competitor-discovery

name

competitor-discovery

description

Find a startup's REAL competitors — the alternatives customers actually compare them to, not just the names on the pitch deck. Use when a founder asks "who are my real competitors?", asks "what does the choice landscape look like for X?", wants to validate or expand a pitch-deck competitor slide, or needs to map alternatives before pain mining or positioning work. Combines Anysite MCP (LinkedIn company/search, LinkedIn posts `mentioned[]` extraction + aggregation, Reddit, YouTube video search + subtitles + comments, Twitter search, YC, SEC for public incumbents, DuckDuckGo as Exa fallback) and Exa MCP (semantic web search + JS-page fetch) to surface direct competitors, substitutes, "doing nothing" workarounds, and convergence threats. Supports two modes: `top5` (5 anchor names) and `landscape` (5 anchors + 25–70-name long-tail + suggested strategic-group axes). Run this before customer-pain-mining or positioning-map.

Competitor Discovery

The pitch deck usually names 2–3 lookalikes. The real comparison set is whatever the customer almost picked instead — including substitutes, free generic tools, "doing nothing," and adjacent-market players one feature-pivot away. This skill produces that map.

Framing (so the categories don't drift)

Three industry frameworks converge on what to capture:

• Christensen JTBD: The customer is "hiring" your product for a job. The true competitor is anything else they could hire for the same job — including bananas (vs. milkshakes), spreadsheets (vs. SaaS), boredom (vs. Facebook).
• April Dunford (Obviously Awesome): Competitive alternatives are the starting point of positioning. Ask "what would the customer do if our product didn't exist?" — this surfaces "do nothing" as the #1 alternative in ~25% of lost enterprise deals. Beware "phantom competitors" — theoretical companies that never show up in real deals; they dilute positioning.
• Porter's Five Forces — Substitutes + New Entrants: Substitutes solve the same outcome with a different category. New entrants are adjacent-market players one feature pivot away.

These map to our four output categories (Direct / Substitute / Workaround / Convergence). Detectors, app stores, compliance tools, payment processors, distribution platforms — these are "rule-setters" that shape the market, not competitors. Note them in context.

When this skill applies

• A founder names 1–3 obvious competitors; you validate or expand
• Pre-flight before customer-pain-mining or positioning-map
• Mapping a product into a category when the category is fuzzy
• Sanity-checking a pitch deck competitor slide

What you need

1. Product name (e.g. "Anysite")
2. One-liner — what it does, for whom (e.g. "AI writing for college students that drafts, cites, runs integrity checks")
3. Pitch-deck competitors if named (optional)
4. Segment indicator — consumer / SMB / B2B / DevTool / DeepTech. This determines which source order to use.
5. Mode — top5 (default; produces 5 anchor names + 1 workaround) or landscape (produces 5 anchors + 25-70 name long-tail + 2 suggested strategic-group axes for positioning-map).

Ask for missing info before running. Don't guess from the product name.

Tools

Anysite MCP (call mcp__claude_ai_Anysite__discover(source, category) once before first execute):

