Editing Synapolis/Agent Substrate API Catalog (section)

== Сравнительная таблица ==

Сокращения: ''ZDR'' — zero data retention; ''BYOK'' — bring your own key; ''MTok'' — 1 млн токенов; ''официально'' означает, что строка опирается на публичные docs/pricing/policy provider-а, но не заменяет contract review.

{| class="wikitable sortable"
! API/provider
! Типы возможностей
! Сильные стороны
! Слабые стороны
! Цена/стоимость ориентир и единица
! Качество / latency / context / tool-use / code
! Policy / copyright / content limits
! Data retention / training / privacy controls
! Secret leakage risk
! Lock-in / availability / geography
! Recommended use in Synapolis agents
! Not recommended for
! Integration notes
! Source links / date checked
|-
| OpenAI
| LLM/chat/responses, reasoning, tool-use, code, embeddings, speech/vision/image, batch, regional processing.
| Сильная универсальность, mature API/SDK, хорошее tool/function calling, embeddings дешёвые, enterprise privacy/ZDR options.
| Premium frontier costs; policy enforcement; модельный роутинг и названия быстро меняются; некоторые endpoints/features могут иметь особые retention limits.
| От ~$0.05/MTok input на nano/cheap tiers до premium frontier; embeddings text-embedding-3-small около $0.02/MTok, 3-large около $0.13/MTok; batch обычно дешевле. Проверять live pricing.
| Высокое качество для agents/code/reasoning; latency зависит от model/service tier; context у новых семейств large.
| Строгие universal usage policies; ownership terms assign output to user where law allows; copyright/IP risk всё равно остаётся у интегратора по downstream use.
| API data не используется для training без opt-in; default retention до 30 дней для abuse/service, ZDR для qualifying org/endpoints; regional processing с uplift для eligible models.
| Средний: прямой API лучше BYOK-агрегатора, но secrets всё равно нельзя слать; включать redaction и project-level keys.
| US provider; Azure/OpenAI/AWS Bedrock/regional options уменьшают, но не убирают lock-in.
| Default high-quality substrate; code review, synthesis, structured tool-use, embeddings baseline.
| Неподготовленные secret-bearing prompts; use cases без human oversight в regulated/high-stakes областях.
| Использовать Responses API/tool schemas, batch for background, cache, service tiers; отдельные org/project keys.
| [https://developers.openai.com/api/docs/pricing pricing], [https://developers.openai.com/api/docs/guides/your-data data controls], [https://openai.com/policies/usage-policies/ usage policies], [https://openai.com/policies/row-terms-of-use/ terms] — checked 2026-08-07.
|-
| Anthropic Claude
| LLM/chat/messages, extended thinking, tool-use, computer/code workflows, managed agents/Claude Code surfaces.
| Очень сильный coding/repo reasoning, long-form analysis, cautious tool-use, enterprise posture.
| Дороже low-cost конкурентов; policy stricter; model availability/rate limits can bottleneck; consumer Claude Code privacy differs from API/enterprise.
| Claude pricing page: Sonnet 5 introductory ~$2/$10 per MTok input/output through 2026-08-31 then ~$3/$15; Haiku lower, Opus/Fable higher. Проверять live.
| High quality for code agents and analysis; context/model family varies; latency often higher than small models.
| Strong AUP/safety; явные disallowed/high-risk use constraints; copyright litigation/news risk exists, but API output/IP terms need contract review.
| API commercial products: not used for training by default; standard deletion within 30 days except files/violations/law/agreements; ZDR available for eligible API/enterprise.
| Средний-низкий при API+ZDR; высокий для consumer/free/chat or prompts containing repo secrets.
| US provider, strong AWS/GCP/Bedrock routes; potential lock-in through Claude Code workflows.
| Primary substrate for serious coding agents, careful writing/analysis, high-risk tool plans with review.
| Cheap background classification; low-policy use; secret-bearing workflows without enterprise/ZDR.
| Prefer direct API/Bedrock/Vertex where contracts fit; use prompt caching/batch; tool boundaries explicit.
| [https://platform.claude.com/docs/en/about-claude/pricing pricing], [https://platform.claude.com/docs/en/about-claude/models/overview models], [https://platform.claude.com/docs/en/manage-claude/api-and-data-retention retention], [https://privacy.claude.com/en/articles/7996868-is-my-data-used-for-model-training training], [https://www.anthropic.com/news/usage-policy-update usage policy] — checked 2026-08-07.
|-
| Google Gemini / Vertex AI / Agent Platform
| Gemini API, Vertex AI, multimodal text/image/video/audio, grounding with Google Search, agents, embeddings, speech/vision via Google Cloud.
