AI take on Snorkel AI
As of August 2026, Snorkel stands out for environment-first agent evaluation, though buyers must validate scope, timing, and commercial terms through sales.
AI Infrastructure & APIs comparison · 2026
Snorkel AI vs Zep
Compare Snorkel AI and Zep as AI Infrastructure & APIs tools on fit, pricing, and the capabilities that actually overlap in 2026. Pick Snorkel AI for teams building post-training and RL datasets for coding agents evaluated on senior-level software engineering tasks and constructing runnable environments so computer-use agents can be trained and scored on long-horizon desktop and web workflows. Pick Zep for teams giving a production customer-support agent persistent memory of a user's prior issues and stated preferences and fusing CRM, billing and product-event JSON into a single per-user graph an agent can query.
Updated Aug 23, 2026
The frontier AI data lab building expert training data, benchmarks, and runnable evaluation environments
Starts at Custom pricing (quote-based)·AI Infrastructure & APIs
Agent memory at enterprise scale, built on temporal context graphs
Starts at $0/month·AI Infrastructure & APIs
At a glance
Snorkel AIZep
CategoryAI Infrastructure & APIsAI Infrastructure & APIs
PricingStarts at Custom pricing (quote-based)Starts at $0/month
Free tierNoYes
PlatformsWebWeb
Suitable forTeams building post-training and RL datasets for coding agents evaluated on senior-level software engineering tasks and constructing runnable environments so computer-use agents can be trained and scored on long-horizon desktop and web workflowsTeams giving a production customer-support agent persistent memory of a user's prior issues and stated preferences and fusing CRM, billing and product-event JSON into a single per-user graph an agent can query
CompanySnorkel AI, Inc.Zep AI, Inc.
Founded20192023
How they differ
Shared AI Infrastructure & APIs rubric, filled from each listing. Not a score.
Model catalog
Snorkel AIZep
Text modelsNot publishedNo
Image modelsNot publishedNo
Video modelsNot publishedNo
Speech modelsNot publishedNo
Open-weight modelsNot publishedNo
Pinned versionsYesNot published
Serving
Snorkel AIZep
One unified APINot publishedNo
OpenAI-compatible APINot publishedNo
Streaming responsesNot publishedNot published
Batch jobsNot publishedNot published
Provider fallbackNot publishedNot published
Published rate limitsNot publishedNot published
Build & tune
Snorkel AIZep
Fine-tuningLimitedNot published
EmbeddingsNot publishedNot published
Vector storeNot publishedNot published
RAG pipelinesYesLimited
Agent frameworkYesNo
Tool / function callingNot publishedLimited
Observe & control
Snorkel AIZep
Usage dashboardNot publishedYes
Per-request costsNot publishedNot published
Traces / loggingYesYes
EvalsYesNo
Prompt managementNot publishedNot published
Zero-retention optionNot publishedLimited
Access
Snorkel AIZep
Public APINot publishedYes
Official SDKNot publishedYes
Self-serve signupNoYes
Free trialNot publishedNot published
Team workspaceNot publishedNot published
SSO / SAMLNot publishedNot published
Mobile appsNoNo
Browser extensionNot publishedNo
Self-host / on-premNot publishedYes
Commercial
Snorkel AIZep
Commercial licenseNot publishedNot published
Usage-based pricingNot publishedNot published
Invoice / PONot publishedNot published
SOC 2Not publishedYes
GDPR / DPANot publishedNot published
Audit logYesYes
Role-based accessNot publishedLimited
Features
These listings describe different capabilities. What each one ships:
Only Snorkel AI
- Snorkel Data Series: curriculum-structured datasets with rubrics, reviewer guidance, difficulty tiers, and eval slices
- Custom data development for bespoke datasets, evals, and benchmark expansions targeting a named failure surface
- Specialized agents built on expert data and evaluated in real workflows with pass/fail criteria
- Well-specified expert-level task specs with target distributions, acceptance criteria, and verifier definitions written before data work begins
- Calibrated expert review, with reviewers trained against gold sets authored by Snorkel researchers and scored for agreement and bias
- Rubrics distilled into programmatic graders and fine-tuned evaluator models rather than human spot-checks alone
- Author, multi-reviewer, and final-adjudicator pipeline with full audit trails and label-level provenance
- Edge-case coverage across difficulty bands and failure modes via expert-authored seeds and templated generation
Only Zep
- Temporal context graphs that record facts with a validity window
- Fact invalidation when new information contradicts the graph, with old facts kept as history
- Point-in-time queries: ask what is true now or what was true on a past date
