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What is Tokenization of Real-World Assets? The $30 Trillion Revolution Explained

Explore the transformative concept of real-world asset tokenization, its rapid growth, major asset classes involved, and the role of Token Metrics in analysis and investment.
Token Metrics Team
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The financial world is undergoing a historic transformation as real-world assets migrate onto blockchain networks at an unprecedented pace. What began as experimental pilots has rapidly expanded into a $30 billion market in 2025, with projections soaring to $30 trillion by 2034. This groundbreaking shift, known as real-world asset tokenization, is fundamentally changing how investors trade, own, and manage valuable assets. Platforms like Token Metrics are at the forefront, empowering investors to capitalize on this massive evolution in the financial landscape.

Understanding RWA Tokenization

So, what is tokenization of real world assets? At its core, real-world asset tokenization is the process of creating a digital representation of ownership rights in physical and traditional financial assets by issuing digital tokens on a blockchain. These assets encompass a broad spectrum—from real estate, corporate bonds, and private credit to commodities, art, and other traditionally illiquid assets. Instead of relying on paper certificates, complex legal frameworks, and intermediaries, tokenization uses blockchain technology to simplify ownership and enable seamless trading.

Imagine a high-value asset like a $10 million real estate property. Through the tokenization process, this property can be divided into 10,000 digital tokens, each representing fractional ownership worth $1,000. Investors can purchase any number of these tokens, gaining fractional ownership in assets that were previously accessible only to wealthy individuals or large institutions. This innovation democratizes access to markets, allowing retail investors to participe alongside institutional investors in asset classes that were once out of reach.

By converting traditional assets into tokenized assets, asset owners and issuers can unlock liquidity, reduce costs, and streamline asset management. The token not only represents fractional ownership but can also encode rights such as interest payments or dividends, all managed efficiently through smart contracts on the blockchain.

The Explosive Growth of RWA Markets

The growth of the real world asset tokenization market has been nothing short of spectacular. From a modest $8.6 billion at the start of 2025, the market ballooned to over $30 billion by mid-year—an astonishing 400% increase in just three years and roughly 260% growth in the first half of 2025 alone. This rapid expansion signals that financial institutions have moved beyond pilot projects to large-scale implementation.

Looking ahead, market forecasts vary but all suggest massive growth. McKinsey predicts the market will reach $2 trillion by 2030, Boston Consulting Group estimates $16 trillion, and Standard Chartered projects up to $30 trillion by 2034. Even the most conservative projections indicate that asset tokenization will reshape the global financial markets, creating opportunities larger than many national economies.

This surge is fueled by several factors, including clearer regulatory frameworks in key jurisdictions like the United States. As regulators provide guidelines for legal compliance, institutional investors gain the confidence to participate in blockchain-based financial products. The convergence of technology, regulation, and market demand is propelling the tokenization revolution forward.

Major Asset Classes Being Tokenized

Tokenization spans a diverse array of asset classes, reflecting its broad applicability across traditional financial assets and physical assets alike. Among the most prominent:

  • Private credit dominates with 58% of the market, approximately $14 billion in tokenized value. Tokenizing private credit reduces operational costs and opens up investment opportunities in lending markets previously reserved for specialized funds and asset managers. The creation of secondary markets enhances liquidity and investor access.
  • US Treasuries hold the second-largest share at 34% ($8.2 billion). These tokenized real-world assets appeal to institutions seeking yield-bearing, blockchain-native securities with around-the-clock trading capabilities. The tokenized treasury market has seen a remarkable 539% growth between January 2024 and April 2025.
  • Real estate tokenization accounts for about 6% of the market. This includes tokenizing real estate assets such as residential buildings and commercial real estate, enabling fractional ownership and easier transfer of property interests through digital tokens.
  • Other asset classes include commodities like gold (3%), equity tokens, and carbon credits (each about 1%). This diversity highlights that tokenization technology adapts well to a broad range of traditional asset classes and tangible assets.

By converting these assets into digital assets on blockchains, investors gain access to previously inaccessible or illiquid markets, benefiting from enhanced transparency and operational efficiency.

Wall Street Embraces Blockchain

The endorsement of blockchain technology by major financial players marks a pivotal moment in the adoption of tokenized real-world assets. Industry giants such as BlackRock, Goldman Sachs, JPMorgan, Franklin Templeton, and Apollo have transitioned from pilot programs to full-scale deployment.