| Strong multimodal, large context, cheap Flash tiers, Google Cloud IAM/VPC/region/data tooling, integrated Search grounding.
| Policy/terms differ between free AI Studio, paid Gemini API, Vertex/Agent Platform; store=true defaults and logging settings need care.
| Gemini API snippets show Flash-like paid tiers around $0.15/$1.25 MTok and higher Pro tiers; Vertex/Agent Platform Gemini 3.x Pro/Flash pricing can be $1.5-$4 input and $9-$18 output depending model/context/region. Search grounding after free quota about $14/1K requests.
| Very good latency/cost on Flash; Pro stronger for analysis; excellent multimodal; tool-use acceptable but integration choices matter.
| Google generative AI prohibited use policy; stricter around illegal/harmful, privacy, deception, explicit sexual content depending product.
| Paid Gemini API: no training on prompts/responses per ZDR docs; ZDR requires avoiding store/session/logging features; Agent Platform request-response logging disabled by default but Interactions API may default store=true unless set false.
| Средний: Google Cloud controls can be strong, but accidental storage/logging is easy.
| Strong lock-in if using Vertex/Agent Platform/Search grounding; good regional/cloud availability.
| Cheap massive background agents, multimodal ingestion, grounded search, GCP-native secure deployments.
| Low-policy needs; workflows that cannot tolerate Google account/project policy changes.
| Explicitly set store=false where applicable; separate AI Studio/free from paid API; use IAM/service accounts.
| [https://ai.google.dev/gemini-api/docs/pricing Gemini API pricing], [https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing Agent Platform pricing], [https://ai.google.dev/gemini-api/docs/zdr Gemini ZDR], [https://docs.cloud.google.com/gemini-enterprise-agent-platform/resources/zero-data-retention Agent Platform ZDR], [https://policies.google.com/terms/generative-ai/use-policy policy] — checked 2026-08-07.
|-
| xAI Grok
| LLM/chat/code, reasoning, tool-use, image/video/voice APIs.
| Grok 4.5 marketed for code, non-hallucination and agentic tool calling; comparatively lenient brand positioning; OpenAI-compatible style.
| Smaller enterprise/privacy track record than OpenAI/Google/Anthropic; policy and terms changed recently; limited independent operational history.
| Official pricing: grok-4.5 short context about $2 input / $6 output MTok, long context $4/$12; grok-build lower; media priced per image/sec.
| Potentially strong code/agentic tasks; 500K+ context on flagship; latency depends on capacity.
| AUP says maximize user control while requiring legal/responsible/safe use; not "uncensored" for illegal/harmful use.
| Privacy policy applies; enterprise terms for API/business; verify API retention/training in contract/docs before secrets.
| Средний-высокий until org-specific privacy terms are confirmed.
| US provider; model catalog narrower; availability/capacity may fluctuate.
| Experimental coding agents, writing/analysis alternative, low-friction public-data tasks.
| Secret-sensitive or regulated workflows without enterprise terms; sole provider dependency.
| Use as routed option behind gateway with fallback; log model/version and policy date.
| [https://docs.x.ai/developers/pricing pricing], [https://docs.x.ai/developers/models models], [https://x.ai/legal/acceptable-use-policy AUP], [https://x.ai/legal/terms-of-service-enterprise enterprise terms], [https://x.ai/legal/privacy-policy privacy] — checked 2026-08-07.
|-
| Mistral AI
| LLM/chat, agents Studio/Vibe, code Vibe, embeddings, OCR, fine-tuning, EU enterprise.
| EU provider, open/proprietary mix, good price-performance, privacy controls, self-hostable/open-weight ecosystem.
| Public pricing page may require docs/console for detailed model rows; ZDR only on Scale/stateless API; some products stateful.
| API starts low; exact model prices vary. Official docs/pricing should be checked per model; third-party summaries place cheap tiers around $0.10+/MTok and frontier higher.
| Good multilingual/code/general; latency good on smaller models; tool-use maturing.
| Mistral usage/legal docs; generally less restrictive than Anthropic/OpenAI but not law-free.
| API privacy controls for training/retention; ZDR available only Scale and stateless calls, not Vibe/agents/conversations/batch/stateful products.
| Средний-низкий for Scale ZDR stateless; средний for normal API; high for stateful agent products with secrets.
| EU geography advantage; lock-in moderate if using Studio/Vibe agents.
| EU-friendly general agents, cheaper writing, open-weight/local transition path.
| High-security unless ZDR/contract in place; tasks requiring best-in-class coding certainty.
| Prefer stateless API for private use; document if using Labs/free tiers.
