TinyFish vs Bright Data: Web Scraping, Agents and Pricing

Bright Data and TinyFish both support web data collection, but they approach the problem through different product offerings. Bright Data provides proxy infrastructure, scraping tools, managed browsers, and web data services. TinyFish provides Search, Fetch, Browser, and Agent APIs for retrieving information and completing workflows on live websites.

The important question isn't whether one platform is universally better. It's whether your workload primarily needs large-scale data extraction, access infrastructure, browser control, or goal-directed automation.

Quick Reference: Which Tool Fits Your Situation?

1. You need IP rotation across 195+ countries → Bright Data
2. You need AI-driven multi-step workflows from natural language → TinyFish
3. You're scraping known sites at massive scale (e-commerce, search, professional platforms) → Bright Data
4. You're automating dynamic, diverse workflows with login and navigation → TinyFish
5. You need pre-built scrapers for 120+ specific platforms → Bright Data
6. You need goal-directed workflows across different websites →TinyFish Agent can navigate and interact with supported sites using natural-language instructions, subject to site access requirements and workflow limitations.
7. You have existing scrapers and just need better proxies → Bright Data
8. You want browser, proxy, LLM, and complete web infrastructure in one step price → TinyFish

What Each Product Actually Does

Bright Data is a full web data platform:

• Proxy networks: 150M+ residential IPs in a static pool (Bright Data's product pages also reference 400M+ monthly rotating IPs — the 150M figure reflects the dedicated/static pool, 400M includes monthly active rotation); datacenter, ISP, and mobile proxies across 195+ countries.
• Web Unlocker: Automated proxy rotation + CAPTCHA solving for protected sites.
• Scraping Browser: Cloud-hosted browser with built-in unblocking.
• Pre-built scrapers (Datasets): Ready-made collectors for 120+ popular sites across e-commerce, professional, real estate, and more.
• SERP API: Search engine result pages as structured data.
• MCP Server: Connects Bright Data tools to AI agent frameworks.

TinyFish provides four complementary APIs for working with live web data:

• Search: Discover relevant web pages and receive structured search results.
• Fetch: Retrieve content from known URLs without building a separate scraping pipeline.
• Browser: Run browser sessions through managed infrastructure while retaining programmatic control.
• Agent: Execute multi-step website workflows from natural-language instructions and return structured results.
• These products support different levels of interaction, from retrieving content to navigating websites and completing more complex tasks.

Where Bright Data Wins

IP network breadth. 150M+ residential IPs across 195 countries is genuinely unmatched. For geo-specific data collection — pricing verification across 50 countries, ad compliance monitoring, content localization checks — this infrastructure has no direct substitute. TinyFish includes residential proxy routing in 7 countries (US, GB, CA, DE, FR, JP, AU). For niche geographies, Bright Data is the clear choice.

Pre-built scrapers for known sites. If your target is any of 120+ supported platforms, Bright Data's pre-built collectors are production-ready. No prompt engineering, no AI reasoning overhead — just structured data from known schemas. This is faster and more reliable than an agent for these specific sites at scale.

Raw proxy access for existing code. If you already have Playwright or Puppeteer scripts and just need better proxies, Bright Data integrates without changing your codebase. TinyFish requires switching to the agent paradigm — you can't plug TinyFish's proxies into your existing scraper.

Volume at scale. Bright Data handles hundreds of millions of requests daily. For bulk data collection on known targets, their infrastructure is battle-tested at a level few platforms match.

Where the Approaches Diverge

New or unknown sites. Bright Data's pre-built scrapers cover 120+ sites. The web has millions. When your target isn't on the supported list, you're back to building your own scraper — choosing proxies, writing selectors, handling JS rendering, managing retries. TinyFish handles any site from a natural language description. For teams whose targets are fixed and well-supported, Bright Data's pre-built approach is more reliable. For teams whose targets shift frequently, TinyFish's flexibility matters.

Dynamic, multi-step workflows. Bright Data's Scraping Browser gives you a cloud browser with proxy access — powerful for developers who want to write their own orchestration logic. The tradeoff: login flows, conditional navigation, form filling, and multi-page sequences are your code to write and maintain. TinyFish's agent handles the full sequence in one call. The first gives you control; the second gives you speed to production.

The distinction is particularly relevant for teams with changing target websites. A prebuilt data collector can be efficient when the source and required schema are consistent. A goal-directed agent becomes useful when the workflow involves conditional navigation, unfamiliar interfaces, or interactions that vary between websites. Teams should evaluate both approaches against the specific sources they need to access, rather than assuming one execution model is suitable for every task.

Pricing structure. Bright Data's modular pricing (per GB, per request, per CPM) gives sophisticated buyers the ability to optimize cost per workload but forecasting spend across multiple products requires careful modeling. TinyFish charges per step with all infrastructure included. Simpler to forecast, less opportunity to optimize components independently.

AI integration models. Bright Data's MCP server lets AI agents orchestrate their existing tool suite — proxies, unlockers, scrapers — via function calls. This works well for teams building custom agent pipelines who want best-in-class access infrastructure underneath. TinyFish's AI is built into the execution engine itself: the agent perceives pages, decides actions, and adapts mid-workflow without external orchestration. The difference is whether AI sits on top of your tools (Bright Data) or is the tool (TinyFish).

Pricing: Complexity vs Simplicity

Bright Data

Bright Data's pricing is modular — each layer billed separately:

• Residential proxies: ~$2.50–15/GB (promotional pricing currently as low as $2.50/GB; standard pay-as-you-go $8/GB; promotions may end without notice — check brightdata.com/pricing for current rates)
• Datacenter proxies: ~$0.60–1.20/GB (shared pool); dedicated IPs ~$3–6/GB
• Web Unlocker: ~$1–3/1K requests
• Scraping Browser: ~$0.10–0.20/hr
• Pre-built scrapers: Varies by site and volume
• SERP API: ~$2–5/1K searches

Ranges based on Bright Data's publicly listed pricing, third-party buyer data (Vendr), and independent reviews as of Q1 2026. Actual rates vary by volume, contract terms, and negotiation. Enterprise buyers typically negotiate 20–40% below list.

