marketplace |
| integrations | TimeGPT in Snowflake: Anomaly Detection & Explainability | 868 | new | https://www.nixtla.io/blog/timegpt-snowflake-integration |
| integrations | Key Concepts - TimeGPT Foundational model for time series forecasting and anomaly detection | 665 | new | https://www.nixtla.io/docs/about/key-concepts |
| integrations | Privacy Notice - TimeGPT Foundational model for time series forecasting and anomaly detection | 1,704 | new | https://www.nixtla.io/docs/about/privacy-notice |
| integrations | Nixtla - TimeGPT Foundational model for time series forecasting and anomaly detection | 583 | new | https://www.nixtla.io/docs/about/sub-categoria |
| integrations | Terms and Conditions - TimeGPT Foundational model for time series forecasting and anomaly detection | 3,036 | new | https://www.nixtla.io/docs/about/terms-and-conditions |
Features
| Page type | Evidence | Source |
|---|---|---|
| features | [Skip to main content](#main-content) | https://www.nixtla.io/blog/automated-time-series-feature-engineering-with-mlforecast |
| features | Basic product analytics are active to help improve Nixtla. You can turn them off now. Advertising, identity tracking, and session replay remain off unless you allow all cookies. See our Privacy Policy for details. | https://www.nixtla.io/blog/automated-time-series-feature-engineering-with-mlforecast |
| features | Reject analyticsAllow all cookies | https://www.nixtla.io/blog/automated-time-series-feature-engineering-with-mlforecast |
| features | Missed What's New in the Nixtla Engine? Watch the recording | https://www.nixtla.io/blog/automated-time-series-feature-engineering-with-mlforecast |
| features | Automated Time Series Feature Engineering with MLforecast | https://www.nixtla.io/blog/automated-time-series-feature-engineering-with-mlforecast |
| features | Replace hours of custom feature engineering code with MLforecast's automated lag features, rolling statistics, and target transformations for faster, more reliable time series forecasting. | https://www.nixtla.io/blog/automated-time-series-feature-engineering-with-mlforecast |
| features | Aug 26, 2025 • 15 min read | https://www.nixtla.io/blog/automated-time-series-feature-engineering-with-mlforecast |
| features | Developer Advocate - Nixtla | https://www.nixtla.io/blog/automated-time-series-feature-engineering-with-mlforecast |
| features | Building effective time series models requires creating dozens of engineered features: lag values from previous periods, rolling averages over different windows, seasonal indicators, and target transformations. The traditional approach involves writing hundreds of lines of custom pandas, external link code, handling edge cases manually, and maintaining separate pipelines for each use case. | https://www.nixtla.io/blog/automated-time-series-feature-engineering-with-mlforecast |
| features | This manual process creates several problems: | https://www.nixtla.io/blog/automated-time-series-feature-engineering-with-mlforecast |
| features | Time consuming: 1-2 hours per model just for feature creation | https://www.nixtla.io/blog/automated-time-series-feature-engineering-with-mlforecast |
| features | Error prone: Missing values, incorrect window calculations, transformation reversals | https://www.nixtla.io/blog/automated-time-series-feature-engineering-with-mlforecast |
| features | Inconsistent: Different feature implementations across models and teams | https://www.nixtla.io/blog/automated-time-series-feature-engineering-with-mlforecast |
| features | Maintenance heavy: Breaking when data schemas or patterns change | https://www.nixtla.io/blog/automated-time-series-feature-engineering-with-mlforecast |
| features | MLforecast eliminates this pain with automated feature engineering that's faster, more reliable, and consistent across all your forecasting models. | https://www.nixtla.io/blog/automated-time-series-feature-engineering-with-mlforecast |
| features | > Before diving into automated feature engineering, establishing baseline model performance provides essential context for measuring improvement and avoiding over-engineering. | https://www.nixtla.io/blog/automated-time-series-feature-engineering-with-mlforecast |
| features | > The source code of this article can be found in the interactive Jupyter notebook, external link. | https://www.nixtla.io/blog/automated-time-series-feature-engineering-with-mlforecast |
| features | Introduction to MLforecast | https://www.nixtla.io/blog/automated-time-series-feature-engineering-with-mlforecast |
| features | MLforecast is Nixtla's machine learning forecasting library that handles the complete time series modeling pipeline: | https://www.nixtla.io/blog/automated-time-series-feature-engineering-with-mlforecast |
| features | Automated feature engineering: Lag features, rolling statistics, date features | https://www.nixtla.io/blog/automated-time-series-feature-engineering-with-mlforecast |
Pricing and plans
| Page type | Evidence | Source |
|---|---|---|
