--- title: "What SLA Terms Should You Look for in a B2B Data API Contract?" description: "API service credits cover 10% of your fee. Downtime costs $50K+ in misrouted leads. Learn what SLA terms to negotiate for production agent workloads." canonical: "https://www.explorium.ai/blog/business-data/what-sla-terms-should-you-look-for-in-a-b2b-data-api-contract/" last-updated: "2026-05-05" --- # What SLA Terms Should You Look for in a B2B Data API Contract? > API service credits cover 10% of your fee. Downtime costs $50K+ in misrouted leads. Learn what SLA terms to negotiate for production agent workloads. - Canonical URL: https://www.explorium.ai/blog/business-data/what-sla-terms-should-you-look-for-in-a-b2b-data-api-contract/ - Last updated: 2026-05-05 ## Q1. What SLA Terms Should You Look for in a B2B Data API Contract? Score any B2B data API vendor contract against these 8 SLA categories before you sign. If more than two are missing, your production agents are running unprotected. ### ☐ The 8 Non-Negotiable SLA Categories Most SLA guides treat this as a generic SaaS checklist. But when you're powering AI agents that run enrichment loops at 2 AM with no human in the loop, the stakes are different. Here's what I'd look for, and what I'd push back on if it's missing. #### ☐ Uptime Guarantee ≥99.9% with 24/7 Measurement Not business-hours-only. Not excluding "scheduled maintenance." Your agents don't sleep, so the uptime clock shouldn't either. #### ☐ Latency SLA with P50/P95/P99 Percentile Targets Average latency is a vanity metric. Demand percentile-based guarantees because a 3-second P95 means 1 in 20 [enrichment calls](https://www.explorium.ai/data-enrichment/introduction-to-data-enrichment/) is slow enough to timeout your agent chain. #### ☐ QPM/Rate-Limit Guarantee for Multi-Agent Orchestration 50 RPM might work for a single user clicking through a UI. It doesn't work for three agents sharing one API key during a batch enrichment run. Know the sustained limit and the burst allowance. #### ☐ Data Freshness Commitment (Documented Refresh Cadences) "We keep data up to date" isn't an SLA. Documented cadences, daily for events, weekly for [firmographics](https://www.explorium.ai/business-data/firmographics/), monthly for [technographics](https://www.explorium.ai/business-data/technographic-data/), are. #### ☐ Match Rate or Field-Fill-Rate SLA This is the one most vendors won't give you. If 40% of enrichment calls return null on critical fields, your agent is making decisions on incomplete data. Push for written match-rate floors. #### ☐ Service Credit Structure with Automatic Credits If you have to file a claim within 30 days and argue with support to get a 10% credit on your monthly fee, that's not a remedy. It's a deterrent designed to prevent you from claiming. #### ☐ Support Response Time Tiers (P1 ≤15 min, P2 ≤1 hr) When your pipeline stalls at 3 AM, an email queue with a 24-hour SLA is functionally the same as no support at all. #### ☐ Clearly Defined Exclusions List Maintenance windows, force majeure, third-party dependencies, "partial degradation": if these aren't explicitly defined and capped, they'll be used to avoid every credit you're entitled to. ### 📊 How to Score Your Current Contract Score What It Means ✅ 7–8 checked Production-ready. Negotiate optimization and custom terms. ⚠️ 4–6 checked Critical gaps exist. Push back before signing. ❌ 0–3 checked Not production-grade. Your agents are exposed. ### How Explorium Covers These Categories We built [Explorium's API contracts](https://www.explorium.ai/our-product/) for production AI workloads from the ground up: documented uptime, published latency benchmarks (200–500ms P50, sub-2s P95), transparent QPM tiers starting at 200 RPM, and data freshness cadences across 50+ sources. Most [GTM engineering teams](https://www.explorium.ai/use-case/gtm-engineering/) find every checkbox covered without extended negotiation. > "Instead of connecting to multiple data sources and APIs, we only require one connection, Explorium!" — Mirit H., Mid-Market [***Explorium G2 - Verified Review***](https://www.g2.com/products/explorium/reviews/explorium-review-4522137) Scored your current vendor below 6? Each section below breaks down exactly what each SLA term should look like, and what minimums to demand for production agent workloads. ## Q2. What Does 99.9% Uptime Actually Mean for a B2B Data API? 