For example, BlackRock’s $2.9 billion BUIDL fund and Franklin Templeton’s $420 million tokenized money market fund demonstrate serious institutional capital embracing blockchain infrastructure. Larry Fink, CEO of BlackRock—the world’s largest asset manager—has publicly declared that tokenization represents the next evolution in financial markets.

When institutions managing trillions of dollars commit to real-world asset tokenization, it signals that this innovation is not speculative hype but a fundamental transformation of traditional finance and capital markets. Their involvement accelerates the development of regulatory compliance standards and fosters broader market acceptance.

Key Benefits Driving Adoption

  • Enabling fractional ownership: Tokenization breaks down high-value assets into smaller, affordable units. This lowers entry barriers, allowing retail investors to participate in markets like tokenized real estate or private equity with minimal capital.
  • Enhanced liquidity: By transforming traditionally illiquid assets into tradable tokens, tokenization facilitates quicker asset transfers. Real estate or private credit investments that once took months to exit can now be sold in minutes on secondary markets.
  • 24/7 global trading: Unlike traditional markets limited by banking hours and geographic boundaries, tokenized assets trade continuously across global markets. Investors anywhere can transact at any time, increasing market efficiency.
  • Transparency and security: Blockchain’s immutable ledger records every transaction and ownership change, offering unprecedented visibility and reducing fraud risk. This transparency builds investor confidence and simplifies asset servicing.
  • Reduced costs and operational efficiency: By consolidating functions such as distribution, clearing, settlement, and custody into blockchain-based protocols, tokenization lowers fees and counterparty risk. This streamlining benefits both asset issuers and owners.

Together, these advantages are reshaping how financial assets are issued, managed, and traded, opening new frontiers for institutional investors and retail participants alike.

As the tokenization of real world assets accelerates, investors face a complex landscape with hundreds of projects and platforms. This complexity underscores the need for sophisticated tools to analyze opportunities and manage risk effectively. Token Metrics, a leading crypto trading and analytics platform, plays a crucial role in this space. It offers comprehensive analysis of tokenized assets, tracking the performance of various RWA protocols and providing data-driven insights. Investors can evaluate the fundamentals of projects, assess regulatory requirements, and compare tokenomics with confidence.

The platform’s AI-driven ratings and indices enable users to benchmark tokenized real estate, private credit, and treasury tokenization initiatives against each other using consistent methodologies. Real-time market data and alerts keep investors informed about key developments, such as new institutional partnerships or changes in securities laws impacting tokenized assets.

For those building diversified portfolios that include both native crypto assets and tokenized real-world assets, Token Metrics offers a unified infrastructure to optimize allocation, understand correlations, and navigate the evolving financial landscape.

Challenges and Considerations

Despite its promise, the tokenization revolution faces several challenges:

  • Regulatory uncertainty remains a major hurdle. Jurisdictions differ in how they define securities, tax tokenized assets, and enforce disclosures, complicating global transactions and legal compliance.
  • Technical standardization is still evolving. Multiple blockchains host tokenized assets, but interoperability is limited, potentially locking assets within specific networks.
  • Legal frameworks and blockchain links: The connection between blockchain records and legal systems requires further development. Practical questions—such as responsibility for property maintenance or tax obligations on tokenized real estate—need clear legal frameworks.
  • Balancing regulation and innovation: Ensuring regulatory frameworks support innovation without hindering growth is a delicate task for regulators and industry participants.

Addressing these issues is critical to unlocking the full potential of asset tokenization and expanding its adoption across traditional markets.

The Road Ahead

The tokenization of real-world assets is no longer a question of if but when and how fast. Industry leaders like Larry Fink of BlackRock and Jamie Dimon of JPMorgan have openly endorsed this transformation, signaling that blockchain-based asset ownership is the future of finance.

The year 2025 marks a tipping point where regulatory clarity, institutional participation, and technological maturity converge to enable widespread adoption. The era of pilots is ending; production-scale deployments are underway. Over the next decade, tokenization will likely encompass nearly every asset class, fundamentally altering how ownership is recorded, transferred, and managed.