| pricing | Overview of TimeGPT’s Enterprise subscription plans with deployment options, support, and trial details. | https://www.nixtla.io/docs/introduction/timegpt_subscription_plans#usage-options |
| pricing | If you’d like to explore custom plan options—for instance, adjusting the number of API calls, user limits, or support level—reach out to us at support@nixtla.io. | https://www.nixtla.io/docs/introduction/timegpt_subscription_plans#usage-options |
| pricing | Free Trial Available!\\When you **create your account**, you receive a 30-day free trial with no credit card required. Your access expires after 30 days unless you upgrade to a paid plan. If you need more time to evaluate or want to continue using TimeGPT, please contact us for flexible plan options. | https://www.nixtla.io/docs/introduction/timegpt_subscription_plans#usage-options |
| pricing | Visit our dashboard and sign up for a new account. | https://www.nixtla.io/docs/introduction/timegpt_subscription_plans#usage-options |
| pricing | Before the trial ends, decide if you’d like to upgrade to an Enterprise plan or contact us for a custom plan. | https://www.nixtla.io/docs/introduction/timegpt_subscription_plans#usage-options |
| pricing | Pricing and Billing Information\\Additional pricing details and frequently asked questions can be found on our [FAQ page](introduction/faq). | https://www.nixtla.io/docs/introduction/timegpt_subscription_plans#usage-options |
| pricing | Master what-if forecasting with TimeGPT for retail pricing optimization. Learn scenario analysis to predict demand changes from price adjustments using the M5 dataset. Step-by-step Python tutorial. | https://www.nixtla.io/docs/use_cases/what_if_forecasting_price_effects_in_retail |
| pricing | Pricing decisions significantly impact retail demand. TimeGPT makes it possible to forecast product demand while incorporating price as a key factor, enabling retailers to explore how the forecast changes under different pricing assumptions. This tutorial demonstrates how to use TimeGPT for scenario analysis by forecasting demand under various pricing conditions. You’ll learn to incorporate price data into forecasts and compare forecasts across different price assumptions. | https://www.nixtla.io/docs/use_cases/what_if_forecasting_price_effects_in_retail |
| pricing | A scenario forecast is not a price-effect estimate. Changing a future price and re-forecasting shows the model’s conditional forecast given that price: a relationship learned from how price and demand moved together historically. In retail history, prices are rarely changed at random: markdowns respond to slow sales, and promotions are timed to seasons. The learned relationship therefore mixes the true effect of price with the pricing policy that set it, so the difference between two scenario forecasts can overstate or understate the real demand response to a price change. This is most pronounced for price moves outside the historically observed range.Use these scenarios to explore and stress-test assumptions, not to read off price elasticities. Before committing a pricing decision, validate the implied demand response with a price experiment or a holdout test. | https://www.nixtla.io/docs/use_cases/what_if_forecasting_price_effects_in_retail |
| pricing | How to run what-if scenarios with different pricing strategies | https://www.nixtla.io/docs/use_cases/what_if_forecasting_price_effects_in_retail |
| pricing | ](#how-to-forecast-sales-with-pricing-scenarios) | https://www.nixtla.io/docs/use_cases/what_if_forecasting_price_effects_in_retail |
| pricing | How to Forecast Sales with Pricing Scenarios | https://www.nixtla.io/docs/use_cases/what_if_forecasting_price_effects_in_retail |
| pricing | Let’s investigate how the demand, our target `y`, of these products has evolved in the last year of data. | https://www.nixtla.io/docs/use_cases/what_if_forecasting_price_effects_in_retail |
| pricing | Historical pricing trends showing price changes over 5 years for retail products | https://www.nixtla.io/docs/use_cases/what_if_forecasting_price_effects_in_retail |
| pricing | In this run, forecast demand increases when we reduce the price and decreases when we increase it: the direction most retailers would expect. Keep in mind that this reflects the association the model learned from this product’s pricing history; as cautioned [above](#introduction), the size of the gap between the scenarios, and for some products even its direction, should not be read as the causal effect of the price change. Finally, let’s plot the forecasts for our different pricing scenarios, showing how TimeGPT forecasts a different demand when the price of a set of products is changed. | https://www.nixtla.io/docs/use_cases/what_if_forecasting_price_effects_in_retail |
| pricing | What-if scenario comparison showing demand forecasts under different pricing strategies | https://www.nixtla.io/docs/use_cases/what_if_forecasting_price_effects_in_retail |
| pricing | What-if forecasting with TimeGPT supports pricing discussions by: | https://www.nixtla.io/docs/use_cases/what_if_forecasting_price_effects_in_retail |