99.9% uptime means your B2B data API can be unavailable for up to 8 hours and 46 minutes per year, or roughly 43 minutes per month. For production AI agents making continuous enrichment calls overnight, that's enough downtime to stall an entire pipeline cycle. And the real number is often worse than the SLA suggests. ### ⏰ The Uptime-to-Downtime Math SLA Level Annual Downtime Monthly Downtime Weekly Downtime 99% ("two nines") 3 days, 15 hrs 7 hrs 18 min 1 hr 41 min 99.9% ("three nines") 8 hrs 46 min 43 min 50 sec 10 min 5 sec 99.95% 4 hrs 23 min 21 min 55 sec 5 min 2 sec 99.99% ("four nines") 52 min 36 sec 4 min 23 sec 1 min 0 sec 99.999% ("five nines") 5 min 16 sec 26 sec 6 sec That table looks clean on paper. Here's where it breaks in practice: - Scheduled maintenance exclusions can erase 2–4 hours of "claimed" uptime per month. Some vendors carve out 4–8 hours of maintenance that simply doesn't count against SLA. - Business-hours-only measurement: your agents run 24/7, but some vendors only measure uptime during 9-to-5 windows. Weekend and overnight outages? Invisible. - Partial degradation loopholes: if the API returns any response (even errors or 15-second timeouts), it may not count as "downtime" under their definition. According to the Uptrends State of API Reliability 2025 report, average API uptime actually fell from 99.66% to 99.46% year-over-year, a 60% increase in downtime, translating to an additional 90 minutes of outage every month across the industry. #### 🔧 What This Means for Agent Pipelines When a human user hits an error, they refresh and try again. When an AI agent enriching 5,000 leads encounters 43 minutes of downtime mid-loop, the pipeline either stalls entirely or, worse, silently passes unenriched records downstream, corrupting every scoring and routing decision that follows. #### How Explorium's Architecture Changes the Equation We built Explorium's infrastructure to power [always-on agent workflows](https://www.explorium.ai/building-ai-agents/building-scalable-ai-agents-and-enterprise-solutions-a-deep-dive-into-data-quality-and-infrastructure/), not human-triggered, business-hours-only queries. With 50+ underlying [data sources](https://www.explorium.ai/data/), downtime in one upstream provider doesn't cascade into your agent's pipeline. The platform routes through alternative sources. That's structural reliability, not just a contractual promise. > "Explorium gives us the data I need when I need it. This saves us a lot of time and money instead of managing each data source separately." — Ishi N., Enterprise [***Explorium G2 - Verified Review***](https://www.g2.com/products/explorium/reviews/explorium-review-4553554) > "Contact info frequently missing or incorrect. Half the day calling wrong/disconnected numbers. Mobiles frequently wrong." — Verified User, IT Services, Mid-Market [***Apollo - G2 Verified Review***](https://www.g2.com/products/apollo-io/reviews/apollo-io-review-10761677) ## Q3. What Is the Minimum Uptime Guarantee You Should Demand for Production AI Agent Workloads? When a human clicks "enrich" and gets an error, they retry in five minutes. When an AI agent processing 10,000 leads encounters downtime, the entire pipeline stalls, or worse, it silently fails and passes unenriched records downstream where they corrupt lead scores, routing decisions, and outbound sequences. Production AI agents demand fundamentally different uptime guarantees than traditional API consumers. #### ❌ Why Traditional SLAs Fall Short for Agents Most B2B data API contracts were written for human-scale usage. A 99% uptime SLA, which sounds reasonable on a sales call, permits over 7 hours of downtime per month. For an agent running [enrichment loops](https://www.explorium.ai/explorium-guides/how-explorium-upgrades-your-data-pipeline/) overnight, that means an entire batch cycle can fail before anyone notices. The problem compounds with single-source providers. When Apollo's API goes down, your enrichment is fully down. There's no failover, no alternative path, no partial result. Same with PDL. You have a single point of failure for every enrichment call your agent makes. > "Data inaccuracies lead to negative outcomes. Wrong personnel details, private employee info listed as company contacts, misdirected communications." — Anders J., Developer, Small-Business [***Apollo - G2 Verified Review***](https://www.g2.com/products/apollo-io/reviews/apollo-io-review-9872618) #### ✅ The Three Additions That Make 99.9% Actually Mean 99.9% The minimum for production agent workloads is 99.9% measured uptime. But the number alone isn't enough. Negotiate these three critical additions: - **Maintenance windows must count against uptime,** or be capped at ≤15 minutes per month. If a vendor excludes 4 hours of "scheduled maintenance," your real SLA is closer to 99.4%. - **Partial degradation must count as downtime.