| [https://mistral.ai/pricing/ pricing], [https://docs.mistral.ai/models/overview models], [https://docs.mistral.ai/admin/monitor-comply/privacy-data-controls privacy controls], [https://help.mistral.ai/en/articles/347612-can-i-activate-zero-data-retention-zdr ZDR], [https://legal.mistral.ai/terms terms] — checked 2026-08-07.
|-
| Cohere
| Command LLMs, enterprise RAG, Embed, Rerank, Model Vault/dedicated deployments.
| Strong RAG/rerank/multilingual enterprise story; dedicated Model Vault for secure predictable inference.
| Not usually first pick for frontier coding; pricing page mixes API and dedicated infra; generation catalog less buzz than frontier labs.
| Rerank 4 Pro via OpenRouter listed ~$0.0025/search; dedicated Model Vault examples $4-$10/hour instances; older Embed/Rerank per-token varies by platform.
| Excellent retrieval/reranking; Command useful for enterprise RAG; code weaker than Claude/OpenAI top tiers.
| Enterprise acceptable-use terms; not a low-policy provider.
| Enterprise privacy/security controls; verify per API/platform and cloud marketplace route.
| Low-medium for dedicated/vault; medium for API; never send raw secrets into RAG rerank unless approved.
| Canada/enterprise/cloud marketplace availability; some lock-in in RAG pipeline tuning.
| RAG rerank, multilingual embeddings, private enterprise retrieval substrate.
| Primary code agent model; cheap creative chat.
| Use Cohere Rerank after cheap vector retrieval; cap docs/query and log token/search cost.
| [https://cohere.com/pricing pricing], [https://docs.cohere.com/docs/how-does-cohere-pricing-work pricing docs], [https://docs.cohere.com/docs/models models], [https://openrouter.ai/cohere/rerank-4-pro rerank price reference] — checked 2026-08-07.
|-
| DeepSeek
| LLM/chat/reasoning/code, OpenAI-compatible endpoint, long context, low-cost models.
| Extremely strong price-performance for coding/reasoning; cheap background agents; large context on newer models.
| Chinese jurisdiction/censorship/security concerns; pricing page warns of future increases; availability/payment/region risk.
| Official docs: V4 Flash about $0.14 input cache miss / $0.28 output MTok; V4 Pro about $0.435/$0.87 MTok with very cheap cache-hit input; price increase warning.
| Good code/reasoning for cost; latency/capacity can vary; tool-use depends wrapper.
| Content/policy/censorship profile differs from US/EU providers; avoid political/legal-sensitive assumptions.
| Privacy policy exists; do not assume Western enterprise ZDR. Treat as non-secret public-data substrate unless contract says otherwise.
| Высокий for secrets/IP-sensitive prompts; acceptable for public/low-risk tasks after redaction.
| China-linked; sanctions/export/procurement/geography risk; strong lock-in if prompts tuned to DeepSeek quirks.
| Cheap background, code draft, long-context public analysis, fallback in router.
| Secrets, customer data, regulated workloads, geopolitical/policy-sensitive analysis without cross-check.
| Use via LiteLLM/OpenAI-compatible route with strict redaction; verify model IDs and peak pricing.
| [https://api-docs.deepseek.com/quick_start/pricing/ pricing], [https://cdn.deepseek.com/policies/en-US/deepseek-privacy-policy.html privacy] — checked 2026-08-07.
|-
| Qwen / Alibaba Cloud Model Studio
| Qwen text/code/multimodal/audio/video via Alibaba Cloud Model Studio; open-weight Qwen locally or via providers.
| Strong open-weight ecosystem, cheap and capable multilingual/code models, cloud + self-host path.
| International availability/region/payment and policy/censorship concerns; docs/pricing by region; China-linked procurement risk.
| Official Model Studio pay-as-you-go by model; third-party summaries cite Qwen Flash around $0.10/$0.40 MTok, Plus around $0.40/$2.40, 397B higher. Verify live Alibaba Cloud region.
| Very good price-performance; Qwen Coder strong; latency depends host.
| Alibaba Cloud policies; Chinese jurisdiction/censorship risk; not for illegal/harmful use.
| Verify Alibaba Cloud data handling per account/region; open weights self-host avoid API retention but shift ops risk to Synapolis.
| High for direct cloud with secrets unless contract; low if self-hosted and isolated.
| China/Singapore/international region constraints; open-weight reduces lock-in.
| Cheap code/background via open weights; self-host candidates for private tasks.
| Sensitive Western customer data/direct secrets on Alibaba-hosted API.
| Prefer self-host/open-weight when privacy matters; use cloud for public/low-risk throughput.