| pricing | Comparing multiple pricing scenarios simultaneously | https://www.nixtla.io/docs/use_cases/what_if_forecasting_price_effects_in_retail |
| pricing | Scenario forecasts are a starting point for a pricing conversation, not a substitute for measuring price effects: the differences between scenarios reflect learned associations, and their magnitude and direction can differ from the true demand response. Validate promising scenarios with price experiments or holdout tests before acting on them. | https://www.nixtla.io/docs/use_cases/what_if_forecasting_price_effects_in_retail |
Integrations and supported platforms
| Page type | Evidence | Source |
|---|---|---|
| integrations | > ## Documentation Index | https://www.nixtla.io/docs |
| integrations | > Fetch the complete documentation index at: /docs/llms.txt | https://www.nixtla.io/docs |
| integrations | > Use this file to discover all available pages before exploring further. | https://www.nixtla.io/docs |
| integrations | [Skip to main content](#content-area) | https://www.nixtla.io/docs |
| integrations | Missed What's New in the Nixtla Engine? Watch the recording | https://www.nixtla.io/docs |
| integrations | TimeGPT Foundational model for time series forecasting and anomaly detection home pagelight logodark logo | https://www.nixtla.io/docs |
| integrations | Ctrl KAsk AssistantCtrl+I | https://www.nixtla.io/docs |
| integrations | INTRODUCTION | https://www.nixtla.io/docs |
| integrations | Copy pageCopy page | https://www.nixtla.io/docs |
| integrations | Welcome to TimeGPT - The foundational model for time series forecasting and anomaly detection | https://www.nixtla.io/docs |
| integrations | ](#power-your-time-series-analysis-with-timegpt) | https://www.nixtla.io/docs |
| integrations | Power your time series analysis with TimeGPT | https://www.nixtla.io/docs |
| integrations | TimeGPT is the first foundation model for time series, providing state-of-the-art forecasting and anomaly detection capabilities to help you make better decisions with your time series data. | https://www.nixtla.io/docs |
| integrations | Quick Start Guide | https://www.nixtla.io/docs |
| integrations | Get started with TimeGPT in minutes with our simple Python interface | https://www.nixtla.io/docs |
| integrations | ](/docs/forecasting/timegpt_quickstart) | https://www.nixtla.io/docs |
| integrations | Set Up Your API Key | https://www.nixtla.io/docs |
| integrations | Set up your environment to start using TimeGPT right away | https://www.nixtla.io/docs |
| integrations | ](/docs/setup/setting_up_your_api_key) | https://www.nixtla.io/docs |
| integrations | ](#core-capabilities) | https://www.nixtla.io/docs |
Product and company updates
No supporting public-page evidence was collected for this category.
Rank tracking
Persistent keyword baselines and position movement.
This analysis created the first position baseline at 2026-10-22T21:59:55.917Z. Movement appears after the next fresh analysis using the same location and language.
Features
Features and capabilities
Core functions and evidence-backed product capabilities.
| Capability | What it does |
|---|---|
| Forecasting | Generates accurate predictions for time series data using TimeGPT S6. |
| Anomaly detection | Automatically detects anomalies in time series data, as shown in Bitcoin price analysis S14. |
| Exogenous variables | Incorporates numeric, categorical, and date/time features to improve forecast accuracy S36 S29 S32. |
| What-if scenario analysis | Simulates different pricing strategies to compare demand forecasts S19. |
| Feature importance | Computes model-agnostic feature importance weights for exogenous features via the /v2/explain endpoint S23. |
| Real-time monitoring | Supports real-time monitoring of time series data S6. |
Integrations and ecosystem
Connected products, exports, APIs, and workflow compatibility.
| Integration or interface | Type | What it enables |
|---|---|---|
| NixtlaClient SDK | SDK | Provides a Python interface to initialize and call TimeGPT for forecasting S14. |
| REST API | API | Allows direct calls to TimeGPT endpoints, including /v2/explain for feature importance S23. |
| Azure deployment | Integration | Enables hosting TimeGPT on Azure, managed by Nixtla S11. |
| Self-hosted deployment | Integration | Allows running TimeGPT on your own infrastructure via Docker or Python wheel S11. |
Pricing
Pricing and plans
Published plans, free access, trials, credits, and pricing limitations.
| Plan or offer | Verified price and billing | Included | Limits |
|---|---|---|---|
| Enterprise API | Contact sales S11 | API access, scalable calls S11 | API call limits tailored S11 |
| Enterprise on Azure | Contact sales S11 | Hosted on Azure, managed by Nixtla S11 | Not established |
| Enterprise self-hosted | Contact sales S11 | Docker image or Python wheel on your infrastructure S11 | Not established |
| Free trial | 30-day free trial, no credit card required S11 | Access to TimeGPT features S11 | Expires after 30 days unless upgraded S11 |
Competition
Competitive landscape
Domains competing for the same organic search demand.