For investors eager to participate in this revolution, leveraging platforms like Token Metrics is essential to navigate the rapidly evolving market. As the market grows from billions to trillions, those equipped with superior data, analysis, and insights will be best positioned to benefit from one of the most significant wealth creation opportunities in financial history.

The future of asset ownership is being written in code and deployed on blockchains. Understanding what is tokenization of real world assets today is your first step toward engaging with the financial system of tomorrow.

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Token Metrics: AI-powered crypto research and ratings platform. We help investors make smarter decisions with unbiased Token Metrics Ratings, on-chain analytics, and editor-curated “Top 10” guides. Our platform distills thousands of data points into clear scores, trends, and alerts you can act on.
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Recent Posts

Research

Support and Resistance API: Auto-Calculate Smart Levels for Better Trades

Token Metrics Team
4

Most traders still draw lines by hand in TradingView. The support and resistance API from Token Metrics auto-calculates clean support and resistance levels from one request, so your dashboard, bot, or alerts can react instantly. In minutes, you’ll call /v2/resistance-support, render actionable levels for any token, and wire them into stops, targets, or notifications. Start by grabbing your key on Get API Key, then Run Hello-TM and Clone a Template to ship a production-ready feature fast.

What You’ll Build in 2 Minutes

A minimal script that fetches Support/Resistance via /v2/resistance-support for a symbol (e.g., BTC, SOL).

  • A one-liner curl to smoke-test your key.
  • A UI pattern to display nearest support, nearest resistance, level strength, and last updated time.

Next Endpoints to add

  • /v2/trading-signals (entries/exits)
  • /v2/hourly-trading-signals (intraday updates)
  • /v2/tm-grade (single-score context)
  • /v2/quantmetrics (risk/return framing)

Why This Matters

Precision beats guesswork. Hand-drawn lines are subjective and slow. The support and resistance API standardizes levels across assets and timeframes, enabling deterministic stops and take-profits your users (and bots) can trust.

Production-ready by design. A simple REST shape, predictable latency, and clear semantics let you add levels to token pages, automate SL/TP alerts, and build rule-based execution with minimal glue code.

Where to Find

Need the Support and Resistance data? The cURL request for it is in the top right of the API Reference for quick access.

👉 Keep momentum: Get API Key • Run Hello-TM • Clone a Template

How It Works (Under the Hood)

The Support/Resistance endpoint analyzes recent price structure to produce discrete levels above and below current price, along with strength indicators you can use for priority and styling. Query /v2/resistance-support?symbol=<ASSET>&timeframe=<HORIZON> to receive arrays of level objects and timestamps.

Polling vs webhooks. For dashboards, short-TTL caching and batched fetches keep pages snappy. For bots and alerts, use queued jobs or webhooks (where applicable) to avoid noisy, bursty polling—especially around market opens and major events.

Production Checklist

  • Rate limits: Respect plan caps; add client-side throttling.
  • Retries/backoff: Exponential backoff with jitter for 429/5xx; log failures.
  • Idempotency: Make alerting and order logic idempotent to prevent duplicates.
  • Caching: Memory/Redis/KV with short TTLs; pre-warm top symbols.
  • Batching: Fetch multiple assets per cycle; parallelize within rate limits.
  • Threshold logic: Add %-of-price buffers (e.g., alert at 0.3–0.5% from level).
  • Error catalog: Map common 4xx/5xx to actionable user guidance; keep request IDs.
  • Observability: Track p95/p99; measure alert precision (touch vs approach).
  • Security: Store API keys in a secrets manager; rotate regularly.

Use Cases & Patterns

  • Bot Builder (Headless): Use nearest support for stop placement and nearest resistance for profit targets. Combine with /v2/trading-signals for entries/exits and size via Quantmetrics (volatility, drawdown).
  • Dashboard Builder (Product): Add a Levels widget to token pages; badge strength (e.g., High/Med/Low) and show last touch time. Color the price region (below support, between levels, above resistance) for instant context.
  • Screener Maker (Lightweight Tools): “Close to level” sort: highlight tokens within X% of a strong level. Toggle alerts for approach vs breakout events.
  • Risk Management: Create policy rules like “no new long if price is within 0.2% of strong resistance.” Export daily level snapshots for audit/compliance.