** If responses exceed 2x normal latency or error rates climb above 1%, that's functionally down for an agent pipeline even if the API technically returns responses. - **Measurement must be 24/7 with independent monitoring rights.** Your agents don't work banker's hours. Neither should the uptime clock. Insist on the right to use third-party monitoring tools as the source of truth. For mission-critical pipelines, such as [lead scoring](https://www.explorium.ai/explorium-guides/identify-your-icp-and-prioritize-optimal-leads/), real-time inbound qualification, and automated outreach, the real target is 99.95% or higher. #### How Multi-Source Architecture Changes the Uptime Conversation This is where data API architecture matters more than the SLA document. [Explorium's aggregation layer](https://www.explorium.ai/our-product/) routes enrichment requests across 50+ underlying providers. When one upstream source experiences issues, the platform fulfills your request through alternative paths. Your agent's enrichment call has 50+ potential fulfillment routes, turning vendor redundancy from your engineering team's problem into the platform's built-in capability. Single-source APIs have a single point of failure. Aggregated multi-source APIs have structural resilience. That distinction matters more for effective uptime than any contractual percentage. ## Q4. What Latency SLA Should a B2B Data API Guarantee for Real-Time Enrichment? Latency SLA defines how fast a [B2B data API returns enrichment results](https://www.explorium.ai/data-enrichment/introduction-to-data-enrichment/), but average latency is the wrong metric to negotiate around. Production agent workloads need percentile-based guarantees because a single slow response in a 10,000-record enrichment loop can cascade into pipeline timeouts, credit waste, and stale data delivery. ### 📊 Understanding P50, P95, and P99 If a vendor tells you "average response time is 500ms," that number hides the tail. Here's what percentiles actually measure: Percentile What It Means Why It Matters for Agents P50 (median) Half of all requests are faster than this Your "normal" experience, what most enrichment calls feel like P95 Only 5% of requests are slower The number that determines whether your agent pipeline runs inline or needs async fallback P99 Only 1% of requests are slower The worst-case scenario that triggers timeouts and cascading failures in multi-step chains A 500ms P50 with a 5-second P95 means 1 in 20 calls takes 10x longer than expected. In a 5-step agent chain where each step calls enrichment, a 5-second P95 compounds to 25 seconds total, well past most pipeline timeout thresholds. #### ⚡ Benchmark Targets for B2B Data Enrichment Enrichment Pattern P50 Target P95 Target P99 Target Sync (single-record, real-time) ≤500ms ≤2s ≤5s Async (bulk, batch processing) N/A Batch completion within 15–60s for 100+ records — Cached/pre-computed lookups ≤200ms ≤500ms ≤1s Explorium's documented benchmarks sit at 200–500ms P50 and sub-2s P95 across enrichment endpoints. That's within the production-ready range for real-time agent workflows. #### What Fast Latency Actually Enables - ⚡ Real-time lead scoring: enrichment results return before the routing decision times out - ⚡ Meeting prep agents: attendee profiles enriched within the 3–5 second window before briefing generation - ⚡ Inbound qualification: form submissions processed with enriched data before the "thank you" page loads - ⚡ Multi-step agent chains: no accumulated latency debt across sequential enrichment calls #### Why This Is the Most Operationally Impactful Term After Uptime The difference between a 500ms P50 and a 3-second P50 is the difference between an agent that enriches inline and one that requires async queuing, retry logic, and timeout handling. Every millisecond compounds across thousands of daily enrichment calls. Think of latency percentiles like highway speed during