Sampled competitors
| Competitor | First-page appearances | Sampled queries | Average position | Queries observed |
|---|---|---|---|---|
| nixtlaverse.nixtla.io | 3 | 1 | 5 | nixtla |
| youtube.com | 2 | 1 | 3.5 | nixtla |
| nixtla.github.io | 2 | 2 | 5.5 | nixtla, timegpt |
| aihorizonforecast.substack.com | 1 | 1 | 1 | timegpt |
| ai.azure.com | 1 | 1 | 3 | timegpt |
| sciencedirect.com | 1 | 1 | 5 | timegpt |
| medium.com | 1 | 1 | 7 | nixtla |
| x.com | 1 | 1 | 9 | nixtla |
| github.com | 1 | 1 | 9 | timegpt |
Website intelligence — Collected evidence
Customers, testimonials, and first-party review claims
No supporting public-page evidence was collected for this category.
Independent review sources discovered
| Platform | Status | Discovered source |
|---|---|---|
| discovered_not_scraped | https://colab.research.google.com/github/Nixtla/nixtla/blob/main/nbs/docs/use-cases/2_bitcoin_price_prediction.ipynb | |
| discovered_not_scraped | https://colab.research.google.com/github/Nixtla/nixtla/blob/main/nbs/docs/use-cases/5_what_if_pricing_scenarios_in_retail.ipynb | |
| G2 | discovered_not_scraped | https://www.g2.com/products/nixtla/reviews?utm_source=rewards-medal |
Backlink profile
Authority and referring-domain signals.
Backlink comparison profile
- Domain rating: 64/100
- Backlinks: 17,414
- Referring domains: 1,783
- Dofollow backlinks: 79.0%
- Dofollow referring domains: 54.0%
| Source DR | Source title | Anchor | Source URL | Target URL |
|---|---|---|---|---|
| 78 | Time-Series Forecasting \ | Tiger Data | Nixtla TimeGPT | https://www.tigerdata.com/blog/what-is-time-series-forecasting |
| 45 | Probabilistic Time Series Analysis: Opportunities and Applications — PyMC Labs Blog | AutoETS | https://www.pymc-labs.com/blog-posts/probabilistic-forecasting | https://nixtlaverse.nixtla.io/statsforecast/docs/models/autoets.html |
| 57 | ZaDark – Zalo Dark Mode \ | ZaDark by Quaric | — | https://zadark.com/ |
| 97 | GitHub - Nixtla/nixtla: TimeGPT-1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, electricity, f | www.nixtla.io/docs | https://github.com/Nixtla/nixtla | https://www.nixtla.io/docs/introduction/introduction |
| 89 | An Introduction to Time Series Forecasting with Generative AI \ | Databricks Blog | TimeGPT | https://www.databricks.com/blog/introduction-time-series-forecasting-generative-ai |
| 71 | Portfolio \ | True Ventures | NixtlaNixtla builds AI software for time series forecasting and anomaly detection.Infrastructure Software | https://www.trueventures.com/portfolio |
| 39 | GreatPoint Ventures Companies | Nixtla | https://www.gpv.com/companies | https://www.nixtla.io/ |
| 36 | GreatPoint Ventures - VC Fund Breakdown | nixtla.io | https://www.vcsheet.com/fund/greatpoint-ventures | https://nixtla.io/ |
| 74 | 9 ARIMA models – Forecasting: Principles and Practice, the Pythonic Way | model | https://otexts.com/fpppy/09-arima.html | https://nixtlaverse.nixtla.io/statsforecast/docs/models/arima.html |
| 85 | Databricks AutoML 101: Automating ML model development (2026) | DeepAR | https://www.flexera.com/blog/finops/databricks-automl/ | https://nixtlaverse.nixtla.io/neuralforecast/models.deepar.html |
Collection decision
The Scout search index remains intentionally excluded from automatic Scout analysis. This Scout traffic model profile costs 5 credits (0.5 cents) and is included in the first comparison run so its usefulness can be judged before the recurring collection is finalized.
AI visibility
Measured ChatGPT and Google AI mentions with cited sources.
AI-assistant referral traffic
- Estimated current generative-AI referral share: 1.8%
Scout traffic model did not return a separate AI-referral trend series for this domain.