• mcp__claude_ai_Anysite__execute(source="linkedin", category="search", endpoint="search_companies", params={...}) — broad competitor search by keywords. Best primitive for consumer / mid-market.
• mcp__claude_ai_Anysite__execute(source="linkedin", category="search", endpoint="search_posts", params={...}) — listicle posts naming competitors with structured mentioned[] field. Landscape mode aggregates this across N posts.
• mcp__claude_ai_Anysite__execute(source="reddit", category="search", endpoint="search_posts", params={...}) — real comparison threads.
• mcp__claude_ai_Anysite__execute(source="twitter", category="search", endpoint="search_posts", params={...}) — viral category quotes. Set min_likes ≥ 5 to filter noise; for breakthrough quotes set ≥ 50.
• mcp__claude_ai_Anysite__execute(source="yc", category="search", endpoint="search_companies", params={...}) — useful for B2B / DeepTech, also for niches where the latest YC batches surge into a consumer category (check w/o assumption).
• mcp__claude_ai_Anysite__execute(source="youtube", category="search", endpoint="search_videos", params={...}) — listicle videos for B2C / prosumer niches. Filter by view_count ≥ 10K.
• mcp__claude_ai_Anysite__execute(source="youtube", category="video", endpoint="video_subtitles", params={...}) — fetch the transcript of 1 head-to-head comparison video for deep-dive context. Auto-captions can garble product names ("Elicit" → "illicit", "SciSpace" → "size space") — use for cross-validation, not primary extraction.
• mcp__claude_ai_Anysite__execute(source="youtube", category="video", endpoint="video_comments", params={...}) — comments under listicle videos cross-validate which competitors customers compare against. Filter like_count >= 5. Watch for affiliate astroturf (same product praised in 5+ short identical comments).
• mcp__claude_ai_Anysite__execute(source="duckduckgo", category="search", endpoint="search", params={...}) — Exa fallback for budget-constrained runs.
• mcp__claude_ai_Anysite__execute(source="sec", category="search", endpoint="search_companies", params={...}) — for niches with a public incumbent (e.g. Chegg in EdTech, Salesforce in B2B sales). Pull the latest 10-K, fetch the Competition section via Exa.
• mcp__claude_ai_Anysite__query_cache(cache_key, conditions, sort_by, aggregate, group_by, ...) — slice cached results. Landscape mode aggregates LinkedIn mentioned[] here.

Exa MCP:

• mcp__claude_ai_Exa__web_search_exa(query, numResults) — semantic search. Describe the ideal page, not keywords.
• mcp__claude_ai_Exa__web_fetch_exa(urls) — read JS-heavy pages (modern SaaS marketing sites the webparser can't render).

Budget:

• top5 mode: ~6 Anysite executes + ~3 Exa calls.
• landscape mode: ~10 Anysite + ~4 Exa. Includes 1 YouTube subtitle deep-dive, 1 SEC fetch if public incumbent, 1 landscape-mode aggregation.

After the budget, marginal signal drops fast.

How to run

Source order depends on segment:

• Consumer / Prosumer / EdTech / SMB SaaS: Exa → LinkedIn search_companies → LinkedIn search_posts (with mentioned[]) → YouTube search_videos → Reddit. Skip YC unless recent batch surge. Skip Instagram for software (validated).
• B2B / Enterprise / DeepTech / DevTool: YC → LinkedIn search_companies → LinkedIn search_posts → Exa → Reddit. Skip YouTube unless niche has analyst-channel ecosystem. SEC 10-K if public incumbent.
• Public-incumbent niche (EdTech, B2B sales, HR, fintech with public anchors): SEC 10-K Competition section is canonical — add as Step 8.

If Exa is rate-limited or returns thin results, fall back to duckduckgo/search/search — covers different long-tail URLs.

If mode = landscape, also run Step 3.5 (LinkedIn mentioned[] aggregation) and optionally Step 4a (YouTube subtitle deep-dive on the highest-view-count head-to-head video).

Step 1 — Exa: category listicles (consumer-first niches)

Find the "best X" comparison pages. These already did the clustering for you.

mcp__claude_ai_Exa__web_search_exa(query="listicle ranking best <category> tools for <audience> 2025 — detailed comparison with pricing pros and cons", numResults=8)

Read the highlights — most listicles return 3–5 named competitors right in the snippet, often with prices. If a listicle looks dense, fetch it:

mcp__claude_ai_Exa__web_fetch_exa(urls=["<top listicle URL>"])

For Anysite (API-first web data platform with MCP for AI agents), the search "listicle ranking best web scraping APIs and AI agent data extraction platforms 2025 — detailed comparison with pricing and pros and cons" surfaces Bright Data, Apify, Firecrawl, ScrapingBee, ScraperAPI, Oxylabs, Zyte, Diffbot, Octoparse in the first page. That is the canonical set.

Step 2 — LinkedIn company search (broad category discovery)

mcp__claude_ai_Anysite__execute(source="linkedin", category="search", endpoint="search_companies", params={"keywords": "<2-3 named-feature keywords>", "count": 20})

Pick keywords that name what the product does, not what it is. "AI writing assistant students academic" beats "EdTech AI" by 10x.