rush hour. The average speed might be 60 mph, but if 5% of your trips take 45 minutes instead of 15, your entire schedule breaks. P95 and P99 guarantees protect your [agent pipeline](https://www.explorium.ai/building-ai-agents/the-lifecycle-of-data-in-agent-development/) from those tail-end slowdowns that averages conveniently hide. > "No latency issues and great performance considering the breadth of data calculations we're working with. Great team of engineers helped customize our solution." — Gartner Verified Review, 5.0/5.0 [***Explorium - Gartner Peer Insights***](https://www.gartner.com/reviews/market/data-preparation-tools/vendor/explorium/product/explorium-external-data-platform/review/view/3589462) ## Q5. What's the Real Cost When Your B2B Data API Goes Down? Your data enrichment API went down for 3 hours last Tuesday, between 11 PM and 2 AM. No big deal, right? Your inbound [lead scoring agent](https://www.explorium.ai/explorium-guides/using-explorium-to-score-and-export-data/) processed 800 new signups during that window with zero enrichment. It scored them all as "low priority" because it had no [firmographic data](https://www.explorium.ai/business-data/firmographics/) to evaluate ICP fit. By Wednesday morning, your SDR team skipped 47 accounts that were actually enterprise-qualified leads, 12 of which matched your ideal customer profile perfectly. Two of those accounts signed with a competitor within the month. The API was down for 3 hours. The revenue impact lasted 90 days. ### 💰 Why Data API Downtime Has a Multiplier Effect Generic SaaS downtime calculators frame losses as "transactions per hour." That framing doesn't work for data enrichment. Enrichment downtime has a cascade effect because stale or missing data doesn't pause your pipeline; it corrupts it. Three root causes drive the cascade: - **Unenriched records still get scored, routed, and actioned.** Your agent doesn't throw an error when enrichment returns null. It makes a decision with whatever data is available, which is none. - **Agents don't wait.** They process records as they arrive. Downtime doesn't create a backlog; it creates a batch of bad decisions that propagate through every downstream system. - **The cost isn't the API fee.** It's the compounding effect of bad decisions made with incomplete data during the outage window. #### ❌ The Downstream Cost Cascade - Stale enrichment → inaccurate lead scores → SDRs work the wrong accounts → 15–20 wasted hours per week of sales capacity - Missing firmographic data → ICP matching fails → enterprise leads routed to the SMB queue → lost deal velocity on your highest-value prospects - Zero [intent signals](https://www.explorium.ai/business-data/b2b-intent-data/) → outreach timing is random → 40–60% lower response rates on campaigns launched during outage windows - Incomplete records → agent hallucination → personalization errors in outbound sequences → brand damage at scale A $500/month data API with 4 hours of undetected downtime can cost $50K+ in misrouted leads and missed pipeline over the following quarter. The SLA isn't protecting your API bill; it's protecting your revenue pipeline. #### ✅ How to Protect Your Pipeline Three non-negotiables for any data API contract: - **Demand SLAs that define downtime broadly,** including partial degradation, elevated latency, and elevated error rates, not just "API returns no response." - **Require real-time status pages** with historical uptime dashboards. If your vendor can't show you a public uptime record, they're hiding something. - **Choose multi-source architectures** that fail over, not fail completely. We built [Explorium's aggregation layer](https://www.explorium.ai/our-product/) around this principle. When one of 50+ upstream providers experiences issues, enrichment continues through alternative sources. Your agent never operates with zero data. During rare provider-level incidents, you might see slightly reduced signal breadth, but the pipeline never goes dark. Cost-of-downtime math should be the first slide in every data vendor evaluation, not the last. ## Q6. How Do API Service Credits Work, and Why Are They Never Enough? Standard API service credits compensate 5–25% of your monthly fee for downtime that exceeds the SLA. If you're paying $500/month and experience a full day of downtime, you might get $125 back. As the previous section showed, the actual cost in misrouted leads and missed pipeline could be $50K+. Credits are a contractual gesture, not real compensation. ### ⚠️ How Credit Tiers Typically Work SLA Breach Level Typical Credit What It Actually Covers 99.9% missed (e.g., 99.5% actual) 10% of monthly fee A rounding error on your actual losses 99.0% missed 25% of monthly fee Still a fraction of downstream impact Below 99.0% 50% of monthly fee (rare) Often capped at one month's fee total #### ❌ The Five Credit Gotchas - **Credits capped at 30–50% of one month's fee.