Website intelligence — Collected evidence
Site-stated AI capabilities
| Page type | Evidence | Source |
|---|---|---|
| other | Enterprise-grade time series forecasting and anomaly detection. Accurate predictions powered by Nixtla's industry-leading AI solutions. | https://www.nixtla.io/ |
| features | We'll use LightGBM, external link for our machine learning models throughout this tutorial. | https://www.nixtla.io/blog/automated-time-series-feature-engineering-with-mlforecast |
| features | MLforecast is Nixtla's machine learning forecasting library that handles the complete time series modeling pipeline: | https://www.nixtla.io/blog/automated-time-series-feature-engineering-with-mlforecast |
| features | We'll use LightGBM, external link for our machine learning models throughout this tutorial. | https://www.nixtla.io/blog/automated-time-series-feature-engineering-with-mlforecast |
| features | Applied AI Scientist, Core maintainer of NeuralForecast | https://www.nixtla.io/blog/neuralforecast-exogenous-features |
| integrations | Getting a new AI vendor approved inside a large organization is often slower than building the integration itself. Procurement, security review, a new contract to negotiate, all before a single API call gets made. TimeGPT is now available through Microsoft Foundry, external link, which changes that path for Azure customers: deploy it inside your own Azure subscription, get billed as standard Azure consumption, and skip the separate vendor review since Microsoft has already vetted it as part of listing the model. For practitioners, nothing about the API changes. You're using the same TimeGPT you already know; it's just faster to get approved and deployed. | https://www.nixtla.io/blog/timegpt-azure-marketplace |
| integrations | Open the Microsoft Foundry model catalog, external link and search "timegpt". | https://www.nixtla.io/blog/timegpt-azure-marketplace |
These are first-party product claims, not independent measurements of AI-search visibility.
Marketing opportunities
Actions calculated directly from the collected evidence.
| Opportunity | Recommended action | Evidence |
|---|---|---|
| Competitor gap | Review the sampled queries where nixtlaverse.nixtla.io appears. | 3 first-page appearances across nixtla |
Website intelligence — Collected evidence
Website evidence gaps
- customers: no usable public page was collected; verify whether the site publishes this evidence.
- reviews: no usable public page was collected; verify whether the site publishes this evidence.
- changes: no usable public page was collected; verify whether the site publishes this evidence.
- identity: no usable public page was collected; verify whether the site publishes this evidence.
- technical: no usable public page was collected; verify whether the site publishes this evidence.
Trust and proof
Customers, proof, and reviews
Published proof, customer signals, and review evidence.
Independent reviews
| Platform | Page | How it was found | Note |
|---|---|---|---|
| Gartner Peer Insights | Nixtla Reviews & Ratings 2026 / Gartner Peer Insights | Grounded web search | - Data and Analytics Manager 50M-1B USD SoftwareReview Source #### Efficient forecasting and ease of use, but limited native explainability challenges4.0Sep 11, 2026 We Implemented the forecasting functionality ecosystem offered by Nixtla by leveraging statsforecast and TimeGPT via Python in order to model order volumes & demand patterns across various different operational funnels. Certain things that worked well for us included the computational throughput and the ease of embedding into our ex |
| Capterra | Nixtla Software Pricing, Alternatives & More 2026 / Capterra | Grounded web search | an AI time-series forecasting software that modifies access and generates advanced predictive insights ... has an open-source ecosystem with tools for feature extraction and hierarchical forecasting. The platform can also be fine-tuned on a user's own |
Customer proof
| Proof type | Named customer or source | Evidence |
|---|---|---|
| Testimonial | Anindya Saha | testimonial — "Since integrating Nixtla's forecasting engine, our ML monitoring has transformed. (22 Nixtla Customer Reviews & References / FeaturedCustomers) |
| Case study | TradeSmith | customer case study — # T r a d e S m i t h TradeSmith integrates Nixtla’s TimeGPT to forecast 22,000+ financial time series daily, delivering half-a-million forecasts per month with minimal tuning. (AI Investment Forecasting / TradeSmith Case Study) |
Independent mentions
- TimeGPT: Production Ready Time Series Foundation Model for Forecasting / Hacker News — The website (https://www.nixtla.io/) says all sorts of companies are using this: Ford, Lyft, Nestle, T-Mobile, Wayfair, etc etc. That's not actually true, right? Surely that's a lie? I find it very unlikely that these huge companies are actually using this, especially considering how sketchy the website is (the About page doesn't list any employees/executives, there are no case studies, no white papers describing what this product even is.) I get the impression this product only exists to scam i
- Success Stories / Nixtla / Time Series Forecasting — # C u s t o m e r S u c c e s s S t o r i e s ... 60 regions worldwide, Microsoft supports a diverse client base that includes 95% of Fortune 500 companies, government bodies, and innovative startups. 85% Less False Positives 10x Improvement in Latency
- The website (https://www.nixtla.io/) says all sorts of companies are using this:... / Hacker News — That's not actually true, right? Surely that's a lie? I find it very unlikely that these huge companies are actually using this, especially considering how sketchy the website is (the About page doesn't list any employees/
- Prudential: AI Financial Forecasting at Scale / Nixtla — # P r u d e n t i a l F i n a n c i a l, I n c ... Prudential integrates Nixtla’s TimeGPT to predict thousands of quarterly financial metrics, without retraining, empowering strategic planning across its global business.