Returns 10+ companies with URN, name, industry. Cross-reference with Step 1 — companies appearing in both are strong direct competitors.

If keyword combo returns 0 results, broaden it (drop one term) — narrow queries fail silently on LinkedIn.

Step 3 — LinkedIn search_posts: listicle-shaped posts

Founders + creators in your niche routinely cross-post "10 tools for X" listicles. These contain structured competitor lists:

mcp__claude_ai_Anysite__execute(source="linkedin", category="search", endpoint="search_posts", params={"keywords": "<seed competitor 1> <seed competitor 2> <niche keyword>", "sort": "recent", "date_posted": "past-month", "count": 15})

Use seed-competitor + niche keywords (e.g. "Firecrawl Bright Data web scraping" or "Paperpal academic writing"). Broad market queries like "AI writing tools students best 2025" tend to return discourse posts (ethics, policy, hiring) rather than competitor listicles.

Each post has a mentioned[] array — extract company names and URLs directly. A single high-engagement listicle post typically names 6–12 competitors. Empirically, one well-targeted query on the web-scraping niche surfaced a 50-like "AI consensus" post naming 10 competitors (Bright Data, Zyte, ScrapingBee, Firecrawl, Scrape.do, ScraperAPI, Apify, Scrapingdog, Oxylabs, Scrapfly) with LinkedIn URLs attached.

Filter the spam: much of LinkedIn is SEO accounts cross-posting identical tool lists. Keep only posts where comment_count + sum(reactions[].count) >= 10. Use query_cache:

mcp__claude_ai_Anysite__query_cache(cache_key="<from step 3>", conditions=[{"field": "comment_count", "op": ">=", "value": 5}])

Step 3.5 — Landscape-mode aggregation (only if mode = "landscape")

For market-mapping or pitch-deck-validation use cases, you don't want a top-5 — you want the full long-tail. Aggregate the mentioned[] field across the 15+ posts from Step 3 into one ranked list. Anysite's query_cache group_by only handles single-level fields, so do the aggregation in your head / inline:

1. Use query_cache(cache_key=<from step 3>, sort_by="comment_count", sort_order="desc", limit=15) to surface the top engagement-weighted posts.
2. For each post in the cache, walk mentioned[] and pull name where @type == "@linkedin_search_post_mentioned_company".
3. Tally name → count across all posts. Sort descending. Names with ≥2 cross-post citations are your anchor candidates (high-confidence landscape members); names with 1 citation are the long-tail watch list.

Empirically: 1 broad LinkedIn search (Step 3, count=25) tends to surface 10–28 unique competitor names, with 5+ having ≥2 cross-post citations. The cross-post citation count is your phantom-competitor filter: keep names with ≥2 citations as candidates, set the rest aside in a "long-tail / watch list" section. You can optionally run Step 3 twice with different keyword combinations (e.g. one query with niche keywords + one with seed-trio competitors) and merge tallies.

Bonus: use the same cached posts to surface the top creator-analyst in the niche (highest-engagement repeat author) — useful for linkedin/user/posts follow-up if you later want creator-commentary signal.

Step 4 — YouTube listicle videos (B2C / prosumer / EdTech / consumer-AI / DevTool)

mcp__claude_ai_Anysite__execute(source="youtube", category="search", endpoint="search_videos", params={"query": "best <category> tools <year> comparison review", "count": 15})

Filter for view_count ≥ 10K — that's the affiliate-spam vs. real-creator line. Look for title patterns: "X vs Y", "best N tools for <audience>", "I tried N tools". Each high-view-count video names 5–15 competitors with feature comparison. Validated on the web-scraping niche: "Apify Bright Data Firecrawl comparison review" returned a Greg Isenberg Firecrawl explainer at 169K views and an Apify-vs-Octoparse-vs-BrowseAI head-to-head.