** Even catastrophic multi-day outages won't yield more than half a month's payment back. - **Claim-based, not automatic.** Most vendors require you to file a claim within 30 days. Miss the window, and you lose the credit entirely. - **"Downtime" narrowly defined.** Partial degradation, slow responses, and elevated error rates often don't qualify, even though they break your agent pipeline just as effectively. - **Credits apply to future invoices, not refunds.** You don't get money back; you get a discount on the next month of a service that just failed you. - **Credits don't count against liability caps.** Unless you negotiate otherwise, service credits are your only remedy. You can't claim additional damages. #### 💸 What to Negotiate Instead - **Automatic credits,** no claim required, applied the moment monitoring detects a breach - **Credits based on affected API calls,** not monthly fee percentage - **Termination rights** if SLA is breached in 2+ consecutive months, without early termination penalties - **Right to independent monitoring** as the contractual source of truth #### How Explorium's Pricing Model Changes the Math We built Explorium on a [credit-based pricing model](https://www.explorium.ai/credit-details/), one-time packages, not monthly subscriptions. You purchase credits, and you consume them per enrichment call. When enrichment fails or returns null, you don't consume credits. That's a fundamentally different compensation structure than getting a 10% discount on next month's recurring fee. Combined with multi-source architecture that reduces single-point-of-failure risk, the conversation shifts from *compensating for downtime* to *preventing it structurally*. The best SLA compensation is never needing to use it. > "Their product enables us to test multiple data sources and to save money by removing sources that have a poor benefit." — Verified User, Financial Services [***Explorium G2 - Verified Review***](https://www.g2.com/products/explorium/reviews/explorium-review-4509316) > "Per-row credit cost can vary 100% from stated amounts, e.g., stated 11 credits/row, actual 25. Contact data quality varies wildly; feels like a black box." — Verified User, IT Services, Mid-Market [***Clay - G2 Verified Review***](https://www.g2.com/products/clay-com-clay/reviews/clay-review-12029107) ## Q7. What Data-Quality SLA Terms Are Unique to B2B Data API Contracts? Every SLA guide on the internet covers uptime, latency, and credits. But for B2B [data enrichment](https://www.explorium.ai/data-enrichment/introduction-to-data-enrichment/) APIs, those are table stakes. The SLA terms that actually determine whether your agent pipeline produces accurate results are data-quality guarantees: match rate, field-fill rate, freshness cadences, schema stability, and accuracy benchmarks. Most vendors don't include these because they can't guarantee them. #### ❌ Why Traditional Vendors Avoid Data-Quality SLAs Traditional data providers hide behind volume claims, "200M+ contacts," "150M+ companies," without committing to accuracy on any individual enrichment call. Ask Apollo or PDL for a contractual match-rate guarantee, and the conversation stalls. Ask for documented field-fill rates across firmographic fields, and the answer is "it varies." This means your agent enriches records with no SLA on whether the data it receives is correct, complete, or current. > "Contact info frequently missing or incorrect. Half the day calling wrong/disconnected numbers. Mobiles frequently wrong." — Verified User, IT Services, Mid-Market [***Apollo - G2 Verified Review***](https://www.g2.com/products/apollo-io/reviews/apollo-io-review-10761677) > "Company data doesn't refresh often enough. Only 20% of known contacts could be found, including people at companies for 1+ year." — Verified User, Internet, Mid-Market [***Clearbit - G2 Verified