- Multi-source ingest from chat history, business data (JSON) and user interactions
- Automated context assembly returning token-efficient context blocks
- Observations: patterns, recurrences and co-occurrences detected across the graph
- Provenance on every fact, traced back to the source episode
- Context Lake architecture governing millions of context graphs as one system
Use cases
These listings describe different use cases. What each one ships:
Only Snorkel AI
- Building post-training and RL datasets for coding agents evaluated on senior-level software engineering tasks
- Constructing runnable environments so computer-use agents can be trained and scored on long-horizon desktop and web workflows
- Designing rubrics and programmatic graders for open-ended outputs where correctness is hard to define
- Standing up environment-first evaluation for agents in regulated work such as insurance underwriting or financial reasoning
- Sourcing legal research evaluation data through partnerships like BigLaw Bench: Research with Harvey
- Deploying a specialized enterprise agent grounded in a company's own tools, codebase, corpus, and data permissions
- Diagnosing why an evaluation rubric breaks down using the RIFT failure mode taxonomy from Snorkel AI research
Only Zep
- Giving a production customer-support agent persistent memory of a user's prior issues and stated preferences
- Fusing CRM, billing and product-event JSON into a single per-user graph an agent can query
- Answering point-in-time questions such as what a customer's plan or preference was on a specific date
- Reducing prompt token spend by replacing full chat transcripts with an assembled context block
- Auditing why an agent said something by tracing the underlying fact back to its source episode
- Detecting behavioural patterns, such as repeat upgrade timing, and feeding them to an agent as Observations
- Running agent memory inside a regulated customer's own VPC via Bring Your Own Cloud
- Exposing agent memory to IDEs and assistants through the Memory MCP Server
Plans
Snorkel AI
Custom engagement (quote-based) Contact sales
No pricing page is published on snorkel.ai and no list price appears on the homepage, company page, or llms.txt · Homepage calls to action are "Request dataset samples" and "Talk to our team" rather than a signup or free trial · Scope is negotiated per dataset, benchmark, evaluation harness, environment, or specialized agent program · Snorkel Data Series, custom data development, and specialized agents are all sold through the same sales conversation · Free resources exist outside the commercial contract, including public benchmark leaderboards, research papers, and the Open Benchmarks Grants program
Zep
Free $0/mo or $0/yr
10,000 credits per month (no rollover or auto top-up) · 2 projects · Memory MCP Server seat · Custom entity/edge types · Variable rate limits depending on service-wide load · Lower priority Episode processing · Community support
Flex $125/mo or $1250/yr
50,000 credits per month included, then $25 per 10,000 credits · Auto top-up at 20% (10,000 credits / $25) · 30-day credit rollover · 600 requests per minute · 5 projects · 5 Memory MCP Server seats · 10 custom entity/edge types · API logs retained 1 day · Unlimited memories and retrieval users · Community support · Cloud deployment
Flex Plus $375/mo or $3750/yr
200,000 credits per month included, then $75 per 40,000 credits · Auto top-up at 20% (40,000 credits / $75) · 60-day credit rollover · 1,000 requests per minute · 10 projects · 15 Memory MCP Server seats · 20 custom entity/edge types · Observations, custom extraction instructions, webhooks, analytics · API logs retained 7 days · Unlimited memories and retrieval users · Priority support
Snorkel AI strengths
- Snorkel AI publishes its benchmarks and leaderboards openly, including Senior SWE-Bench and OSWorld 2.0, so buyers can inspect the methodology before contracting
- Snorkel AI documents a concrete quality process covering task specs, reviewer calibration, adjudication, and label-level provenance rather than vague quality claims
- Snorkel AI delivers runnable environments and deterministic graders alongside data, so evaluation is reproducible across model versions
- Snorkel AI has a peer-reviewed research trail dating to the 2017 VLDB data programming paper, with 250+ publications cited on the company page
- Snorkel AI covers coding, computer use, terminal, legal, financial, and insurance domains rather than one narrow vertical
- Snorkel AI maintains a published llms.txt that lays out capabilities, benchmarks, and research links in a structured, checkable form
Watch-outs
- Snorkel AI publishes no pricing page, no plan table, and no list price anywhere on snorkel.ai, so cost cannot be estimated before a sales call