For the single highest-view-count head-to-head comparison video, optionally fetch the transcript:

mcp__claude_ai_Anysite__execute(source="youtube", category="video", endpoint="video_subtitles", params={"video": "<id>", "lang": "en"})

Auto-caption caveat: names can garble ("Elicit" → "illicit", "SciSpace" → "size space"). Use the transcript to cross-validate names already in your candidate list, not to extract new names via regex.

Optionally fetch top comments:

mcp__claude_ai_Anysite__execute(source="youtube", category="video", endpoint="video_comments", params={"video": "<id>", "count": 30})

Filter like_count >= 5. Watch for affiliate astroturf — the same product praised in 5+ short identical-tone comments from different accounts is paid promotion, not signal. Skip the cluster.

YouTube has stronger affordance for B2C / prosumer / DevTool niches than for enterprise B2B. For DeepTech / strict-B2B niches, prefer YC + SEC over YouTube.

Step 5 — Reddit alternative-hunting (real comparison set)

For 2–3 of your strongest candidates from Steps 1–3:

mcp__claude_ai_Anysite__execute(source="reddit", category="search", endpoint="search_posts", params={"query": "<competitor> alternative", "sort": "relevance", "time_filter": "year", "count": 15})

Useful query patterns (pick 2 of these per run, not all):

• "<competitor> alternative" — direct substitution
• "<job-to-be-done> tool recommendations" — discovery threads
• "better than <competitor>" — explicit comparisons

Use sort=relevance, not sort=top, because top sorts by virality and floods you with off-topic viral content. Relevance is per-query.

Single-phrase queries beat OR-chains. "Bright Data alternative expensive" works; OR-chained queries like "Bright Data alternative sucks expensive overpriced" confuse Reddit's relevance ranking and return off-topic threads.

In each thread, capture: competitor names mentioned in the title and top comments, subreddit, post title, vote_count. Vote count is your relevance signal.

Step 6 — Workaround / "doing nothing"

The real competitor is often free + manual. Search Reddit for the manual workaround:

mcp__claude_ai_Anysite__execute(source="reddit", category="search", endpoint="search_posts", params={"query": "<job-to-be-done> manually OR spreadsheet OR diy OR self-host", "count": 10, "sort": "relevance", "time_filter": "year"})

If users describe a free-and-manual recipe in detail, that recipe is a competitor. Founders systematically underrate this. For Anysite, "self-host Crawl4AI + bring your own LLM key + manage proxies yourself" is the dominant developer workaround across r/webscraping, r/LocalLLaMA — explicitly costed out in multiple comparison blogs.

Step 7 — (B2B / DevTool / DeepTech) YC search

Worth running for B2B / DeepTech / Enterprise / Dev Tool. YC tends to be thin for most consumer / EdTech niches; spot-check first. Exception: if the niche has had a recent YC batch surge (e.g. agentic-AI-consumer in W25/F25), check before skipping.

mcp__claude_ai_Anysite__execute(source="yc", category="search", endpoint="search_companies", params={"query": "<2-3 niche keywords>", "count": 20})

Read one_liner, industries, subindustry. Keep names whose tagline overlaps the JTBD. Validated on the web-scraping niche: "web scraping browser automation agent" returned Browser Use (W25, 50K stars in 3 months) and StableBrowse (S26) — two convergence threats from the agentic-browser side that the named-competitor set missed.

Step 8 — (Public-incumbent niche only) SEC Competition section

For niches where a public-company incumbent exists (EdTech: Chegg, Duolingo; B2B sales: Salesforce; HR: Workday; etc.):

mcp__claude_ai_Anysite__execute(source="sec", category="search", endpoint="search_companies", params={"entity_name": "<incumbent>", "forms": ["10-K"], "count": 3})

Pick the latest 10-K, fetch its document_url via Exa: mcp__claude_ai_Exa__web_fetch_exa(urls=["<doc_url>"]). Look for the "Competition" and "Risk Factors" sections — these contain a legal-grade enumerated competitor list (Chegg's 10-K names Course Hero, ChatGPT, Quizlet directly). Skip when no public incumbent exists.