Review***](https://www.g2.com/products/clearbit/reviews/clearbit-review-3559485) #### ✅ The 5 Data-Quality SLA Terms to Demand Term What It Means Target Benchmark **Match Rate** % of input records returning a successful enrichment match ≥85% company, ≥70% contact **Field-Fill Rate** % of returned records where key fields are populated, not null ≥80% on core firmographic fields **Data Freshness** Documented refresh cadences per data category Daily (events), weekly (firmographics), monthly ([technographics](https://www.explorium.ai/business-data/technographics/)) **Schema Stability** API response schemas won't change without versioned deprecation 90-day notice minimum before breaking changes **Accuracy Benchmarks** Published accuracy rates on critical fields with independent verification ≥95% on core company fields ### ⭐ Explorium's Published Accuracy Benchmarks We publish accuracy numbers and stand behind them. Here's how Explorium compares on independently verifiable firmographic fields: Field Explorium ZoomInfo Apollo Clearbit Experian Employee Count 97.8% 88.3% 78.2% 32.9% 65.5% Website URL 97.8% 89.6% 78.0% 54.0% 61.4% NAICS Code 97.3% 89.6% 69.3% 45.6% 77.6% This gap exists because aggregating and cross-referencing 50+ sources catches errors, fills gaps, and flags stale records that any single provider alone will miss. The [data quality](https://www.explorium.ai/explorium-guides/improve-data-quality-enrich-leads-salesforce-explorium/) SLA isn't a marketing claim; it's independently verifiable with your own test records. > "The richness and breadth of data is incredible. I really like the instant access to the most useful and reliable external data." — Ishi N., Enterprise [***Explorium G2 - Verified Review***](https://www.g2.com/products/explorium/reviews/explorium-review-4553554) ## Q8. What SLA Fine Print and Exclusions Should You Watch For? Your B2B data API went down for 6 hours last month. You file for service credits under your 99.9% SLA, only to learn that 4 of those hours were during a "scheduled maintenance window" that doesn't count, and the other 2 hours were classified as "degraded performance" (responses returned, just at 15x normal latency) which also doesn't qualify. Your contract says 99.9%. Your actual experience was 98.5%. The SLA held, on paper. ### ⚠️ The 5 Exclusion Red Flags #### 1. Maintenance Windows Carved Out of Uptime Some vendors exclude 4–8 hours per month of "scheduled maintenance" from SLA calculations. If your agent runs 24/7, those hours matter. Negotiate a hard cap: maintenance must count against uptime, or be limited to ≤30 minutes per month. #### 2. "Partial Outage" Loopholes If the API returns any response, even errors, timeouts, or garbage data, it may not count as "downtime" under the vendor's definition. For your agent, a 15-second response time is functionally identical to no response at all. #### 3. Force Majeure Broad Enough to Include Weather Force majeure clauses have expanded to cover "internet disruptions," "third-party provider issues," and "causes beyond reasonable control." If your data vendor's upstream source goes down and they invoke force majeure, your SLA is effectively void for the duration. #### 4. Customer-Caused Exclusions Exceeded a rate limit the vendor set too low? That's "customer-caused" downtime under many contracts, even if the throttling was the vendor's architectural limitation. #### 5. Vendor-Controlled Measurement If the vendor is the sole arbiter of what counts as downtime, using only their internal monitoring, there's no independent verification. Demand third-party monitoring rights as the contractual source of truth. #### 💰 The Hidden Costs Beyond Downtime - [Agent pipelines](https://www.explorium.ai/building-ai-agents/the-lifecycle-of-data-in-agent-development/) that silently degrade (elevated latency, partial data) without ever triggering an SLA breach - Engineering hours spent documenting outages for credit claims that ultimately get denied - False confidence from a "99.9% SLA" that delivers 98% effective availability after all exclusions are applied #### ✅ What to Negotiate Into Your Contract - **Maintenance capped at 30 min/month,** counted within uptime calculations - **"Downtime" redefined** to include >5x baseline latency or >2% error rate - **Force majeure limited** to genuinely unforeseeable events, not upstream provider outages that are a normal operational risk - **Independent