| [https://www.alibabacloud.com/help/en/model-studio/models models], [https://www.alibabacloud.com/help/en/model-studio/model-pricing pricing], [https://modelstudio.alibabacloud.com/ Model Studio] — checked 2026-08-07.
|-
| Moonshot / Kimi
| Kimi LLM/code/long-context, Kimi K-series; API and open-weight/aggregator routes.
| Strong code/agentic benchmarks and long-context claims; open-weight Kimi variants broaden hosting options.
| Direct official pricing/resources can be less enterprise-transparent; China-linked risk; top models may be expensive.
| Kimi resources page: K3 about $3 input cache miss / $0.30 cache hit / $15 output MTok; K2.6 about $0.95 miss / $0.16 hit / $4 output; OpenRouter K3 about $2.50/$14.
| Strong coding and long-horizon agentic workflows if model claims hold; latency depends host/provider.
| Treat as low-policy only in the sense of less Western SaaS friction; still legal/policy and censorship risks.
| Do not assume ZDR on direct API/aggregators; self-hosted open weights give better control.
| High for secrets unless self-hosted and audited.
| China-linked and aggregator-dependent availability; open weights reduce vendor lock-in.
| Coding experiments, public repo agents, cost/performance benchmark lane.
| Secret-sensitive, regulated, political/censorship-critical outputs without independent verification.
| Prefer OpenRouter/Together/Fireworks only for non-secret eval; pin provider route for reproducibility.
| [https://www.moonshot.ai/ Moonshot], [https://www.kimi.com/resources/kimi-k3-pricing Kimi K3 pricing], [https://www.kimi.com/resources/kimi-k2-6-pricing Kimi K2.6 pricing], [https://openrouter.ai/moonshotai/kimi-k3 OpenRouter K3] — checked 2026-08-07.
|-
| Together AI
| Serverless inference, dedicated endpoints, GPU clusters, fine-tuning, embeddings/rerank/moderation, open models.
| Broad open-model catalog, serverless-to-dedicated path, privacy settings/ZDR, good for routing experiments.
| Cost varies by model; passthrough third-party model privacy can differ; GPU/dedicated economics require capacity planning.
| Serverless per MTok by model; public summaries show ~$0.03-$7+/MTok; GPU clusters/dedicated by GPU-hour; check live pricing.
| Good latency/throughput; quality follows selected model; supports many code/open models.
| Provider policy plus third-party/open-model licenses; not a single uniform content policy.
| Privacy settings can disable storage/training and enable ZDR; docs note third-party model providers/passthrough need attention.
| Medium; lower with ZDR and direct provider route, higher with passthrough/unknown models.
| US provider; open catalog reduces model lock-in, platform lock-in remains.
| Open-model benchmark lane, cheap background, dedicated endpoints for stable workloads.
| Secrets unless ZDR/contract and no passthrough ambiguity.
| Use model allowlists, privacy settings, spend caps; record exact model/provider.
| [https://www.together.ai/pricing pricing], [https://docs.together.ai/docs/privacy-and-security privacy/security], [https://www.together.ai/terms-of-service terms/ZDR], [https://www.together.ai/privacy privacy] — checked 2026-08-07.
|-
| Fireworks AI
| Serverless/on-demand/reserved inference, open models, embeddings, fine-tuning, OpenAI/Anthropic-compatible APIs.
| High-throughput open-model serving, ZDR posture for open models, good enterprise options, batch 50% pricing.
| Exact prices per model/tier; prepaid billing; hosted model quality follows open model.
| Official: serverless per MTok; embeddings as low as $0.008-$0.1/MTok by model/size; batch 50% of serverless; H100/on-demand pricing separate.
| Good latency/throughput for open models; code quality depends Qwen/Kimi/DeepSeek/Llama selected.
| Open-model licenses and Fireworks terms; not an illegal/uncensored bypass.
| Privacy policy says no AI training without opt-in; docs describe ZDR for most services, Response API has retention caveat.
| Medium-low for open-model ZDR; medium if response/conversation state is used.
| US provider; platform lock-in moderate; self-host transition possible by using open weights.
| Cheap public-data agents, code model hosting, batch inference, fine-tuned open models.
| Highest security unless contract; workflows needing proprietary frontier quality.
| Use standard/priority/fast tier explicitly; batch background jobs; avoid stateful retention for secrets.
| [https://fireworks.ai/pricing pricing], [https://docs.fireworks.ai/serverless/pricing serverless pricing], [https://docs.fireworks.ai/guides/security_compliance/data_handling ZDR/data handling], [https://fireworks.ai/privacy-policy privacy] — checked 2026-08-07.
