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Instagram Data, Delivered

Public Instagram content and engagement metrics, normalised to the same schema as every other platform we cover and pushed into your stack. Nothing to build, nothing to maintain.

source specification

Source
instagram.com
Objects
Posts and Reels, Public profiles, Hashtag feeds, Comment threads, Engagement metrics
Formats
JSON · CSV · Parquet
Refresh
Continuous to daily
Scope
Public content only

about the platform

What makes Instagram different

Instagram is where products are discovered before they are searched for. The platform is visual first, but the commercially useful signal sits in the layer around the image — the caption, the hashtag set, the location tag, and above all the comment thread, where the questions people ask about a product are the questions your category is actually being evaluated on. Reels added a second, faster-moving surface on top of the original grid, and the two behave differently enough that treating them as one feed loses information. A brand presence here is effectively a public catalogue with an attached focus group.

why teams track it

Why Instagram data is worth having

For consumer brands, Instagram is the closest thing to a live read on how a product is perceived at the moment of discovery, months before that perception shows up in search volume or sales data. Brand teams use it to track share of voice against competitors, creator teams use follower and engagement history to price partnerships against real reach rather than a media kit, and trend forecasters use hashtag velocity to catch a category shift while it is still cheap to act on.

Visit instagram.com

applications

What teams build on Instagram data

Influencer vetting against real numbers

A media kit reports the follower count. The engagement history reports whether those followers exist. Pulling a creator’s last few hundred posts shows the engagement-to-follower ratio over time, and a flat follower line with a sudden step change is the shape audience purchasing makes.

Product discovery and trend forecasting

Hashtag and caption velocity in a category moves ahead of search volume, because people post about a product before they know what to call it. Tracking the rise of a term here gives a lead time that keyword tools cannot.

Competitive share of voice

Mention volume, engagement weight, and creator overlap against a named competitor set, measured continuously instead of reconstructed once a quarter from a report.

Comment mining for product objections

The comment thread under a product post is where sizing, quality, shipping, and price complaints get aired publicly. That is unprompted voice-of-customer data on the exact SKU it refers to.

Campaign measurement across creators

Every post carrying a campaign hashtag, with its engagement envelope and posting time, collected into one table — so campaign reporting stops depending on each creator sending screenshots.

Visual training corpora

Public image and video posts with their captions attached form paired image-text data, which is the format multimodal training and evaluation work actually needs.

what you get back

Fields in the Instagram feed

Platform-specific detail on top of the unified social schema, so a dashboard built on one platform works on the next without a rewrite.

  • Post and Reel captions, hashtags, and mention lists
  • Like, comment, save, share, and view counts where exposed
  • Public profile attributes — follower and following counts, bio, category, external link
  • Comment threads with author handle, timestamp, and reply nesting
  • Media type and carousel item count per post
  • Location tags where the poster attached one

Public surfaces only

We collect what a logged-out visitor can see. No authentication is bypassed, no private content is touched, and no attempt is made to deanonymise anyone.

One schema across platforms

A post from any network arrives with the same field names, so adding a platform does not mean rewriting anything downstream.

Deletion propagates

When content is removed at source it drops out of subsequent deliveries, and retention windows on delivered data are configurable per engagement.

10+
Platforms covered
<60s
Detection latency
3
Delivery formats
99.9%
Uptime SLA

questions

Frequently Asked Questions

Everything you need to know before you send us your first request.

Both, and most engagements use a combination. You supply a set of handles — your own, your competitors’, and the creators you work with — plus the hashtags and brand terms that matter in your category, and collection is scoped to those. Adding or removing a term is a configuration change during the engagement, not a new build.
No. Stories are ephemeral by design and expire within twenty-four hours, and treating them as a durable dataset misrepresents what they are. We collect the permanent public surfaces — grid posts, Reels, profiles, and comments — which is what supports historical comparison anyway.
For a public account, the collectable history is what the profile still exposes, which for most accounts is the full posting archive. Backfill depth is confirmed per handle during scoping rather than promised in the abstract, because a profile that has deleted its older posts genuinely has no history to recover.
Never. Collection is limited to what a logged-out visitor can see. Private accounts, follower-gated posts, and direct messages are out of scope entirely — no authentication is bypassed and no login-walled surface is touched.
Into the systems you already run. Normalised records land as JSON, CSV, or Parquet in S3, GCS, or Azure Blob, or straight into Snowflake or BigQuery, on whatever cadence you set. Webhooks push matches to your endpoints as they are detected, which is how most customers wire alerting. A solutions engineer sets the schema, cadence, and destination with you during onboarding.
Keeping the collector working is our responsibility. Extractors are monitored continuously, and when the platform changes its structure we patch upstream while the schema we deliver to you stays fixed. If a change causes a coverage gap we tell you which window was affected rather than quietly returning fewer records.
Pricing is scoped per engagement — the number of tracked terms, handles, or communities, the refresh cadence, and the delivery destinations involved. Historical backfill is quoted separately. Every engagement starts with a free sample run against your own brand terms, so you can judge the data before committing.

Still have questions?

Talk to an engineer

Ready to Get Started?

Talk to us about your sources and volume. We'll return a sample dataset from your target sites before you commit to anything.

SOC 2 Type II
GDPR & CCPA compliant
99.9% uptime SLA

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