- Skulicity Cuts Forecast Runtime By Over 50% With TimeGPT — With Nixtla's TimeGPT, Skulicity transformed a compute-heavy forecasting pipeline into a scalable, production-ready system, enabling faster replenishment decisions across dynamic retail supply chains without replatforming its Microsoft Fabric environment
- Intergrid Cuts Forecast Deployment Time by 80% With TimeGPT — With Nixtla's TimeGPT, Intergrid rapidly scales reliable forecasting across new power markets while giving its analytics team the capacity to focus on optimization and ... Reduced forecast deployment time from multiple days of manual setup to under an
External links were grounded in one live web search. Search snippets are leads, not independently scraped page content; rejected links remain available in Review evidence.
Company, security, and trust
Ownership, policies, security signals, and public trust evidence.
| Signal | Finding | Source |
|---|---|---|
| Company and ownership | Founded in 1875, Prudential offers life insurance, annuities, retirement planning, group insurance, and institutional investment management via its PGIM division. | source |
| Contact and support | High-level support options (email, chat, phone, or dedicated support) | source |
| Legal and privacy | Basic product analytics are active to help improve Nixtla. You can turn them off now. Advertising, identity tracking, and session replay remain off unless you allow all cookies. See our Privacy Policy for details. | source |
| Security and compliance | Your data never leaves your own infrastructure. Zero data egress. Zero data retention. SOC 2 Type II. | source |
| Uptime and status page | Not published on the collected pages. | — |
Not found on the collected pages: uptime and status page. This only means Scout did not find it on the pages it read; it says nothing about the company itself.
Documentation and product updates
Help resources, product guidance, and visible changes over time.
| Resource or update | Type | What is available | Date or freshness |
|---|---|---|---|
| Introduction - TimeGPT Foundational model for time series forecasting and anomaly detection | Documentation | Welcome to TimeGPT - The foundational model for time series forecasting and anomaly detection (source) | Not dated |
| TimeGPT Subscription Plans - TimeGPT Foundational model for time series forecasting and anomaly detection | Documentation | Overview of TimeGPT's Enterprise subscription plans with deployment options, support, and trial details. (source) | Not dated |
| Forecasting Bitcoin Prices - TimeGPT Foundational model for time series forecasting and anomaly detection | Documentation | Master Bitcoin price forecasting with TimeGPT. Complete Python tutorial covering cryptocurrency prediction, anomaly detection, uncertainty quantification, and risk management strategies. (source) | Not dated |
| What-If Forecasting: Price Effects in Retail - TimeGPT Foundational model for time series forecasting and anomaly detection | Documentation | Master what-if forecasting with TimeGPT for retail pricing optimization. Learn scenario analysis to predict demand changes from price adjustments using the M5 dataset. Step-by-step Python tutorial. (source) | Not dated |
| Compute model-agnostic feature importance weights - TimeGPT Foundational model for time series forecasting and anomaly detection | Documentation | Compute model-agnostic feature importance weights for the provided exogenous features. It takes a JSON as an input containing the historical data with exogenous features and the attribution method to use. No foundation model is involved. The response contains one normalized weight per feature. (source) | Not dated |
| Date Features - TimeGPT Foundational model for time series forecasting and anomaly detection | Documentation | Use holidays flags and special dates to improve your accuracy (source) | Not dated |
| Categorical Variables - TimeGPT Foundational model for time series forecasting and anomaly detection | Documentation | Learn how to incorporate external categorical variables in your TimeGPT forecasts to improve accuracy. (source) | Not dated |
| Date/Time Features - TimeGPT Foundational model for time series forecasting and anomaly detection | Documentation | Learn how to incorporate date/time features into your forecasts to improve performance. (source) | Not dated |
Not found in the collected public sources: public roadmap, changelog or release notes, status page, separate help center or knowledge base.