|-
| GroqCloud
| Fast inference for selected open models, Whisper/STT, TTS, tool-use if model supports.
| Very low latency/tokens-per-second, cheap small/medium open models, self-serve ZDR controls.
| Narrower model catalog; not always best quality; rate limits/capacity can shape production behavior.
| Official docs: Llama 3.1 8B ~$0.05/$0.08 MTok; Llama 3.3 70B ~$0.59/$0.79; GPT-OSS 120B ~$0.15/$0.60; Whisper by audio hour.
| Excellent latency; quality follows model; context often 131K on listed models.
| Acceptable-use/responsible AI policies; open-model licenses apply.
| Docs: usage metadata collected; customer data not retained by default except opt-in/persistence/reliability; ZDR self-serve.
| Medium-low with ZDR; still third-party cloud, no raw secrets.
| US provider, specialized hardware; model choice/capacity lock-in.
| Fast cheap background agents, streaming UX, STT transcription, low-latency classifiers.
| Hard reasoning/code requiring frontier accuracy; secret workflows without isolation.
| Use as latency tier behind router; fallback on quality-sensitive tasks.
| [https://groq.com/pricing pricing], [https://console.groq.com/docs/models models], [https://console.groq.com/docs/your-data data], [https://console.groq.com/docs/legal legal] — checked 2026-08-07.
|-
| Cerebras
| Ultra-fast inference for selected open/frontier-ish models; developer and paid tiers.
| Very high speed claims; useful for latency-sensitive agents and batch acceleration.
| Smaller catalog; detailed per-model token rates may require console; enterprise maturity narrower than hyperscalers.
| Official pricing page: free trial $5 credits; developer self-serve starts around $10; public docs emphasize flexible pricing. Historical public rates were ~$0.10/MTok 8B and ~$0.60/MTok 70B, verify live.
| Very low latency/high throughput; quality follows model selection.
| Standard AUP/contracts; open-model licenses.
| Verify retention/training terms in contract/trust center before secrets.
| Medium until ZDR/contract confirmed.
| US provider, specialized infra; availability/catalog lock-in.
| Latency-sensitive public-data agents, fast eval loops.
| Secret/high-compliance use without terms; broad multimodal tasks.
| Treat as speed lane in router; benchmark live latency with Synapolis prompts.
| [https://www.cerebras.ai/pricing pricing], [https://www.cerebras.ai/ press/site], [https://www.cerebras.ai/press-release/cerebras-launches-the-worlds-fastest-ai-inference historical pricing context] — checked 2026-08-07.
|-
| Perplexity Sonar
| Search-grounded chat/completions, citations, answer engine, OpenAI-compatible API.
| Useful search/browser substrate with citations and low integration cost.
| More expensive than raw search; citation quality must be verified; not a browser automation replacement.
| Official docs: Gateway billed per token at model rates; Sonar pay-as-you-go no subscription. Third-party summaries cite base ~$1/$1 MTok plus request/search fees; verify live model card.
| Good for current factual answers; latency includes search; not best for code edits.
| Must respect source copyright/ToS; not a license to copy pages.
| Verify API data terms; search queries may reveal intent/secrets.
| Medium-high: search prompts often contain sensitive business questions.
| US provider; source index/model routing dependency.
| Current research with citations, pre-RAG discovery, fact-check lane.
| Secret/internal search queries; legal/medical/financial final answers without human/source review.
| Always store cited URLs/date; use as input to human/agent verification, not final truth.
| [https://docs.perplexity.ai/docs/getting-started/pricing pricing], [https://docs.perplexity.ai/docs/sonar/quickstart Sonar quickstart] — checked 2026-08-07.
|-
| Tavily
| Web search, extract, crawl/research endpoints for AI agents/RAG.
| Agent-oriented search/extraction API; simple credit pricing; good for web research pipelines.
| Credits can burn quickly on /research; source quality/copyright/prompt injection risks remain.
| Free 1,000 credits/month; PAYG ~$0.008/credit; monthly ~$0.0075-$0.005/credit; basic/advanced/research consume different credits.
| Latency/search quality good for agent use; not an LLM by itself.
| Web content license/copyright responsibility remains with integrator.
| Search queries/extracted pages sent to Tavily; verify privacy terms for sensitive topics.
| Medium: queries can leak plans; never include credentials.
| SaaS provider; index/extraction behavior lock-in moderate.
| Search/RAG substrate, source discovery, monitoring.
| Secret-bearing browsing; executing page instructions.
| Sanitize queries; isolate browser/search tool from credentialed tools; cite sources.