Step 9 — Synthesize

Cluster everything into four buckets:

1. Direct competitors — same job, same audience, same category (e.g. Firecrawl for Anysite)
2. Substitutes — different category, same outcome / JTBD (e.g. ChatGPT for an academic AI writer — Christensen lens)
3. Workarounds / "doing nothing" — no product, but a habit the customer already has (Dunford lens — often the #1 alternative the pitch deck misses)
4. Convergence threats (optional) — adjacent-market players one feature-pivot away (e.g. Browser Use's agentic browser is a convergence threat for Anysite's web-data API). Porter "new entrants from adjacent markets." Include only if you can name ≥1 candidate with cross-source signal.

Sort within each bucket by independent-source count: a name appearing in Exa + LinkedIn + Reddit + YouTube is much stronger evidence than one appearing only in a single SEO listicle. Apply the phantom-competitor filter: drop any name with only 1 source unless it's the founder's own pitch-deck-named competitor (validate or invalidate explicitly).

In landscape mode, identify 2 strategic-group axes that best separate the comparison set — e.g. Pricing (free → $30/mo) and Audience breadth (academic-only → general). Pass these to positioning-map as the suggested axes.

Output

Markdown report.

top5 mode (default, ~400–600 words)

# Competitor Discovery — <Product>

## Executive summary
3 sentences: top-5 real alternatives, the dominant workaround, the one finding the founder probably didn't expect.

## The comparison set

| # | Name | Category | Evidence | Source |
|---|------|----------|----------|--------|
| 1 | ... | Direct / Substitute / Workaround / Convergence | 1-line, e.g. "Cited in 6 of 8 listicles + r/college thread" | Exa, LinkedIn, Reddit, YouTube |

## Rule-setters / context players (shape the market but aren't substitutes)
- e.g. GPTZero, Turnitin for AI writing
- e.g. Apple App Store for consumer mobile

## So what
- Names missing from the pitch deck
- Dominant workaround pattern
- Strongest cross-source signal
- Convergence threats to watch

landscape mode (~700–1000 words, includes long-tail)

# Competitor Landscape — <Product>

## Executive summary
3-4 sentences: 5 anchor names by independent-source count, the dominant workaround, the convergence threat, the 2 axes that best separate the field.

## Anchor comparison set (top 5 by cross-source signal)

(same table as top5 mode)

## Long-tail (named in 1-2 sources only — watch list)

Grouped by category:
- **Direct**: <name1>, <name2>, ... (each with single-line citation)
- **Substitute**: <name1>, ...
- **Convergence**: <name1>, ...

## Suggested axes for positioning-map handoff
1. <Axis A — e.g. "Pricing: free → $30/mo">
2. <Axis B — e.g. "Audience breadth: academic-only → general writing">

## Rule-setters / context players

(same as top5)

## So what
(same as top5)

Keep tone factual. If a query returned no signal, write "no signal found" rather than padding.