third-party monitoring rights** written into the agreement #### Why Multi-Source Architecture Neutralizes Fine-Print Risk This is where Explorium's architecture provides a structural advantage. "Third-party provider failure" is the most commonly invoked SLA exclusion in data API contracts. But when your vendor IS the aggregation layer across [50+ sources](https://www.explorium.ai/data/), not a passthrough to a single upstream database, individual provider failures don't void your SLA because they don't take down your enrichment. The fine print reveals whether your vendor built for contracts or for production. Choose the one built for production. > "Depending on where the data is coming from, the data can often be mismatched or have outdated information. It is best to cross-reference the output data from other enriched information." — Omar G., Mid-Market [***Explorium G2 - Verified Review***](https://www.g2.com/products/explorium/reviews/explorium-review-4522910) > "Switched from free trial to paid plan ($100/month). After a few days, account disabled with no warning or explanation. Support unresponsive after multiple contact attempts." — Verified User, Computer Software, Mid-Market [***People Data Labs - G2 Verified Review***](https://www.g2.com/products/people-data-labs/reviews/people-data-labs-review-12260586) ## Q9. How Should Support Response Times Be Defined in a B2B Data API SLA? A production B2B data API SLA must define four support severity levels with committed response times, not "best effort." When your [AI agent pipeline](https://www.explorium.ai/building-ai-agents/building-scalable-ai-agents-and-enterprise-solutions-a-deep-dive-into-data-quality-and-infrastructure/) stalls at 3 AM because enrichment calls are returning errors, a 24-hour email response SLA is useless. Agents don't pause and wait for a support ticket to resolve. They keep processing records with whatever data is available, which means every hour without a fix is an hour of corrupted pipeline output. ### ⏰ The Four Severity Tiers You Should Demand Priority Definition Target Response Update Cadence Target Resolution **P1, Critical** API fully down; agents blocked ≤ 15 minutes Every 30 minutes ≤ 4 hours **P2, High** Significant degradation; elevated errors ≤ 1 hour Every 2 hours ≤ 8 hours **P3, Medium** Non-critical issue; workaround available ≤ 4 hours As needed ≤ 24 hours **P4, Low** Docs, feature requests, minor questions ≤ 1 business day N/A Best effort These aren't aspirational targets; they're the baseline that enterprise SLA matrices define for production infrastructure. The difference with B2B data APIs is that most vendors don't even publish severity tiers, let alone commit to them contractually. #### ⚠️ Three Non-Negotiables Beyond the Table - **24/7 P1 coverage, not business-hours-only.** [Agent pipelines](https://www.explorium.ai/building-ai-agents/the-lifecycle-of-data-in-agent-development/) run at midnight, on weekends, and during holidays. If P1 support is limited to 9–5 ET, your SLA has a 16-hour blind spot every day. - **Escalation paths with named contacts.** Ticket queues with auto-responders don't cut it for production-blocking issues. Demand a named technical escalation contact for P1 and P2 incidents, someone your engineering team can reach directly. - **Proactive status page notifications.** Your team shouldn't discover outages from failed agent runs. Any incident above P3 should trigger automatic notifications through a public or customer-accessible status page. #### ✅ How Explorium Supports Production Builders We designed [Explorium's support infrastructure](https://www.explorium.ai/our-product/) for production agent builders, not casual platform users. That means direct email support at support@explorium.ai, dedicated Slack channels for enterprise customers, and [API-first documentation](https://www.explorium.ai/news-and-updates/explorium-data-bundle-api-release/) built for developers who integrate programmatically. When you submit a support request, we ask for the specific API endpoint, request payload, and steps to replicate, because the engineers on the other end are solving the actual technical problem, not triaging through a generalist support queue. > "An amazing account management team that provides guidance and assistance with all aspects of the product." — Mirit H., Mid-Market [***Explorium G2 - Verified