Technical
Technical and delivery signals
Delivery model, platforms, API, data ownership, reliability claims and release activity.
| Signal | Finding | Source |
|---|---|---|
| Delivery model (cloud, self-hosted, desktop, browser extension) | Available as cloud deployment on Azure or self-hosted on your infrastructure S11. | S11 |
| Platforms and apps | Accessible via web dashboard and Python SDK S11 S14. | S11 S14 |
| API and extensibility (API, SDK, webhooks, MCP, plugins) | Provides REST API and SDKs for integration S23 S6. | S23 S6 |
| Data ownership and export | Not published on the collected pages. | — |
| Performance and reliability claims | Recognized by IDC as leading time series ecosystem for reliability and scalability S1. | S1 |
| Release activity | Not published on the collected pages. | — |
Measured infrastructure
Domain, certificate, and hosting signals, measured directly.
| Signal | Finding |
|---|---|
| First archived | 2022-10-24 — web archive |
| TLS certificate | Let's Encrypt, valid, expires 2026-12-05 |
| Security headers | HSTS, CSP, X-Content-Type-Options, X-Frame-Options, Referrer-Policy |
| Server header | Vercel |
| Mail handled by | aspmx.l.google.com, alt1.aspmx.l.google.com |
| Nameservers | ns-cloud-d3.googledomains.com, ns-cloud-d1.googledomains.com |
Not measurable: rdap: no RDAP server published for .io. A gap here means the lookup was unavailable, not that the finding is negative.
Technical site audit
A bounded multi-page crawl with page-level issues and URLs.
Scout — Bounded technical crawl
- Pages crawled: 15 of 15 maximum
- Technical health: 97/100
- Critical issues: 0
- Warnings: 6
- Notices: 7
Issues
| Severity | Issue | Value | Affected URL |
|---|---|---|---|
| notice | Meta description length should be reviewed | 311 | https://www.nixtla.io/company |
| notice | Low visible word count | 148 | https://www.nixtla.io/book-a-free-trial |
| warning | Title length should be reviewed | 91 | https://www.nixtla.io/docs |
| notice | Meta description length should be reviewed | 184 | https://www.nixtla.io/blog |
| warning | Title length should be reviewed | 68 | https://www.nixtla.io/success-stories/imss |
| notice | Meta description length should be reviewed | 212 | https://www.nixtla.io/success-stories/imss |
| notice | Meta description length should be reviewed | 178 | https://www.nixtla.io/success-stories/prudential |
| notice | Meta description length should be reviewed | 260 | https://www.nixtla.io/success-stories/bullish |
| notice | Meta description length should be reviewed | 193 | https://www.nixtla.io/success-stories/skulicity |
| warning | Duplicate title in audit sample | 2 | https://www.nixtla.io/ |
| warning | Duplicate title in audit sample | 2 | https://www.nixtla.io/ |
| warning | Duplicate meta description in audit sample | 2 | https://www.nixtla.io/ |
| warning | Duplicate meta description in audit sample | 2 | https://www.nixtla.io/ |
Crawled pages
| Status | Title | H1s | Words | Response | URL |
|---|---|---|---|---|---|
| 200 | Nixtla \ | Time Series Forecasting & Anomaly Detection | 1 | 2305 | 131 ms |
| 200 | Nixtla \ | Time Series Forecasting & Anomaly Detection | 1 | 2305 | 125 ms |
| 200 | Success Stories \ | Nixtla \ | Time Series Forecasting | 1 | 772 |
| 200 | Company \ | Nixtla \ | Time Series Forecasting | 1 | 495 |
| 200 | Book a Free Trial \ | Nixtla \ | Time Series Forecasting | 1 | 148 |
| 200 | Introduction - TimeGPT Foundational model for time series forecasting and anomaly detection | 1 | 454 | 194 ms | https://www.nixtla.io/docs |
| 200 | Industries & Use Cases \ | Nixtla \ | Time Series Forecasting | 1 | 181 |
| 200 | Blog \ | Nixtla \ | Time Series Forecasting | 1 | 1182 |
| 200 | Anomaly detection at scale for Lyft | 1 | 632 | 357 ms | https://www.nixtla.io/success-stories/lyft |
| 200 | Microsoft: Empowering Effortless AI Deployment | 1 | 474 | 313 ms | https://www.nixtla.io/success-stories/microsoft |
| 200 | IMSS Scales Nationwide Demand Forecasting for Medicines With TimeGPT | 1 | 661 | 352 ms | https://www.nixtla.io/success-stories/imss |
| 200 | Prudential: AI Financial Forecasting at Scale \ | Nixtla | 1 | 511 | 306 ms |
| 200 | AI Market Intelligence: Bullish & CoinDesk Case Study | 1 | 548 | 320 ms | https://www.nixtla.io/success-stories/bullish |
| 200 | Re:Drink Forecasting beverage refills | 1 | 540 | 288 ms | https://www.nixtla.io/success-stories/redrink |
| 200 | Skulicity Cuts Forecast Runtime By Over 50% With TimeGPT | 1 | 733 | 330 ms | https://www.nixtla.io/success-stories/skulicity |
This is a bounded Scout crawl. Core Web Vitals and Lighthouse scores are not claimed unless a dedicated performance audit is connected.