Don't

• Don't trust similar_organizations[] on the LinkedIn company endpoint for small companies — the field returns unrelated noise. Use linkedin/search/search_companies results instead.
• Don't run YC for consumer SaaS niches without checking. YC has historically been B2B-heavy, but recent batches surface in unexpected categories (e.g. agentic-AI-consumer in W25/S26). Spot-check before skipping.
• Don't include every LinkedIn search result. Filter to top-5 corroborated names in top5 mode; even in landscape mode, separate anchor names (≥2 sources) from the long-tail watch list (1 source).
• Don't use Reddit sort=top for category-level queries — top sorts by global virality (off-topic viral content surfaces) not relevance.
• Don't include the founder's own product as a competitor (it sometimes shows up in search).
• Don't search LinkedIn search_posts by broad market keywords — broad phrases tend to return ethics/discourse posts, not competitor listicles. Use seed-competitor + niche keywords like "Firecrawl Bright Data web scraping" or "Apify Bright Data alternatives".
• Don't run Instagram for SaaS / AI-tools / software competitor discovery — Instagram's affordance is image-first; structured 10+ tool lists live on LinkedIn / YouTube. Instagram is useful for DTC physical-goods niches only.
• Don't extract product names from YouTube auto-subtitles by regex — auto-captions garble names. Use transcripts to cross-validate names already in your candidate list.
• Don't trust clusters of identical short positive comments under YouTube reviews — that pattern is affiliate astroturf. Skip the cluster.
• Don't include phantom competitors (Dunford) — theoretical competitors with only 1 source and no real-deal evidence dilute positioning. Rule: keep only names with ≥2 independent sources, or names explicitly named on the pitch deck (validate or invalidate them explicitly).
• Don't conflate rule-setters with competitors — bot defense (DataDome/Cloudflare/Akamai), app stores, payment processors, compliance tools, distribution platforms shape the rules but aren't substitutes. Note them in context, not in the main table.
• Don't include a complementor / ecosystem player as a competitor — a product that enhances yours (e.g. LangChain or n8n for a scraping API) doesn't compete; it expands the JTBD. Note in context.
• Don't exceed budget — top5 ~6 Anysite + 3 Exa; landscape ~10 Anysite + 4 Exa. After that, marginal signal drops fast.
• Don't use GitHub / Crunchbase / BuiltWith via Anysite right now — those sources are registered but their categories are not currently exposed via this MCP. For OSS-adjacent or funding-data signal, fall back to Exa search + fetch on the relevant GitHub or Crunchbase URL.

Example session — Anysite (top5 mode)

User: "Find real competitors for Anysite — API-first web data platform with 40+ sources + MCP server for AI agents. Pitch deck names Apify, Bright Data, ScrapingBee. DevTool / B2B niche. Top-5 mode."

1. mcp__claude_ai_Exa__web_search_exa(query="listicle ranking best web scraping APIs and AI agent data extraction platforms 2025 — detailed comparison with pricing and pros and cons", numResults=10)
   → Canonical set: Bright Data, Apify, Firecrawl, ScrapingBee, ScraperAPI, Oxylabs, Zyte, Diffbot, Octoparse, Browse AI, Thunderbit. Prices range $0 free → $500+/mo enterprise.

2. mcp__claude_ai_Anysite__execute(linkedin/search/search_posts, keywords="Firecrawl Bright Data web scraping", sort="relevance", count=25)
   → Jason Grad's "AI consensus" post (50 likes, 13K char trail): mentioned[] gives Bright Data, Zyte, ScrapingBee, Firecrawl, Scrape.do, ScraperAPI, Apify, Scrapingdog, Oxylabs.io, Scrapfly — 10 named competitors via LinkedIn URL in one call.

3. mcp__claude_ai_Anysite__execute(linkedin/search/search_companies, keywords="web scraping API agent data extraction", count=20)
   → 3 niche results (fastCRW, Getdataforme, Scrapeclaw) — long-tail watch.

4. mcp__claude_ai_Anysite__execute(reddit/search/search_posts, query="Firecrawl Apify web scraping AI agents", sort="relevance", time_filter="year", count=20)
   → "What Are the Best AI Web Scraping Tools in 2026?" (r/WebScrapingInsider, 11 votes, 45 comments), "8 Best Web Scraping Tools 2026: AI-Native Scrapers Compared" (r/Agent_AI, 38 votes).

5. mcp__claude_ai_Anysite__execute(reddit/search/search_posts, query="Bright Data alternative expensive", count=20)
   → "Bright Data is getting too expensive for failed requests" (r/WebScrapingInsider) — direct evidence of pricing pain.

6. mcp__claude_ai_Anysite__execute(youtube/search/search_videos, query="Apify Bright Data Firecrawl comparison review", count=15)
   → "Firecrawl AI clearly explained" (169K views, Greg Isenberg), "Apify vs Octoparse vs BrowseAI" head-to-head. Confirms Firecrawl + Apify as top anchors with view-count signal.

7. mcp__claude_ai_Anysite__execute(yc/search/search_companies, query="web scraping browser automation agent", count=30)
   → Browser Use (W25, 50K stars in 3 months), StableBrowse (S26, "browser for AI agents") — two convergence threats from the agentic-browser side.