| [https://www.tavily.com/pricing pricing], [https://docs.tavily.com/documentation/api-credits credits], [https://tavily.com/ product] — checked 2026-08-07.
|-
| Brave Search API
| Independent web/news/images/LLM context/search API.
| Own index, predictable pricing, citations/LLM context, privacy-friendly brand.
| Not Google SERP; niche/technical coverage may differ; lower QPS on small plans.
| Official: Search $5 per 1,000 requests, includes $5 monthly credits; enterprise custom.
| Good raw search/grounding; latency and completeness vary by query.
| Search result snippets/URLs only; downstream copyright/ToS still apply.
| Queries go to Brave; privacy better than many search APIs but not secret-safe.
| Medium-low for non-secret queries; medium for strategy queries.
| Vendor/index lock-in; global web availability.
| Default cheap independent search for agents and RAG.
| Need exact Google SERP/ads/local fidelity; secrets in search terms.
| Use LLM Context endpoint where token-efficient; keep citation URLs.
| [https://brave.com/search/api/ pricing/API], [https://brave.com/learn/best-search-api-2026/ pricing explanation] — checked 2026-08-07.
|-
| SerpAPI
| Google/Bing/Yahoo/Maps/Shopping/etc SERP JSON.
| High SERP fidelity, mature JSON schema, legal shield on paid tiers.
| More expensive than Brave/SearchAPI; not AI-specific answer engine.
| Starter $25/month for 1,000 searches; Developer $75/5,000; Production $150/15,000; Big Data $275/30,000.
| Good SERP extraction; latency tied to live search.
| Public search collection/legal shield does not license downstream content use.
| Queries expose intent; account logs/retention need vendor review.
| Medium.
| US provider; Google SERP dependency.
| SEO/Google-specific search tasks, monitoring exact SERP features.
| Cheap high-volume generic search; secret queries.
| Use only when exact SERP matters; otherwise Brave/Tavily.
| [https://serpapi.com/pricing pricing], [https://serpapi.com/ product/legal shield] — checked 2026-08-07.
|-
| SearchAPI.io
| Google SERP and related search APIs.
| Cheaper Google SERP alternative with SLA/legal protection on higher plans.
| Smaller ecosystem than SerpAPI; plan semantics and enhanced speed need modeling.
| Developer $40/month, about $4/1K; Production $100/month, about $3/1K; BigData $250/month, about $2.5/1K; Octo 1M $1.5/1K.
| Good for SERP/RAG discovery; verify fields for required verticals.
| Same SERP/copyright downstream caveats.
| Queries can leak sensitive plans.
| Medium.
| Vendor + Google SERP dependency.
| Cost-sensitive Google SERP substrate.
| Non-Google broad web answer generation; secrets.
| Benchmark against SerpAPI for needed SERP features before switching.
| [https://www.searchapi.io/pricing pricing], [https://www.searchapi.io/ product] — checked 2026-08-07.
|-
| Voyage AI
| Embeddings, multimodal embeddings, rerankers, Batch API.
| Excellent retrieval-specialist provider; cheap rerank token pricing and free initial rerank allowance.
| Narrow scope; acquired/operated with MongoDB ecosystem; not a chat model.
| Rerank-2.5 about $0.05/MTok, lite $0.02/MTok, first 200M rerank tokens free; older embeddings: voyage-3.5 $0.06/MTok, lite $0.02/MTok.
| High RAG quality; latency good; context varies by model.
| Retrieval content copyright remains integrator responsibility.
| Check docs/contract; embeddings encode sensitive content, so treat vectors as derived sensitive data.
| Medium: documents/queries sent to third party and vectors may leak semantics.
| Vendor lock-in via vector dimensions/model behavior; manageable with abstraction.
| RAG indexing/rerank for Synapolis docs, public corpora, non-secret knowledge.
| Raw secrets, private keys, personal data without approval.
| Store model/version/dimension; separate indexes by sensitivity; rotate if changing model.
| [https://docs.voyageai.com/docs/pricing pricing], [https://www.mongodb.com/docs/voyageai/models/ models] — checked 2026-08-07.
|-
| Jina AI
| Embeddings, rerankers, Reader, DeepSearch, multimodal search foundation models.
| Strong multilingual/multimodal retrieval, Reader for web-to-markdown/JSON, open research/models.
| Pricing details may be dashboard/package-based; less enterprise standard than hyperscalers.
| Reranker/Embedding APIs begin with 10M free tokens per new API key; packages beyond. Reader/token pricing varies; verify account pricing.
| Good retrieval quality, especially multilingual/multimodal; not primary chat LLM.