Search Console and Analytics
First-party search and behavioral data connection state.
Modeled acquisition channels
| Channel | Estimated share |
|---|---|
| direct | 37.9% |
| search | 39.5% |
| search organic | 38.7% |
| search paid | 0.8% |
| social | 3.4% |
| social organic | 3.1% |
| social paid | 0.3% |
| referrals | 13.7% |
| paid referrals | 1.4% |
| 1.5% | |
| gen ai | 1.8% |
| affiliate | 0.8% |
| other | 0.0% |
First-party connections
| Integration | Status | Data available after connection |
|---|---|---|
| Google Search Console | Not connected | Clicks, impressions, CTR, average position, queries, pages, countries, and devices |
| Google Analytics | Not connected | Organic sessions, engagement, landing pages, conversions, geography, and devices |
No first-party Search Console or Analytics numbers are shown until the site owner authorizes the corresponding Google account. Scout traffic model values above are modeled estimates, not private analytics totals.
Limits and method
Limitations and open questions
Known constraints and missing evidence from the collected pages.
The collected pages do not mention any competitors by name. Evidence gaps include lack of detailed pricing, security certifications, uptime statistics, and release history. Open questions remain about data ownership and export capabilities.
Sources, method, and limitations
Evidence inventory, collection method, provider use, and constraints.
This report is based on 15 collected pages from nixtla.io, including company pages, documentation, and tutorials. Static and browser rendering providers completed successfully; external proof search was used for verification. Limitations: pricing details are limited to contact-sales plans, and customer evidence is limited to company-published claims.
- Official Websitehttps://www.nixtla.io/Verified
- Official Websitehttps://www.nixtla.io/companyVerified
- Documentationhttps://www.nixtla.io/docsVerified
- Pricing Pagehttps://www.nixtla.io/docs/introduction/timegpt_subscription_plansVerified
- Pricing Pagehttps://www.nixtla.io/docs/use_cases/bitcoin_price_predictionVerified
- Pricing Pagehttps://www.nixtla.io/docs/use_cases/what_if_forecasting_price_effects_in_retailVerified
- Official Websitehttps://www.nixtla.io/blog/automated-time-series-feature-engineering-with-mlforecastVerified
- Official Websitehttps://www.nixtla.io/blog/neuralforecast-exogenous-featuresVerified
- Official Websitehttps://www.nixtla.io/docs/api-reference/compute-model-agnostic-feature-importance-weightsVerified
- Official Websitehttps://www.nixtla.io/docs/reference/date_featuresVerified
- Official Websitehttps://www.nixtla.io/docs/forecasting/exogenous-variables/categorical_featuresVerified
- Official Websitehttps://www.nixtla.io/docs/forecasting/exogenous-variables/date_featuresVerified
- Official Websitehttps://www.nixtla.io/docs/forecasting/exogenous-variables/numeric_featuresVerified
- Documentationhttps://www.nixtla.io/blog/timegpt-azure-marketplaceVerified
- Documentationhttps://www.nixtla.io/blog/timegpt-snowflake-integrationVerified
- Articlehttps://news.ycombinator.com/item?id=40301592Verified
- Review Sitehttps://www.gartner.com/reviews/product/nixtla-916571571Verified
- Articlehttps://www.featuredcustomers.com/vendor/nixtlaVerified
- Articlehttps://www.nixtla.io/success-stories/tradesmithVerified
- Articlehttps://www.nixtla.io/success-storiesVerified
- Articlehttps://news.ycombinator.com/item?id=40309784Verified
- Review Sitehttps://www.capterra.com/p/10028915/TimeGPT/Verified
- Articlehttps://www.nixtla.io/success-stories/prudentialVerified
- Articlehttps://www.nixtla.io/success-stories/skulicityVerified
- Articlehttps://www.nixtla.io/success-stories/intergridVerified