8. Synthesize 4-category map (Direct / Substitute / Workaround / Convergence).

Example session — Anysite (landscape mode)

User: "Map the full web-data-API landscape, including long-tail and AI-agent disruptors. I'm raising — investor wants the competitive-landscape view."

1-3. Same as top5 mode Steps 1-3.

3.5. mcp__claude_ai_Anysite__execute(linkedin/search/search_posts, keywords="Bright Data Apify Firecrawl ScrapingBee", count=25)
     Walk mentioned[] across cached posts, tally @linkedin_search_post_mentioned_company.name. (query_cache group_by handles single-level fields only — aggregate nested array client-side.)
     → 12+ unique names; landscape long-tail surfaces.

4. mcp__claude_ai_Anysite__execute(youtube/search/search_videos, ...) — same as top5.

4a. mcp__claude_ai_Anysite__execute(youtube/video/video_subtitles, video="eH8JdttKIdA") on Firecrawl explainer
    → Full transcript; cross-validates Firecrawl's positioning vs Apify and Bright Data.

4b. mcp__claude_ai_Anysite__execute(youtube/video/video_comments, video="eH8JdttKIdA", count=30)
    → Genuine: "dont use the firecrawl agent, it sucks"; affiliate cluster: spotted and skipped.

5-7. Same as top5 Steps 5-7.

8. (Optional — SEC) No mature public incumbent yet in pure web-data API category; skip SEC. (Bright Data is private; ScrapeNetworks/Brightecore not public.)

9. Synthesize with 4 categories. Suggested axes for positioning-map:
   Axis A: Pricing predictability (credit-multiplier surprise → records-priced → flat-rate)
   Axis B: Agent-native vs developer-native (structured endpoints + MCP → raw proxy/Actor + bring-your-own-schema)

Final table:

#	Name	Category	Evidence	Source
1	Firecrawl	Direct (AI-agent-first)	Top 3 in 6/8 Exa listicles; 119K GitHub stars; named "best for LLM pipelines" everywhere	Exa, LinkedIn (50-like consensus post), Reddit, YouTube (169K-view explainer)
2	Apify	Direct (full-stack platform)	"Best default 2026" per 4 comparison blogs; 20K+ Actor marketplace	Exa, LinkedIn, Reddit (Apify-cost-math thread)
3	Bright Data	Direct (enterprise proxies + APIs)	355 employees; #1 in AI-search-consensus rankings; $500/mo entry	Exa, LinkedIn, Reddit (cost-pain thread)
4	ScrapingBee	Direct (developer API)	Named in every listicle; $49/mo entry; recurring "API simplicity" theme	Exa, LinkedIn
5	"Build it yourself: Crawl4AI + LLM key + proxies + maintenance"	Workaround / "doing nothing" (Dunford)	Dominant Reddit + blog cost-comparison pattern	Reddit, Exa (3 cost-comparison blogs)
—	Browser Use, StableBrowse, Parallel Web Systems, Tavily, Scrapling	Convergence threat (Porter "new entrants")	Agentic-browser / agent-native-API disruption from below; Browser Use 50K stars in 3 months; Parallel Web Systems $100M Series B at $2B	YC (2 hits), LinkedIn (2K-like Scrapling launch), Exa
—	DataDome, Cloudflare, Akamai	Rule-setter (anti-bot defense)	They set the anti-bot bar; not substitutes but shape every roadmap	LinkedIn (DataDome's own content)
—	LangChain, LlamaIndex, n8n, Zapier	Complementor	Every scraper integrates with these; they expand the JTBD, don't compete on it	Exa (every comparison blog)

So what:

• Pitch deck named Apify/Bright Data/ScrapingBee but missed Firecrawl as the closest direct threat in messaging (Firecrawl is "AI agent–native" by branding).
• The dominant workaround is "Crawl4AI + your own LLM key + proxies + your own maintenance" — explicitly costed out in multiple comparison posts.
• Browser Use and StableBrowse are convergence threats one feature-pivot away; the pitch deck should add them.