At high volume with negotiated rates, Bright Data can be extremely cost-effective per request. At lower volume across multiple products, costs compound quickly.

TinyFish

TinyFish uses Wallet-based, usage-based billing. Search and Fetch are free within their published limits. Agent execution costs $0.016 per step, and Browser sessions cost $0.002 per minute. New accounts receive $8 in Wallet funds, and higher limits are available through Enterprise. For workflows using Agent, the price includes the managed execution infrastructure and model inference.

Bright Data vs TinyFish: Which Approach Fits Your Data Pipeline?

The most useful way to compare Bright Data and TinyFish is by evaluating the type of data being collected and the operations required to retrieve it. A workflow involving thousands of known public product pages has different infrastructure requirements from one that needs to navigate authenticated supplier portals.

For high-volume extraction from predictable sources, purpose-built scrapers and proxy infrastructure may be more economical. For workflows that require navigation and decisions across websites, managed agent execution may reduce development and maintenance work. The right choice depends on task success, operating cost, and the engineering resources available.

The real cost comparison

Scenario A: E-commerce product extraction, 10,000 pages. Using Bright Data's pre-built scraper: ~$10–50 depending on tier. Fast, reliable, structured output. TinyFish web agent doing the same: estimated 10–50K steps at $0.016/step = $160–800. Bright Data wins on known targets at scale; this is the scenario pre-built scrapers were built for.

Scenario B — 100 multi-step workflows across diverse insurance portals. Each workflow: log in, navigate to the claims dashboard, extract structured data. Bright Data path: build and maintain a custom scraper per site (proxy config + selector logic + parsing + maintenance). That's significant engineering time before you touch the data. TinyFish: 100 natural language task descriptions × ~10–20 steps per workflow (based on TinyFish internal benchmark data for mid-complexity multi-step tasks; simpler flows run 10 steps, more conditional ones run 20) × $0.016 = ~$16–32. Plus the engineering time is near-zero.

Bright Data is cheaper per page when the site is supported and the workflow is simple. TinyFish is cheaper per workflow when the task is complex and the sites are diverse.

Looking for a Bright Data Alternative?

Teams typically look for Bright Data alternatives for three reasons:

1. Their target sites aren't in the 120+ pre-built scraper list. If you need data from niche or industry-specific sites, you're building custom scrapers regardless of which proxy provider you use — at which point the value of Bright Data's pre-built infrastructure shrinks.
2. They want to stop maintaining scraper code. Selectors break. Sites redesign. Site access requirements evolve. For teams tired of maintenance overhead, a natural language agent that handles site changes automatically changes the calculus.
3. The workflow involves more than extraction. Bright Data's tools are optimized for getting data from sites. When the workflow is doing things on sites — form submissions, dashboard navigation, multi-step authenticated flows — the tool set doesn't fit as well.

If any of these applies to your situation, TinyFish is worth testing on your actual workflow before committing to a proxy-first approach. If your bottleneck is purely geographic coverage or volume on known sites, Bright Data remains the strongest option.

Decision Framework

Choose Bright Data if your task is:

• Collect data at scale from one of 120+ pre-built supported sites — e-commerce, professional networks, real estate
• Route requests through a specific country or city — 150M+ residential IPs across 195 countries
• Plug better proxies into existing Playwright or Puppeteer scripts without rewriting your codebase
• Run with enterprise procurement, compliance documentation, and negotiated contracts

Choose TinyFish if your task is:

• Automate workflows across sites not in Bright Data's 120+ supported list
• Complete authenticated, multi-step sequences — login, navigate, extract — in a single API call
• Avoid maintaining scraper code when target sites change their layouts
• Forecast spend simply: one price per step, all infrastructure (browser, proxy, LLM) included

Use both if: High-volume pipelines on known, supported sites belong on Bright Data's pre-built collectors. Long-tail, diverse, authenticated workflows belong on TinyFish. Many production data teams run this split.

When to Use Both

Bright Data and TinyFish aren't competing for the same workflow. They operate at different layers of the same problem.

The split that works in practice: use Bright Data's pre-built scrapers for your high-volume, known-target data collection — these are its strongest use case, production-ready, and faster than any AI agent for those schemas. Use TinyFish for the long tail: the 40 insurance portals, the 200 dynamic data sources, the authenticated dashboards that don't have a pre-built collector.

The teams that benefit most from running both typically have one or two high-volume known-site pipelines that Bright Data handles cheaply, plus a growing set of one-off or irregular workflows where building and maintaining custom scrapers doesn't make economic sense.

The teams that should just pick one: if the overwhelming majority of your data needs come from Bright Data's 120+ supported sites — meaning you rarely need to scrape anything outside that list — the complexity of adding another platform isn't worth it. If your needs are mostly diverse, dynamic sites with workflow logic, TinyFish alone is cleaner.

Test It On Your Actual Workflow

If your workflow is "extract structured data from a supported platform at scale" — Bright Data is probably the right tool today.

If your workflow is "visit 50 different supplier portals, log in, navigate to the inventory page, and pull structured data", that's TinyFish's native use case. Includes free sign-up, with available $8 credits to start; no credit card required. Available via REST API, Python/Node SDK, CLI, and MCP server for direct integration with Claude C$ 8 in credits availableode and Cursor.

👉 Start free on TinyFish

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• Handling Sites with Strict Automation Requirements
• Why 90% of the Internet Is Invisible