| Web reader/crawler must respect website ToS/copyright.
| API docs need review for retention; embeddings/reader inputs are sensitive derived data.
| Medium.
| Germany/EU-facing but global SaaS; model/vector lock-in.
| RAG, reader/extraction, multilingual retrieval.
| Secrets in docs/pages; high-compliance without contract.
| Use Reader in isolated pipeline; don't execute extracted instructions.
| [https://jina.ai/ product], [https://jina.ai/reranker/ reranker], [https://jina.ai/en-US/embeddings/ embeddings] — checked 2026-08-07.
|-
| OpenRouter
| Aggregated LLM gateway, 400+ models, routing/fallbacks, BYOK/paygo, ZDR filters.
| Fast model discovery, unified API, fallback/routing, privacy controls can restrict ZDR providers.
| Adds extra trust boundary; provider logging varies; model/provider route can change unless pinned; BYOK concentrates keys in gateway.
| Pay-as-you-go per model, claims transparent pricing and many free models; check each model card.
| Quality/latency depends provider route; routing can improve availability but reduce reproducibility.
| Policies differ by underlying provider; OpenRouter is not a policy bypass.
| Docs: prompt retention opt-in; provider logging differs; ZDR can be enforced globally/per model group/guardrail/request.
| High if using BYOK with secrets or prompt logging; medium with ZDR/non-secret prompts.
| Strong platform lock-in for routing/account credits; reduces model lock-in.
| Non-secret evaluation, fallback router, public-data price benchmarking.
| High-security/secret-sensitive direct workflows; regulated data unless contract and ZDR routing are audited.
| Pin model+provider when reproducibility matters; enable ZDR; disable prompt logging; never route secrets.
| [https://openrouter.ai/models models], [https://openrouter.ai/pricing pricing], [https://openrouter.ai/docs/quickstart quickstart], [https://openrouter.ai/docs/guides/privacy/data-collection data], [https://openrouter.ai/docs/guides/privacy/provider-logging provider logging], [https://openrouter.ai/docs/guides/features/zdr ZDR] — checked 2026-08-07.
|-
| LiteLLM Proxy
| Self-hosted LLM gateway/router, virtual keys, spend tracking, budgets, provider abstraction.
| Good Synapolis control plane candidate: central keys, budgets, model allowlists, audit, OpenAI-compatible interface.
| Security-critical service; past/current vulnerabilities possible; misconfig can expose master keys or allow over-broad routing.
| OSS/self-host infra cost; provider token costs pass through; spend tracking uses model cost map.
| Latency overhead small but depends routing; quality depends model; supports many providers.
| Underlying provider policies apply; LiteLLM itself does not make unsafe use safe.
| Privacy depends deployment DB/logging/proxy config and underlying providers.
| High if proxy breached; low-medium if self-hosted locked down with per-agent virtual keys and no prompt logging.
| Reduces provider lock-in; creates internal gateway lock-in/config dependency.
| Synapolis internal model gateway, cost control, per-agent budgets, provider failover.
| Running exposed to public internet without auth, patching, DB hardening, secret management.
| Put behind private network; rotate master key; virtual keys per agent; redact logs; update pricing map.
| [https://docs.litellm.ai/docs/proxy/virtual_keys virtual keys], [https://docs.litellm.ai/docs/proxy/cost_tracking spend tracking], [https://docs.litellm.ai/docs/proxy/users budgets] — checked 2026-08-07.
|-
| Ollama
| Local/cloud open-model runner, embeddings, tool-capable models, offline mode.
| Simple local deployment, offline possible, no per-token API cost, data stays local if actually local.
| Quality/latency constrained by local hardware; operational burden; cloud features differ from local privacy.
| Software free; cost is hardware/electricity/ops. Cloud models/regions may have separate pricing.
| Good for small/medium models and private preprocessing; frontier quality usually lower unless large hardware.
| Open model licenses vary; self-hosting removes SaaS policy but not law/copyright obligations.
| Local/offline: no third-party retention; cloud: check Ollama data handling.
| Low if isolated local; medium if exposed API or cloud.
| Minimal vendor lock-in; model format/library ecosystem lock-in low.
| High-security local summarization/classification, embeddings, private repo pre-pass.
| Frontier coding decisions alone; exposed unauthenticated local API.
| Bind to localhost/private network; model allowlist; monitor VRAM/RAM; do not mix browser tools with secrets.
| [https://ollama.com/ product/data], [https://ollama.com/library model library], [https://github.com/ollama/ollama GitHub] — checked 2026-08-07.
|-
| vLLM
| Self-hosted high-throughput LLM serving, OpenAI-compatible server, batching, prefix caching, quantization/distributed.