Next Steps

  • Get API Key — generate a key and start free.
  • Run Hello-TM — verify your first successful call.
  • Clone a Template — deploy a levels panel or alerts bot today.
  • Watch the demo: Compare plans: Scale confidently with API plans.

FAQs

1) What does the Support & Resistance API return?

A JSON payload with arrays of support and resistance levels for a symbol (and optional timeframe), each with a price and strength indicator, plus an update timestamp.

2) How timely are the levels? What are the latency/SLOs?

The endpoint targets predictable latency suitable for dashboards and alerts. Use short-TTL caching for UIs, and queued jobs or webhooks for alerting to smooth traffic.

3) How do I trigger alerts or trades from levels?

Common patterns: alert when price is within X% of a level, touches a level, or breaks beyond with confirmation. Always make downstream actions idempotent and respect rate limits.

4) Can I combine levels with other endpoints?

Yes—pair with /v2/trading-signals for timing, /v2/tm-grade for quality context, and /v2/quantmetrics for risk sizing. This yields a complete decide-plan-execute loop.

5) Which timeframe should I use?

Intraday bots prefer shorter horizons; swing/position dashboards use daily or higher-timeframe levels. Offer a timeframe toggle and cache results per setting.

6) Do you provide SDKs or examples?

Use the REST snippets above (JS/Python). The docs include quickstarts, Postman collections, and templates—start with Run Hello-TM.

7) Pricing, limits, and enterprise SLAs?

Begin free and scale as you grow. See API plans for rate limits and enterprise SLA options.

Disclaimer

This content is for educational purposes only and does not constitute financial advice. Always conduct your own research before making any trading decisions.

Research

Quantmetrics API: Measure Risk & Reward in One Call

Token Metrics Team
5

Most traders see price—quants see probabilities. The Quantmetrics API turns raw performance into risk-adjusted stats like Sharpe, Sortino, volatility, drawdown, and CAGR so you can compare tokens objectively and build smarter bots and dashboards. In minutes, you’ll query /v2/quantmetrics, render a clear performance snapshot, and ship a feature that customers trust. Start by grabbing your key at Get API Key, Run Hello-TM to verify your first call, then Clone a Template to go live fast.

What You’ll Build in 2 Minutes

  • A minimal script that fetches Quantmetrics for a token via /v2/quantmetrics (e.g., BTC, ETH, SOL).
  • A smoke-test curl you can paste into your terminal.
  • A UI pattern that displays Sharpe, Sortino, volatility, max drawdown, CAGR, and lookback window.

Next Endpoints to Add

  • /v2/tm-grade (one-score signal)
  • /v2/trading-signals
  • /v2/hourly-trading-signals (timing)
  • /v2/resistance-support (risk placement)
  • /v2/price-prediction (scenario planning)

Why This Matters

Risk-adjusted truth beats hype. Price alone hides tail risk and whipsaws. Quantmetrics compresses edge, risk, and consistency into metrics that travel across assets and timeframes—so you can rank universes, size positions, and communicate performance like a professional.

Built for dev speed

A clean REST schema, predictable latency, and easy auth mean you can plug Sharpe/Sortino into bots, dashboards, and screeners without maintaining your own analytics pipeline. Pair with caching and batching to serve fast pages at scale.

Where to Find

The Quant Metrics cURL request is located in the top right of the API Reference, allowing you to easily integrate it with your application.

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How It Works (Under the Hood)

Quantmetrics computes risk-adjusted performance over a chosen lookback (e.g., 30d, 90d, 1y). You’ll receive a JSON snapshot with core statistics:

  • Sharpe ratio: excess return per unit of total volatility.
  • Sortino ratio: penalizes downside volatility more than upside.
  • Volatility: standard deviation of returns over the window.
  • Max drawdown: worst peak-to-trough decline.
  • CAGR / performance snapshot: geometric growth rate and best/worst periods.

Call /v2/quantmetrics?symbol=<ASSET>&window=<LOOKBACK> to fetch the current snapshot. For dashboards spanning many tokens, batch symbols and apply short-TTL caching. If you generate alerts (e.g., “Sharpe crossed 1.5”), run a scheduled job and queue notifications to avoid bursty polling.