Review***](https://www.g2.com/products/explorium/reviews/explorium-review-4522137) > "Great customer support, the team really cares about our experience and has added features we requested in the past." — Kobi M., Business Operations Manager [***Explorium G2 - Verified Review***](https://www.g2.com/products/explorium/reviews/explorium-review-4509480) > "Their CS team is very helpful and responsive in their feedback and they are more than happy to walk through the platform." — Ishi N., Enterprise [***Explorium G2 - Verified Review***](https://www.g2.com/products/explorium/reviews/explorium-review-4553554) #### 💡 The Bottom Line on Support SLAs The support tier table is only as good as the enforcement mechanism behind it. Tie P1 and P2 response time breaches to automatic service credits, and make sure "response" means a qualified engineer acknowledging the issue, not an automated email confirming receipt. ## Q10. The B2B Data API SLA Readiness Scorecard: Rate Any Vendor in 5 Minutes Use this scorecard to evaluate any B2B data API vendor's SLA readiness in under 5 minutes. Score 0 (not offered), 1 (partially meets), or 2 (fully meets) on each criterion, then check your total against the readiness tiers below. ### 📋 The 10-Point Vendor SLA Scorecard # SLA Criterion 0, Not Offered 1, Partial 2, Fully Meets 1 Uptime ≥ 99.9% measured 24/7 with limited exclusions No uptime SLA published Uptime claimed but broad exclusions ≥ 99.9% with capped maintenance windows 2 Latency SLA with P95 percentile target documented No latency data "Fast" claimed, no percentiles P50 and P95 published with benchmarks 3 QPM ≥ 200 sustained with burst allowance Undocumented rate limits Rate limits published, no burst QPM + burst documented with headers 4 Automatic service credits, no claim required No credits offered Credits offered, claim-based Auto-applied on breach detection 5 Data freshness cadences documented per category No freshness data "Regularly updated" claimed Daily/weekly/monthly per data type 6 Match rate / field-fill rate guarantees in writing No accuracy commitments Accuracy "benchmarked" informally Contractual match-rate minimums 7 Schema stability with versioned deprecation No versioning policy Versioning exists, no timeline 90-day deprecation notice guaranteed 8 SLA exclusions clearly defined and limited Broad, undefined exclusions Exclusions listed but expansive Maintenance ≤ 30 min, limited carve-outs 9 24/7 P1 support with ≤ 15 min response Email-only, business hours 24/7 available, no response SLA ≤ 15 min P1 with named escalation 10 Independent monitoring rights Vendor-only measurement Status page available Third-party monitoring contractually permitted ### ⭐ Score Interpretation Score Range Assessment Recommended Action 16–20 ✅ Production-ready Negotiate optimization terms and custom SLAs 10–15 ⚠️ Acceptable foundation Push hard on gaps before signing; your agents have blind spots 5–9 ❌ Significant SLA exposure Your agents are unprotected in critical areas; address before production 0–4 ❌ Not production-grade Evaluate alternative vendors immediately #### ✅ Where Explorium Scores on This Scorecard We designed [Explorium's API infrastructure](https://www.explorium.ai/our-product/) to score 18–20 on this scorecard by default, because the platform was built for [production AI agent workloads](https://www.explorium.ai/use-case/gtm-engineering/) from the ground up, not retrofitted from a human-first prospecting tool. Multi-source aggregation across [50+ providers](https://www.explorium.ai/data/) delivers structural redundancy that single-source vendors cannot match. [Credit-based pricing](https://www.explorium.ai/credit-details/) means you pay per enrichment, not per month, so failed calls don't consume credits. API-first documentation, Slack-based support channels, and published [accuracy benchmarks](https://www.explorium.ai/data-enrichment/introduction-to-data-enrichment/) (97.8% on employee count, 97.8% on website URL, 97.31% on NAICS code) back every claim with verifiable data. #### 💡 How to Use This Scorecard Download this scorecard, evaluate your current vendor and two alternatives side-by-side, and bring the results to your next vendor review meeting. The numbers make the decision for you. [Create a free Explorium account](https://www.explorium.ai/sign-up/) and validate these SLA benchmarks against your own production requirements before committing to any vendor.