- Snorkel AI offers no self-serve signup, free tier, or free trial; the only homepage entry points are "Request dataset samples" and "Talk to our team"
- Vendor contradiction: the Snorkel AI contact page directs users to [email protected] for help using Snorkel, while section 2.4 of the Snorkel AI website terms states the company has no obligation to provide support or maintenance
- The Snorkel AI website terms grant only a personal, noncommercial license to the site and forbid using the site to build a similar or competitive product
- The Snorkel AI website terms cap total liability arising from site use at fifty US dollars (US $50) and require individual binding arbitration with a class action waiver
- The Snorkel AI website terms are still labeled Version 1.0, last updated July 11, 2020, which predates the current frontier-data positioning on the site
- Snorkel AI works through an embedded delivery model with recruited domain experts, so turnaround depends on expert calibration rather than instant API access
- Snorkel AI states no minimum engagement size, dataset volume, or contract length publicly, leaving small teams unable to judge fit without contacting sales
Zep strengths
- Zep publishes concrete p95 latency figures across graph sizes (148ms at 10K to 168ms at 100M) rather than a vague speed claim
- Z
- Temporal context graphs that record facts with a validity window
- Fact invalidation when new information contradicts the graph, with old facts kept as history
- Point-in-time queries: ask what is true now or what was true on a past date
- Multi-source ingest from chat history, business data (JSON) and user interactions
- Automated context assembly returning token-efficient context blocks
- Observations: patterns, recurrences and co-occurrences detected across the graph
Watch-outs
- Zep plan limits come from the published pricing table.
- Zep packaging is recorded from the vendor pages available at analysis time.
Snorkel AI vs Zep verdict
Who each product is for, then labeled AI takes. Not a generic winner.
Bottom line
Pick Snorkel AI for teams building post-training and RL datasets for coding agents evaluated on senior-level software engineering tasks and constructing runnable environments so computer-use agents can be trained and scored on long-horizon desktop and web workflows. Pick Zep for teams giving a production customer-support agent persistent memory of a user's prior issues and stated preferences and fusing CRM, billing and product-event JSON into a single per-user graph an agent can query.
Who Snorkel AI is for
Teams building post-training and RL datasets for coding agents evaluated on senior-level software engineering tasks and constructing runnable environments so computer-use agents can be trained and scored on long-horizon desktop and web workflows
The frontier AI data lab building expert training data, benchmarks, and runnable evaluation environments
Who Zep is for
Teams giving a production customer-support agent persistent memory of a user's prior issues and stated preferences and fusing CRM, billing and product-event JSON into a single per-user graph an agent can query
Agent memory at enterprise scale, built on temporal context graphs
AI take on Zep
As of August 2026, Zep stands out where agent memory must be temporal, governed, and auditable rather than a thin wrapper around transcript retrieval.
Snorkel AI vs Zep FAQ
Common questions when choosing between Snorkel AI and Zep.
Is Snorkel AI or Zep the better AI Infrastructure & APIs tool?
Pick Snorkel AI for teams building post-training and RL datasets for coding agents evaluated on senior-level software engineering tasks and constructing runnable environments so computer-use agents can be trained and scored on long-horizon desktop and web workflows. Pick Zep for teams giving a production customer-support agent persistent memory of a user's prior issues and stated preferences and fusing CRM, billing and product-event JSON into a single per-user graph an agent can query.
Which is cheaper, Snorkel AI or Zep?
Snorkel AI starts at Custom pricing (quote-based). Zep starts at $0/month. Confirm current pricing on each vendor site.
Who should choose Snorkel AI?
Teams building post-training and RL datasets for coding agents evaluated on senior-level software engineering tasks and constructing runnable environments so computer-use agents can be trained and scored on long-horizon desktop and web workflows
Who should choose Zep?
Teams giving a production customer-support agent persistent memory of a user's prior issues and stated preferences and fusing CRM, billing and product-event JSON into a single per-user graph an agent can query
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