Production Checklist

  • Rate limits: Understand your tier caps; add client-side throttling and queues.
  • Retries & backoff: Exponential backoff with jitter; treat 429/5xx as transient.
  • Idempotency: Prevent duplicate downstream actions on retried jobs.
  • Caching: Memory/Redis/KV with short TTLs; pre-warm popular symbols and windows.
  • Batching: Fetch multiple symbols per cycle; parallelize carefully within limits.
  • Error catalog: Map 4xx/5xx to clear remediation; log request IDs for tracing.
  • Observability: Track p95/p99 latency and error rates; alert on drift.
  • Security: Store API keys in secrets managers; rotate regularly.

Use Cases & Patterns

  • Bot Builder (Headless): Gate entries by Sharpe ≥ threshold and drawdown ≤ limit, then trigger with /v2/trading-signals; size by inverse volatility.
  • Dashboard Builder (Product): Add a Quantmetrics panel to token pages; allow switching lookbacks (30d/90d/1y) and export CSV.
  • Screener Maker (Lightweight Tools): Top-N by Sortino with filters for volatility and sector; add alert toggles when thresholds cross.
  • Allocator/PM Tools: Blend CAGR, Sharpe, drawdown into a composite score to rank reallocations; show methodology for trust.
  • Research/Reporting: Weekly digest of tokens with Sharpe ↑, drawdown ↓, and volatility ↓.

Next Steps

  • Get API Key — start free and generate a key in seconds.
  • Run Hello-TM — verify your first successful call.
  • Clone a Template — deploy a screener or dashboard today.
  • Watch the demo: VIDEO_URL_HERE
  • Compare plans: Scale with API plans.

FAQs

1) What does the Quantmetrics API return?

A JSON snapshot of risk-adjusted metrics (e.g., Sharpe, Sortino, volatility, max drawdown, CAGR) for a symbol and lookback window—ideal for ranking, sizing, and dashboards.

2) How fresh are the stats? What about latency/SLOs?

Responses are engineered for predictable latency. For heavy UI usage, add short-TTL caching and batch requests; for alerts, use scheduled jobs or webhooks where available.

3) Can I use Quantmetrics to size positions in a live bot?

Yes—many quants size inversely to volatility or require Sharpe ≥ X to trade. Always backtest and paper-trade before going live; past results are illustrative, not guarantees.

4) Which lookback window should I choose?

Short windows (30–90d) adapt faster but are noisier; longer windows (6–12m) are steadier but slower to react. Offer users a toggle and cache each window.

5) Do you provide SDKs or examples?

REST is straightforward (JS/Python above). Docs include quickstarts, Postman collections, and templates—start with Run Hello-TM.

6) Polling vs webhooks for quant alerts?

Dashboards usually use cached polling. For threshold alerts (e.g., Sharpe crosses 1.0), run scheduled jobs and queue notifications to keep usage smooth and idempotent.

7) Pricing, limits, and enterprise SLAs?

Begin free and scale up. See API plans for rate limits and enterprise SLA options.

Disclaimer

All information provided in this blog is for educational purposes only. It is not intended as financial advice. Users should perform their own research and consult with licensed professionals before making any investment or trading decisions.

Research

Crypto Trading Signals API: Put Bullish/Bearish Calls Right in Your App

Token Metrics Team
4

Timing makes or breaks every trade. The crypto trading signals API from Token Metrics lets you surface bullish and bearish calls directly in your product—no spreadsheet wrangling, no chart gymnastics. In this guide, you’ll hit the /v2/trading-signals endpoint, display actionable signals on a token (e.g., SOL, BTC, ETH), and ship a conversion-ready feature for bots, dashboards, or Discord. Start by creating a key on Get API Key, then Run Hello-TM and Clone a Template to go live fast.

What You’ll Build in 2 Minutes

  • A minimal script that fetches Trading Signals via /v2/trading-signals for one symbol (e.g., SOL).
  • A copy-paste curl to smoke-test your key.
  • A UI pattern to render signal, confidence/score, and timestamp in your dashboard or bot.

Endpoints to add next

  • /v2/hourly-trading-signals (intraday updates)
  • /v2/resistance-support (risk placement)
  • /v2/tm-grade (one-score view)
  • /v2/quantmetrics (risk/return context)

Why This Matters

Action over analysis paralysis. Traders don’t need more lines on a chart—they need an opinionated call they can automate. The trading signals API compresses technical momentum and regime reads into Bullish/Bearish events you can rank, alert on, and route into strategies.

