enterprise deep research

deep research apis are moving from novelty to infrastructure. if you're evaluating them for production use, here's what actually matters beyond the demos.

the adoption curve

most teams follow a predictable path:

  1. exploration — run the api on interesting queries, be impressed by outputs
  2. integration — wire it into a workflow, ship something
  3. disillusionment — hit rate limits, discover hallucinations, realize citations need verification
  4. maturity — build instrumentation, establish quality baselines, treat it as infrastructure

the teams that skip step 3 are the ones that get burned. plan for disillusionment.

what to evaluate

latency profile

deep research isn't fast. expect 30 seconds to 10+ minutes per query depending on the provider and complexity. this has workflow implications:

  • async job queues, not synchronous api calls
  • user expectations need to be set (progress indicators, estimated wait times)
  • timeout handling and retry logic become critical

the index tracks latency ranges. the variance is massive.

rate limits and quotas

every provider has them. most don't publish them clearly. you'll discover them in production. questions to answer before committing:

  • requests per minute/hour/day?
  • token limits per request?
  • concurrent request limits?
  • what happens when you hit them — queue, error, or degraded response?

output structure

some providers return structured json with citations in a predictable schema. others return markdown blobs you have to parse. this affects:

  • how much post-processing you need
  • whether you can reliably extract citations for verification
  • how easily outputs integrate into downstream systems

cost model

pricing models vary significantly:

  • per-request flat fee
  • per-token (input + output)
  • per-search (underlying web queries)
  • hybrid combinations

run your expected query volume through each model. the cheapest per-request option might be the most expensive at scale if queries are long.

operational requirements

monitoring

instrument everything:

  • request latency distributions
  • error rates by error type
  • citation resolution rates
  • output quality scores (even if sampled)

you need baselines to detect degradation.

fallbacks

what happens when your primary provider is down or rate-limited?

  • secondary provider with automatic failover?
  • graceful degradation to cached results?
  • user-facing error messaging?

design this before you need it.

compliance and data handling

if you're in a regulated industry:

  • where does query data go?
  • is there a data processing agreement available?
  • can you get audit logs?
  • what's the data retention policy?

most providers are still catching up here. ask explicitly.

the honest tradeoffs

no provider is best at everything. you're choosing between:

  • speed vs depth — faster responses often mean shallower research
  • cost vs quality — cheaper providers cut corners somewhere
  • structure vs flexibility — more structured outputs may be less comprehensive
  • coverage vs accuracy — more sources cited doesn't mean better sources

the index tries to make these tradeoffs visible. use it to match providers to your specific requirements.

deep research apis are powerful but not magic. treat them as infrastructure, not oracles. build verification into your workflows. plan for failure. the teams that do this will get far more value than the ones chasing impressive demos.