| Production-grade serving engine for open weights; good throughput; keeps data in Synapolis-controlled infra.
| Requires GPU ops, model security, patching, observability; not a model by itself.
| OSS; cost is GPU/hour + storage + ops. RunPod/Lambda/CoreWeave/local GPUs determine spend.
| Excellent throughput/latency when tuned; quality depends selected model.
| Open model licenses and local policy enforcement are Synapolis responsibility.
| No third-party API retention if fully self-hosted; logs and traces become local sensitive artifacts.
| Low if isolated/hardened; high if internet-exposed or logs leak prompts.
| Low vendor lock-in at model/API layer; hardware/cloud lock-in possible.
| Secret-sensitive private inference, high-volume background tasks, self-hosted code/RAG models.
| Tiny VPS without GPU; tasks needing proprietary frontier accuracy.
| Use OpenAI-compatible endpoint behind internal gateway; disable prompt logs or protect them; patch promptly.
| [https://vllm.ai/ vLLM], [https://github.com/vllm-project/vllm GitHub], [https://nm-vllm.readthedocs.io/ docs mirror] — checked 2026-08-07.
|-
| Replicate
| Hosted public/private ML models, image/video/audio/LLM predictions, webhooks.
| Broad model marketplace, quick prototypes, official models with stable API.
| Per-second compute can be costly; public model supply-chain and license quality varies.
| Pay-as-you-go compute time; model pages show estimate; public models billed by run time/hardware or model-specific units.
| Great for media/prototypes; LLM quality depends hosted model and cold starts.
| Model licenses/copyright vary; user responsible for generated media/code use.
| Check retention/privacy for predictions/files; not secret-safe by default.
| Medium-high for uploaded media/private data.
| Platform/model marketplace lock-in; export possible for open models.
| Vision/media experiments, non-secret model prototypes.
| Secret docs, production LLM substrate at scale without cost model.
| Prefer official models for stable API; set webhooks carefully; avoid private keys in inputs.
| [https://replicate.com/pricing pricing], [https://replicate.com/docs/topics/billing billing], [https://replicate.com/docs/topics/models/official-models official models] — checked 2026-08-07.
|-
| RunPod
| GPU pods, serverless GPUs, clusters for self-hosted models/vLLM/Ollama.
| Flexible GPU cost, serverless scale-to-zero, good for self-hosting and batch jobs.
| Ops burden, cold starts, container security, spot/preemption risk, storage/network extra costs.
| Official pricing per GPU/hour or serverless per second; serverless bills from worker start until fully stopped; pricing changed June 2026 with Flex/Active workers.
| Quality depends served model; latency depends worker/cold start/GPU.
| Self-hosted model licenses and local policy enforcement.
| Data stays in chosen deployment/provider infra; still third-party GPU cloud, so encrypt and avoid raw secrets unless contract.
| Medium; lower with encrypted volumes/private network; high on community/spot for secrets.
| GPU provider lock-in moderate; Docker/vLLM reduces.
| Self-hosted open models, experiments, batch inference, private-ish code/RAG when contract acceptable.
| Highest-security secrets without dedicated secure cloud/contract.
| Harden images, no secrets baked into containers, restrict network, clean volumes.
| [https://www.runpod.io/pricing pricing], [https://docs.runpod.io/serverless/pricing serverless pricing], [https://www.runpod.io/blog/serverless-pricing-update pricing update] — checked 2026-08-07.
|-
| AWS Bedrock / Azure AI Foundry / cloud marketplaces
| Managed access to Anthropic, Meta, Mistral, Amazon, OpenAI and other models with IAM/private networking/regional controls.
| Enterprise procurement, IAM, VPC/PrivateLink, audit, compliance, data residency options.
| Higher complexity/cost; model availability by region; service-tier lock-in.
| Bedrock on-demand per token, batch 50% lower for select FMs, provisioned/reserved tiers; exact per-model rates region-dependent.
| Quality depends model; latency and quotas by region/tier.
| Underlying model + cloud provider policies both apply.
| Stronger enterprise controls possible; configure logs carefully.
| Low-medium with private networking/ZDR/contract; medium if default logging broad.
| Strong cloud lock-in; better geography controls.
| High-security enterprise route when Synapolis standardizes on cloud IAM/contracts.
| Casual cheap experiments if direct API cheaper and data non-sensitive.
| Use when compliance/procurement matters more than cheapest token.
| [https://aws.amazon.com/bedrock/pricing/ Bedrock pricing], [https://docs.aws.amazon.com/bedrock/latest/userguide/models.html model availability] — checked 2026-08-07.
|}