Built for dev speed and reliability. A clean schema, predictable performance, and straightforward auth make it easy to wire signals into bots, dashboards, and community tools. Pair with short-TTL caching or webhooks to minimize polling and keep latency low.

Where to Find

You can find the cURL request for Crypto Trading Signals in the top right corner of the API Reference. Use it to access the latest signals!

Live Demo & Templates

  • Trading Bot Starter: Use Bullish/Bearish calls to trigger paper trades; add take-profit/stop rules with Support/Resistance.
  • Dashboard Signal Panel: Show the latest call, confidence, and last-updated time; add a history table for context.
  • Discord/Telegram Alerts: Post signal changes to a channel with a link back to your app.

How It Works (Under the Hood)

Trading Signals distill model evidence (e.g., momentum regimes and pattern detections) into Bullish or Bearish calls with metadata such as confidence/score and timestamp. You request /v2/trading-signals?symbol=<ASSET> and render the most recent event, or a small history, in your UI.

For intraday workflows, use /v2/hourly-trading-signals to update positions or alerts more frequently. Dashboards typically use short-TTL caching or batched fetches; headless bots lean on webhooks, queues, or short polling with backoff to avoid spiky API usage.

Production Checklist

  • Rate limits: Know your tier caps; add client-side throttling and queues.
  • Retries/backoff: Exponential backoff with jitter; treat 429/5xx as transient.
  • Idempotency: Guard downstream actions (don’t double-trade on retries).
  • Caching: Memory/Redis/KV with short TTLs for reads; pre-warm popular symbols.
  • Webhooks & jobs: Prefer webhooks or scheduled workers for signal change alerts.
  • Pagination/Bulk: Batch symbols; parallelize with care; respect limits.
  • Error catalog: Map common 4xx/5xx to clear fixes; log request IDs.
  • Observability: Track p95/p99 latency, error rate, and alert delivery success.
  • Security: Keep keys in a secrets manager; rotate regularly.

Use Cases & Patterns

  • Bot Builder (Headless): Route Bullish into candidate entries; confirm with /v2/resistance-support for risk and TM Grade for quality.
  • Dashboard Builder (Product): Add a “Signals” module per token; color-code state and show history for credibility.
  • Screener Maker (Lightweight Tools): Filter lists by Bullish state; sort by confidence/score; add alert toggles.
  • Community/Discord: Post signal changes with links to token pages; throttle to avoid noise.
  • Allocator/PM Tools: Track signal hit rates by sector/timeframe to inform position sizing (paper-trade first).

Next Steps

  1. Get API Key — create a key and start free.
  2. Run Hello-TM — confirm your first successful call.
  3. Clone a Template — deploy a bot, dashboard, or alerting tool today.

FAQs

1) What does the Trading Signals API return?

A JSON payload with the latest Bullish/Bearish call for a symbol, typically including a confidence/score and generated_at timestamp. You can render the latest call or a recent history for context.

2) Is it real-time? What about latency/SLOs?

Signals are designed for timely, programmatic use with predictable latency. For faster cycles, use /v2/hourly-trading-signals. Add caching and queues/webhooks to reduce round-trips.

3) Can I use the signals in a live trading bot?

Yes—many developers do. A common pattern is: Signals → candidate entry, Support/Resistance → stop/targets, Quantmetrics → risk sizing. Always backtest and paper-trade before going live.

4) How accurate are the signals?

Backtests are illustrative, not guarantees. Treat signals as one input in a broader framework with risk controls. Evaluate hit rates and drawdowns on your universe/timeframe.

5) Do you provide SDKs and examples?

You can integrate via REST using JavaScript and Python snippets above. The docs include quickstarts, Postman collections, and templates—start with Run Hello-TM.

6) Polling vs webhooks for alerts?

Dashboards often use cached polling. For bots/alerts, prefer webhooks or scheduled jobs and keep retries idempotent to avoid duplicate trades or messages.

7) Pricing, limits, and enterprise SLAs?

Begin free and scale as you grow. See API plans for allowances; enterprise SLAs and support are available.

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